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When exposed to rhythmic patterns with temporal regularity, adults exhibit an inherent ability to extract and anticipate an underlying sequence of regularly spaced beats, which is internally constructed, as beats are experienced even when no events occur at beat positions (e.g., in the case of rests). Perception of rhythm and synchronization to periodicity is indispensable for development of cognitive functions, social interaction, and adaptive behavior. We evaluated neural oscillatory activity in premature newborns (n = 19, mean age, 32 ± 2.59 weeks gestational age) during exposure to an auditory rhythmic sequence, aiming to identify early traces of periodicity encoding and rhythm processing through entrainment of neural oscillations at this stage of neurodevelopment. The rhythmic sequence elicited a systematic modulation of alpha power, synchronized to expected beat locations coinciding with both tones and rests, and independent of whether the beat was preceded by tone or rest. In addition, the periodic alpha‐band fluctuations reached maximal power slightly before the corresponding beat onset times. Together, our results show neural encoding of periodicity in the premature brain involving neural oscillations in the alpha range that are much faster than the beat tempo, through alignment of alpha power to the beat tempo, consistent with observations in adults on predictive processing of temporal regularities in auditory rhythms. Research Highlights: In response to the presented rhythmic pattern, systematic modulations of alpha power showed that the premature brain extracted the temporal regularity of the underlying beat.In contrast to evoked potentials, which are greatly reduced when there is no sounds event, the modulation of alpha power occurred for beats coinciding with both tones and rests in a predictive way.The findings provide the first evidence for the neural coding of periodicity in auditory rhythm perception before the age of term. [ABSTRACT FROM AUTHOR]

To memorize a sequence, one must serially bind each item to its rank order. How the brain controls a given input to bind its associated order in sequence working memory (SWM) remains unexplored. Here, we investigated the neural representations underlying SWM control using electrophysiological recordings in the frontal cortex of macaque monkeys performing forward and backward SWM tasks. Separate and generalizable low-dimensional subspaces for sensory and memory information were found within the same frontal circuitry, and SWM control was reflected in these neural subspaces' organized dynamics. Each item at each rank was sequentially entered into a common sensory subspace and, depending on forward or backward task requirement, flexibly and timely sent into rank-selective SWM subspaces. Neural activity in these SWM subspaces faithfully predicted the recalled item and order information in single error trials. Thus, compositional neural population codes with well-orchestrated dynamics in frontal cortex support the flexible control of SWM. [Display omitted] • Separate low-dimensional sensory and memory subspaces are found in the frontal cortex • Spatial items enter a shared sensory subspace and then move into memory subspaces • Neural dynamics in forward and backward tasks reflect flexible control • Neural activities in memory subspaces predict monkeys' behavior Chen et al. discovered the separable and generalizable subspaces for sensory and sequence working memory in macaque frontal neural states. The neural activities and their dynamics within these subspaces reflected the flexible control of sequence working memory in various sequence sorting tasks, predicting monkeys' behavior in single trials. [ABSTRACT FROM AUTHOR]

A central principle in neuroscience is that neurons within the brain act in concert to produce perception, cognition, and adaptive behavior. Neurons are organized into specialized brain areas, dedicated to different functions to varying extents, and their function relies on distributed circuits to continuously encode relevant environmental and body-state features, enabling other areas to decode (interpret) these representations for computing meaningful decisions and executing precise movements. Thus, the distributed brain can be thought of as a series of computations that act to encode and decode information. In this perspective, we detail important concepts of neural encoding and decoding and highlight the mathematical tools used to measure them, including deep learning methods. We provide case studies where decoding concepts enable foundational and translational science in motor, visual, and language processing. Understanding how the brain works has been a formidable challenge in neuroscience, but advances at the computational level are now driving us closer. This perspective discusses core concepts of neural encoding and decoding and how moving toward causal modeling can enable translational and foundational insights into the neural code. [ABSTRACT FROM AUTHOR]

In mammals, action potentials fired by rapidly adapting mechanosensitive afferents are known to reliably time lock to the cycles of a vibration. How and where along the ascending neuraxis is the peripheral afferent temporal code transformed into a rate code are currently not clear. Here, we probed the encoding of vibrotactile stimuli with electrophysiological recordings along major stages of the ascending somatosensory pathway in mice. We discovered the main transformation step was identified at the level of the thalamus, and parvalbumin-positive interneurons in thalamic reticular nucleus participate in sharpening frequency selectivity and in disrupting the precise spike timing. When frequency-specific microstimulation was applied within the brainstem, it generated frequency selectivity reminiscent of real vibration responses in the somatosensory cortex and could provide informative and robust signals for learning in behaving mice. Taken together, these findings could guide biomimetic stimulus strategies to activate specific nuclei along the ascending somatosensory pathway for neural prostheses. [Display omitted] • Electrophysiological recording reveals a shift of neural coding for tactile vibration • Transformation from a temporal to a rate code involves thalamic inhibitory networks • Microstimulation in brainstem DCN can reproduce cortical selectivity for vibration • Microstimulation in DCN, but not in S1, can be used for vibrotactile discrimination Lee et al. show that the transformation of the vibrotactile temporal code found in the periphery into a rate code along the ascending somatosensory pathway involves thalamic processing, particularly through parvalbumin-positive interneurons in the reticular nucleus. Brainstem microstimulation mimics vibration responses in the somatosensory cortex, offering insights for neural prostheses. [ABSTRACT FROM AUTHOR]

The critical brain hypothesis states that the brain can benefit from operating close to a second-order phase transition. While it has been shown that several computational aspects of sensory processing (e.g., sensitivity to input) can be optimal in this regime, it is still unclear whether these computational benefits of criticality can be leveraged by neural systems performing behaviorally relevant computations. To address this question, we investigate signatures of criticality in networks optimized to perform efficient coding. We consider a spike-coding network of leaky integrateand-fire neurons with synaptic transmission delays. Previously, it was shown that the performance of such networks varies nonmonotonically with the noise amplitude. Interestingly, we find that in the vicinity of the optimal noise level for efficient coding, the network dynamics exhibit some signatures of criticality, namely, scale-free dynamics of the spiking and the presence of crackling noise relation. Our work suggests that two influential, and previously disparate theories of neural processing optimization (efficient coding and criticality) may be intimately related. [ABSTRACT FROM AUTHOR]

Understanding the brain requires studying its multiscale interactions from molecules to networks. The increasing availability of large-scale datasets detailing brain circuit composition, connectivity, and activity is transforming neuroscience. However, integrating and interpreting this data remains challenging. Concurrently, advances in supercomputing and sophisticated modeling tools now enable the development of highly detailed, large-scale biophysical circuit models. These mechanistic multiscale models offer a method to systematically integrate experimental data, facilitating investigations into brain structure, function, and disease. This review, based on a Society for Neuroscience 2024 MiniSymposium, aims to disseminate recent advances in large-scale mechanistic modeling to the broader community. It highlights (1) examples of current models for various brain regions developed through experimental data integration; (2) their predictive capabilities regarding cellular and circuit mechanisms underlying experimental recordings (e.g., membrane voltage, spikes, local-field potential, electroencephalography/magnetoencephalography) and brain function; and (3) their use in simulating biomarkers for brain diseases like epilepsy, depression, schizophrenia, and Parkinson’s, aiding in understanding their biophysical underpinnings and developing novel treatments. The review showcases state-of-the-art models covering hippocampus, somatosensory, visual, motor, auditory cortical, and thalamic circuits across species. These models predict neural activity at multiple scales and provide insights into the biophysical mechanisms underlying sensation, motor behavior, brain signals, neural coding, disease, pharmacological interventions, and neural stimulation. Collaboration with experimental neuroscientists and clinicians is essential for the development and validation of these models, particularly as datasets grow. Hence, this review aims to foster interest in detailed brain circuit models, leading to cross-disciplinary collaborations that accelerate brain research. [ABSTRACT FROM AUTHOR]

When making adaptive decisions, we actively demand information, but relatively little is known about the mechanisms of active information gathering. An open question is how the brain prospectively estimates the information gains that are expected to accrue from various sources by integrating simpler quantities of prior certainty and the reliability (diagnosticity) of a source. We examine this question using fMRI in a task in which people placed bids to obtain information in conditions that varied independently in the rewards, decision uncertainty, and information diagnosticity. We show that, consistent with value of information theory, the participants' bids are sensitive to prior certainty (the certainty about the correct choice before gathering information) and expected posterior certainty (the certainty expected after gathering information). Expected posterior certainty is decoded from multivoxel activation patterns in the posterior parietal and extrastriate cortices. This representation is independent of instrumental rewards and spatially overlaps with distinct representations of prior certainty and expected information gains. The findings suggest that the posterior parietal and extrastriate cortices are candidates for mediating the prospection of posterior probabilities as a key step to anticipating information gains during active gathering of instrumental information. Functional neuroimaging reveals multidimensional encodings of expected posterior certainty, possibly supporting humans' ability to anticipate the value of gathering information before making a choice. [ABSTRACT FROM AUTHOR]

Millisecond-scale temporal spiking patterns encode sensory information in the periphery, but their role in the neocortex remains controversial. The sense of touch provides a window into temporal coding because tactile neurons often exhibit precise, repeatable, and informative temporal spiking patterns. In the somatosensory cortex (S1), responses to skin vibrations exhibit phase locking that faithfully carries information about vibratory frequency. However, the respective roles of spike timing and rate in frequency coding are confounded because vibratory frequency shapes both the timing and rates of responses. To disentangle the contributions of these two neural features, we measured S1 responses as rhesus macaques performed frequency discrimination tasks in which differences in frequency were accompanied by orthogonal variations in amplitude. We assessed the degree to which the strength and timing of responses could account for animal performance. First, we showed that animals can discriminate frequency, but their performance is biased by amplitude variations. Second, rate-based representations of frequency are susceptible to changes in amplitude but in ways that are inconsistent with the animals’ behavioral biases, calling into question a rate-based neural code for frequency. In contrast, timing-based representations are highly informative about frequency but impervious to changes in amplitude, which is also inconsistent with the animals’ behavior. We account for the animals’ behavior with a model wherein frequency coding relies on a temporal code, but frequency judgments are biased by perceived magnitude. We conclude that information about vibratory frequency is not encoded in S1 firing rates but primarily in temporal patterning on millisecond timescales. [ABSTRACT FROM AUTHOR]

Cinema, a modern titan of entertainment, holds power to move people with the artful manipulation of auditory and visual stimuli. Despite this, the mechanisms behind how sensory stimuli elicit emotional responses are unknown. Thus, this study evaluated which brain regions were involved when sensory stimuli evoke auditory- or visual-driven emotions during film viewing. Using functional magnetic resonance imaging (fMRI) decoding techniques, we found that brain activities in the auditory area and insula represent the stimuli that evoke emotional response. The observation of brain activities in these regions could provide further insights to these mechanisms for the improvement of film-making, as well as the development of novel neural techniques in neuroscience. In near feature, such a "neuro-designed" products/ applications might gain in popularity. [ABSTRACT FROM AUTHOR]

Communication in the real world is inherently multimodal. When having a conversation, typically sighted and hearing people use both auditory and visual cues to understand one another. For example, objects may make sounds as they move in space, or we may use the movement of a person's mouth to better understand what they are saying in a noisy environment. Still, many neuroscience experiments rely on unimodal stimuli to understand encoding of sensory features in the brain. The extent to which visual information may influence encoding of auditory information and vice versa in natural environments is thus unclear. Here, we addressed this question by recording scalp electroencephalography (EEG) in 11 subjects as they listened to and watched movie trailers in audiovisual (AV), visual (V) only, and audio (A) only conditions. We then fit linear encoding models that described the relationship between the brain responses and the acoustic, phonetic, and visual information in the stimuli. We also compared whether auditory and visual feature tuning was the same when stimuli were presented in the original AV format versus when visual or auditory information was removed. In these stimuli, visual and auditory information was relatively uncorrelated, and included spoken narration over a scene as well as animated or live-action characters talking with and without their face visible. For this stimulus, we found that auditory feature tuning was similar in the AV and A-only conditions, and similarly, tuning for visual information was similar when stimuli were presented with the audio present (AV) and when the audio was removed (V only). In a cross prediction analysis, we investigated whether models trained on AV data predicted responses to A or V only test data similarly to models trained on unimodal data. Overall, prediction performance using AV training and V test sets was similar to using V training and V test sets, suggesting that the auditory information has a relatively smaller effect on EEG. In contrast, prediction performance using AV training and A only test set was slightly worse than using matching A only training and A only test sets. This suggests the visual information has a stronger influence on EEG, though this makes no qualitative difference in the derived feature tuning. In effect, our results show that researchers may benefit from the richness of multimodal datasets, which can then be used to answer more than one research question. Author summary: An increasing number of studies employ naturalistic audiovisual stimuli to uncover the neural tracking of sensory information, but how these may differ from more traditional unimodal paradigms is not well understood. We fit encoding models to predict EEG responses to auditory features (phonological features, the acoustic envelope, pitch) and visual features (Gabor wavelet and scene cuts) using movie trailers presented in audiovisual, audio only, and visual only conditions. We found that presenting an audiovisual stimulus resulted in similar encoding model weights to those derived from auditory or visual only stimuli. However, researchers should be aware of strong influences of visual evoked potentials on the ongoing EEG if studying speech perception in the brain using audiovisual stimuli like those described here. Overall, live action and animated movie trailers that do not solely rely on face information can be used as naturalistic audiovisual stimuli to better understand neural encoding of speech or visual information. [ABSTRACT FROM AUTHOR]

Learning to read places a strong challenge on the visual system. Years of expertise lead to a remarkable capacity to separate similar letters and encode their relative positions, thus distinguishing words such as FORM and FROM, invariantly over a large range of positions, sizes and fonts. How neural circuits achieve invariant word recognition remains unknown. Here, we address this issue by recycling deep neural network models initially trained for image recognition. We retrain them to recognize written words and then analyze how reading-specialized units emerge and operate across the successive layers. With literacy, a small subset of units becomes specialized for word recognition in the learned script, similar to the visual word form area (VWFA) in the human brain. We show that these units are sensitive to specific letter identities and their ordinal position from the left or the right of a word. The transition from retinotopic to ordinal position coding is achieved by a hierarchy of "space bigram" unit that detect the position of a letter relative to a blank space and that pool across low- and high-frequency-sensitive units from early layers of the network. The proposed scheme provides a plausible neural code for written words in the VWFA, and leads to predictions for reading behavior, error patterns, and the neurophysiology of reading. Author summary: Reading is a fundamental skill in modern society, yet the neural mechanisms that allow us to quickly recognize words remain poorly understood. Our research aims to unravel how the brain achieves invariant word recognition—the ability to recognize words regardless of their position, size, or font. We studied artificial neural networks trained to recognize words, mirroring human learning. Our findings reveal that these networks develop specialized units for word recognition, similar to the Visual Word Form Area in the human brain. These units are sensitive to specific letters and their positions within a word. Crucially, we discovered that they achieve this by detecting the spaces around words as reference points. This creates a hierarchical system where early layers detect basic features and spaces, while higher layers combine this information to recognize specific letters at certain positions relative to word edges. This "space bigram" model reconciles previous theories of letter bigrams and letter-position coding. Our results suggest that most written languages may be processed using similar basic principles. This understanding could inform better methods for teaching reading and treating reading disorders. [ABSTRACT FROM AUTHOR]

Neural activity in the cortex exhibits a wide range of firing variability and rich correlation structures. Studies on neural coding indicate that correlated neural variability can influence the quality of neural codes, either beneficially or adversely. However, the mechanisms by which correlated neural variability is transformed and processed across neural populations to achieve meaningful computation remain largely unclear. Here we propose a theory of covariance computation with spiking neurons which offers a unifying perspective on neural representation and computation with correlated noise. We employ a recently proposed computational framework known as the moment neural network to resolve the nonlinear coupling of correlated neural variability with a task-driven approach to constructing neural network models for performing covariance-based perceptual tasks. In particular, we demonstrate how perceptual information initially encoded entirely within the covariance of upstream neurons' spiking activity can be passed, in a near-lossless manner, to the mean firing rate of downstream neurons, which in turn can be used to inform inference. The proposed theory of covariance computation addresses an important question of how the brain extracts perceptual information from noisy sensory stimuli to generate a stable perceptual whole and indicates a more direct role that correlated variability plays in cortical information processing. Author summary: Understanding how the brain represents and processes perceptual information through neuronal firing patterns is at the heart of neuroscience. The prevailing idea suggests that the information is primarily encoded in mean firing rates, whereas correlations among neurons may play a secondary role. However, given that firing variability is ubiquitously observed in cortical neurons, one wonders if correlated noise may play a more central role in neural computation than previously thought. Here, we propose that perceptual information can be encoded in part or even entirely in the correlated variability of spiking neurons. Through a combination of theoretical modeling and machine learning approaches, we construct neural network models capable of processing correlated variability in a task-driven way. We demonstrate that the trained network is able to learn to extract covariance-encoded perceptual information to generate stimulus-selectivity in their mean firing rates, thanks to the nonlinear coupling of statistical moments of their activity. Information-theoretic analysis reveals a near-lossless transfer of perceptual information from the covariance of upstream neurons to the mean firing rate of downstream neurons. Our work offers new insights into the role of correlated variability in cortical processing and hints towards a task-driven paradigm for studying cortical computation with biologically plausible neural network models. [ABSTRACT FROM AUTHOR]

Metamorphic testing (MT) is an effective testing technique having a broad range of applications. One key task for MT is the identification of metamorphic relations (MRs), which is a fundamental mechanism in MT and is critical to the automation of MT. Prior studies have proposed approaches for predicting MRs (PMR). One major idea behind these PMR approaches is to represent program source code information via manually designed code features and then to apply machine‐learning–based classifiers to automatically predict whether a specific MR can be applied on the target program. Nevertheless, the human‐involved procedure of selecting and extracting code features is costly, and it may not be easy to obtain sufficiently comprehensive features for representing source code. To overcome this limitation, in this study, we explore and evaluate the effectiveness of code representation learning techniques for PMR. By applying neural code representation models for automatically mapping program source code to code vectors, the PMR procedure can be boosted with learned code representations. We develop 32 PMR instances by, respectively, combining 8 code representation models with 4 typical classification models and conduct an extensive empirical study to investigate the effectiveness of code representation learning techniques in the context of MR prediction. Our findings reveal that code representation learning can positively contribute to the prediction of MRs and provide insights into the practical usage of code representation models in the context of MR prediction. Our findings could help researchers and practitioners to gain a deeper understanding of the strength of code representation learning for PMR and, hence, pave the way for future research in deriving or extracting MRs from program source code. [ABSTRACT FROM AUTHOR]

Over the past few years, neural networks have started penetrating safety critical systems to make decisions as, for example, in robots, rockets, and autonomous driving cars. Neural networks based on floating-point arithmetic are very time and memory consuming, which are not compatible with embedded systems known to have limited resources. They are also very sensitive to the precision in which they have been trained, so changing this precision generally degrades the quality of their answers. To deal with that, we introduce a new technique to generate a fixed-point code for a trained neural network. This technique is based on fixed-point arithmetic with mixed-precision. This arithmetic is based on integer operations only, which are compatible with small memory devices. The obtained neural network has the same behavior as the initial one (based on the floating-point arithmetic) up to an error threshold defined by the user. The experimental results show the efficiency of our tool SyFix in terms of memory saved and the accuracy of the computations. [ABSTRACT FROM AUTHOR]

We present the latest release of PANNA 2.0 (Properties from Artificial Neural Network Architectures), a code for the generation of neural network interatomic potentials based on local atomic descriptors and multilayer perceptrons. Built on a new back end, this new release of PANNA features improved tools for customizing and monitoring network training, better graphics processing unit support including a fast descriptor calculator, new plugins for external codes, and a new architecture for the inclusion of long-range electrostatic interactions through a variational charge equilibration scheme. We present an overview of the main features of the new code, and several benchmarks comparing the accuracy of PANNA models to the state of the art, on commonly used benchmarks as well as richer datasets. [ABSTRACT FROM AUTHOR]

A core challenge for the brain is to process information across various timescales. This could be achieved by a hierarchical organization of temporal processing through intrinsic mechanisms (e.g., recurrent coupling or adaptation), but recent evidence from spike recordings of the rodent visual system seems to conflict with this hypothesis. Here, we used an optimized information-theoretic and classical autocorrelation analysis to show that information- and correlation timescales of spiking activity increase along the anatomical hierarchy of the mouse visual system under visual stimulation, while information-theoretic predictability decreases. Moreover, intrinsic timescales for spontaneous activity displayed a similar hierarchy, whereas the hierarchy of predictability was stimulus-dependent. We could reproduce these observations in a basic recurrent network model with correlated sensory input. Our findings suggest that the rodent visual system employs intrinsic mechanisms to achieve longer integration for higher cortical areas, while simultaneously reducing predictability for an efficient neural code. Author summary: How the brain integrates information across different timescales is a fundamental question in neuroscience. Results from primates suggest that higher areas in cortex are specialized on integrating information on longer timescales through stronger network recurrence. However, due to anatomical differences and conflicting empirical evidence, it remains open whether this is a property that is shared among species as a general feature of temporal processing. For example, in rodents, higher cortical areas show an increase in adaptation, which suggests stronger redundancy reduction that might oppose an enhanced temporal integration. Here, we combined an information theoretic analysis with an analysis of correlation timescales and found an increase in information and correlation timescales across the anatomical hierarchy of the mouse visual system. Notably, this upward trend is accompanied by a simultaneous reduction in the predictability of single-neuron spiking, suggesting a decrease in temporal redundancy. We could reproduce these findings in recurrent network models, which demonstrates that enhanced temporal integration through an increase in recurrence does not necessarily oppose a reduction of redundancy for individual neurons. Together with our empirical findings, this suggests that mouse visual cortex might exploit both, enhanced temporal integration and stronger adaptation, to tune hierarchical temporal processing. [ABSTRACT FROM AUTHOR]

Knowledge integration based on the relationship between structure and function of the neural substrate is one of the main targets of neuroinformatics and data-driven computational modeling. However, the multiplicity of data sources, the diversity of benchmarks, the mixing of observables of different natures, and the necessity of a long-term, systematic approach make such a task challenging. Here we present a first snapshot of a long-term integrative modeling program designed to address this issue in the domain of the visual system: a comprehensive spiking model of cat primary visual cortex. The presented model satisfies an extensive range of anatomical, statistical and functional constraints under a wide range of visual input statistics. In the presence of physiological levels of tonic stochastic bombardment by spontaneous thalamic activity, the modeled cortical reverberations self-generate a sparse asynchronous ongoing activity that quantitatively matches a range of experimentally measured statistics. When integrating feed-forward drive elicited by a high diversity of visual contexts, the simulated network produces a realistic, quantitatively accurate interplay between visually evoked excitatory and inhibitory conductances; contrast-invariant orientation-tuning width; center surround interactions; and stimulus-dependent changes in the precision of the neural code. This integrative model offers insights into how the studied properties interact, contributing to a better understanding of visual cortical dynamics. It provides a basis for future development towards a comprehensive model of low-level perception. Author summary: Computational modeling can integrate fragments of understanding generated by experimental neuroscience. However, most models considered only a few features of neural computation at a time, leading to either poorly constrained models with many parameters, or lack of expressiveness in over-simplified models. A solution is to develop detailed models, but constrain them with a broad range of anatomical and functional data to prevent overfitting. This requires a long-term systematic approach. Here we present a first snapshot of such an integrative program: a large-scale spiking model of cat primary visual cortex, that is constrained by an extensive range of anatomical and functional features. Together with the associated modeling infrastructure, this study lays the groundwork for a broad integrative modeling program seeking an in-depth understanding of vision. [ABSTRACT FROM AUTHOR]

Brain-computer interface (BCI) is a revolutionizing human-computer interaction with potential applications in both medical and non-medical fields, emerging as a cutting-edge and trending research direction. Increasing numbers of groups are engaging in BCI research and development. However, in recent years, there has been some confusion regarding BCI, including misleading and hyped propaganda about BCI, and even non-BCI technologies being labeled as BCI. Therefore, a clear definition and a definite scope for BCI are thoroughly considered and discussed in the paper, based on the existing definitions of BCI, including the six key or essential components of BCI. In the review, different from previous definitions of BCI, BCI paradigms and neural coding are explicitly included in the clear definition of BCI provided, and the BCI user (the brain) is clearly identified as a key component of the BCI system. Different people may have different viewpoints on the definition and scope of BCI, as well as some related issues, which are discussed in the article. This review argues that a clear definition and definite scope of BCI will benefit future research and commercial applications. It is hoped that this review will reduce some of the confusion surrounding BCI and promote sustainable development in this field. [ABSTRACT FROM AUTHOR]

Epileptic seizures are debilitating because of the clinical symptoms they produce. These symptoms, in turn, may stem directly from disruptions in neural coding. Recent evidence has suggested that the specific temporal order, or sequence, of spiking across a population of cortical neurons may encode information. Here, we investigate how seizures disrupt neuronal spiking sequences in the human brain by recording multi-unit activity from the cerebral cortex in five male participants undergoing monitoring for seizures. We find that pathological discharges during seizures are associated with bursts of spiking activity across a population of cortical neurons. These bursts are organized into highly consistent and stereotyped temporal sequences. As the seizure evolves, spiking sequences diverge from the sequences observed at baseline and become more spatially organized. The direction of this spatial organization matches the direction of the ictal discharges, which spread over the cortex as traveling waves. Our data therefore suggest that seizures can entrain cortical spiking sequences by changing the spatial organization of neuronal firing, providing a possible mechanism by which seizures create symptoms. The precise mechanism by which seizures disrupt neural coding remains unclear. Here, the authors show that discharges arising from the seizure source travel macroscopic distances, where they go on to influence spiking sequences at the microscopic scale. [ABSTRACT FROM AUTHOR]

Simple Summary: Our study demonstrated neural encoding of incidental sensory regularities leading to modulation of cortical responses to both predictive and predicted sensory stimuli. As in the case of goal-directed behavior, such task-irrelevant predictive mechanisms might result from the brain's intrinsic drive of reducing uncertainty about state transition dynamics of the environment. Statistical learning of sensory patterns can lead to predictive neural processes enhancing stimulus perception and enabling fast deviancy detection. Predictive processes have been extensively demonstrated when environmental statistical regularities are relevant to task execution. Preliminary evidence indicates that statistical learning can even occur independently of task relevance and top-down attention, although the temporal profile and neural mechanisms underlying sensory predictions and error signals induced by statistical learning of incidental sensory regularities remain unclear. In our study, we adopted an implicit sensory conditioning paradigm that elicited the generation of specific perceptual priors in relation to task-irrelevant audio–visual associations, while recording Electroencephalography (EEG). Our results showed that learning task-irrelevant associations between audio–visual stimuli resulted in anticipatory neural responses to predictive auditory stimuli conveying anticipatory signals of expected visual stimulus presence or absence. Moreover, we observed specific modulation of cortical responses to probabilistic visual stimulus presentation or omission. Pattern similarity analysis indicated that predictive auditory stimuli tended to resemble the response to expected visual stimulus presence or absence. Remarkably, Hierarchical Gaussian filter modeling estimating dynamic changes of prediction error signals in relation to differential probabilistic occurrences of audio–visual stimuli further demonstrated instantiation of predictive neural signals by showing distinct neural processing of prediction error in relation to violation of expected visual stimulus presence or absence. Overall, our findings indicated that statistical learning of non-salient and task-irrelevant perceptual regularities could induce the generation of neural priors at the time of predictive stimulus presentation, possibly conveying sensory-specific information about the predicted consecutive stimulus. [ABSTRACT FROM AUTHOR]

The complete characterization of the almost-entropic region yields rate regions for network coding problems. However, this characterization is difficult and open. In this paper, we propose a novel algorithm to determine whether an arbitrary vector in the entropy space is entropic or not, by parameterizing and generating probability mass functions by neural networks. Given a target vector, the algorithm minimizes the normalized distance between the target vector and the generated entropic vector by training the neural network. The algorithm reveals the entropic nature of the target vector, and obtains the underlying distribution, accordingly. The proposed algorithm was further implemented with convolutional neural networks, which naturally fit the structure of joint probability mass functions, and accelerate the algorithm with GPUs. Empirical results demonstrate improved normalized distances and convergence performances compared with prior works. We also conducted optimizations of the Ingleton score and Ingleton violation index, where a new lower bound of the Ingleton violation index was obtained. An inner bound of the almost-entropic region with four random variables was constructed with the proposed method, presenting the current best inner bound measured by the volume ratio. The potential of a computer-aided approach to construct achievable schemes for network coding problems using the proposed method is discussed. [ABSTRACT FROM AUTHOR]

This paper presents a procedure for and evaluation of using a semantic similarity metric as a loss function for neural source code summarization. Code summarization is the task of writing natural language descriptions of source code. Neural code summarization refers to automated techniques for generating these descriptions using neural networks. Almost all current approaches involve neural networks as either standalone models or as part of a pretrained large language models, for example, GPT, Codex, and LLaMA. Yet almost all also use a categorical cross‐entropy (CCE) loss function for network optimization. Two problems with CCE are that (1) it computes loss over each word prediction one‐at‐a‐time, rather than evaluating a whole sentence, and (2) it requires a perfect prediction, leaving no room for partial credit for synonyms. In this paper, we extend our previous work on semantic similarity metrics to show a procedure for using semantic similarity as a loss function to alleviate this problem, and we evaluate this procedure in several settings in both metrics‐driven and human studies. In essence, we propose to use a semantic similarity metric to calculate loss over the whole output sentence prediction per training batch, rather than just loss for each word. We also propose to combine our loss with CCE for each word, which streamlines the training process compared to baselines. We evaluate our approach over several baselines and report improvement in the vast majority of conditions. [ABSTRACT FROM AUTHOR]

Evidence for metastable dynamics and its role in brain function is emerging at a fast pace and is changing our understanding of neural coding by putting an emphasis on hidden states of transient activity. Clustered networks of spiking neurons have enhanced synaptic connections among groups of neurons forming structures called cell assemblies; such networks are capable of producing metastable dynamics that is in agreement with many experimental results. However, it is unclear how a clustered network structure producing metastable dynamics may emerge from a fully local plasticity rule, i.e., a plasticity rule where each synapse has only access to the activity of the neurons it connects (as opposed to the activity of other neurons or other synapses). Here, we propose a local plasticity rule producing ongoing metastable dynamics in a deterministic, recurrent network of spiking neurons. The metastable dynamics co-exists with ongoing plasticity and is the consequence of a self-tuning mechanism that keeps the synaptic weights close to the instability line where memories are spontaneously reactivated. In turn, the synaptic structure is stable to ongoing dynamics and random perturbations, yet it remains sufficiently plastic to remap sensory representations to encode new sets of stimuli. Both the plasticity rule and the metastable dynamics scale well with network size, with synaptic stability increasing with the number of neurons. Overall, our results show that it is possible to generate metastable dynamics over meaningful hidden states using a simple but biologically plausible plasticity rule which co-exists with ongoing neural dynamics. Author summary: Neural activity in cortex is often characterized by sequences of transient patterns called metastable states. These metastable states have been found to code for important task variables such as stimuli, expectations, and decisions; their switching dynamics can be thought of as reactivations of the corresponding neural representations. This dynamics can be explained by a neural network model wherein neurons are organized in clusters, with neurons inside the same cluster connected by stronger synapses than neurons belonging to different clusters. But what is the origin of this structure? In this work, we demonstrate how such a structure may emerge via synaptic plasticity. In our model, the synapses continuously adapt their values based on the activities of the neurons they connect. This leads to the structuring of the network and metastable dynamics in the presence of repeated stimuli, but also to robustness to the network's self-generated fluctuations in the absence of consistent stimuli. Most importantly, our model uncovers a self-organizing mechanism that keeps the network in a regime where metastability is the only steady dynamics: weaker or stronger synapses would both lead to the absence of metastable states, but the synaptic plasticity rule interacts with the neural dynamics to keep the values of the synaptic weights within the necessary bounds. [ABSTRACT FROM AUTHOR]

Different stimuli can potentially activate overlapping populations of neurons in the brain. How does the brain maintain information about multiple items? Here, we describe a new theory: neurons might switch back and forth between encoding each item across time. Recent statistical advances have allowed neurophysiology studies to probe for such activity fluctuations, generating support for this theory and opening intriguing new research directions. Many open questions remain, such as the time scale of activity fluctuations, the manner in which they are coordinated across (and read out from) neural ensembles, and the implications for perceptual binding, neural oscillations, and cognitive processes like attention, memory, and thought. Our ability to perceive multiple objects is mysterious. Sensory neurons are broadly tuned, producing potential overlap in the populations of neurons activated by each object in a scene. This overlap raises questions about how distinct information is retained about each item. We present a novel signal switching theory of neural representation, which posits that neural signals may interleave representations of individual items across time. Evidence for this theory comes from new statistical tools that overcome the limitations inherent to standard time-and-trial-pooled assessments of neural signals. Our theory has implications for diverse domains of neuroscience, including attention, figure binding/scene segregation, oscillations, and divisive normalization. The general concept of switching between functions could also lend explanatory power to theories of grounded cognition. [ABSTRACT FROM AUTHOR]

The prefrontal cortex is a key region involved in working memory. Working memory related neural activities in the prefrontal cortex exhibit unexpectedly rich and complex dynamics during even the simplest tasks — a phenomenon called 'dynamic coding'. Task-optimized neural networks also exhibit dynamic coding when optimized on working memory tasks. Two key aspects of a neural network determine whether it exhibits dynamic coding: the connectivity of the network and the inputs it receives. Dynamic coding results from an optimality principle of robust working memory maintenance. Working memory (WM) is a fundamental aspect of cognition. WM maintenance is classically thought to rely on stable patterns of neural activities. However, recent evidence shows that neural population activities during WM maintenance undergo dynamic variations before settling into a stable pattern. Although this has been difficult to explain theoretically, neural network models optimized for WM typically also exhibit such dynamics. Here, we examine stable versus dynamic coding in neural data, classical models, and task-optimized networks. We review principled mathematical reasons for why classical models do not, while task-optimized models naturally do exhibit dynamic coding. We suggest an update to our understanding of WM maintenance, in which dynamic coding is a fundamental computational feature rather than an epiphenomenon. [ABSTRACT FROM AUTHOR]

This document is the back matter of Volume 12 of the journal "Timing & Time Perception." It includes various articles and reviews related to the study of time perception. The articles cover topics such as Vierordt's Law, differences in time perspective among different generations, the relationship between decision-making, time perspective, and stress, and the psychometric validation of the German adaptation of the Temporal Metacognition Scale. The document also includes a special issue on the topic of "The Two Times Problem: Physical Time vs. Human Time," which features commentaries and discussions on the nature of time from different perspectives. [Extracted from the article]

This paper investigates the intricate connection between visual perception and the mathematical modeling of neural activity in the primary visual cortex (V1). The focus is on modeling the visual MacKay effect [D. M. MacKay, Nature, 180 (1957), pp. 849--850]. While bifurcation theory has been a prominent mathematical approach for addressing issues in neuroscience, especially in describing spontaneous pattern formations in V1 due to parameter changes, it faces challenges in scenarios with localized sensory inputs. This is evident, for instance, in MacKay's psychophysical experiments, where the redundancy of visual stimuli information results in irregular shapes, making bifurcation theory and multiscale analysis less effective. To address this, we follow a mathematical viewpoint based on the input-output controllability of an Amari-type neural fields model. In this framework, we consider sensory input as a control function, a cortical representation via the retino-cortical map of the visual stimulus that captures its distinct features. This includes highly localized information in the center of MacKay's funnel pattern "MacKay rays." From a control theory point of view, the Amari-type equation's exact controllability property is discussed for linear and nonlinear response functions. For the visual MacKay effect modeling, we adjust the parameter representing intra-neuron connectivity to ensure that cortical activity exponentially stabilizes to the stationary state in the absence of sensory input. Then, we perform quantitative and qualitative studies to demonstrate that they capture all the essential features of the induced after-image reported by MacKay. [ABSTRACT FROM AUTHOR]

To enhance the software implementation process, developers frequently leverage preexisting code snippets by exploring an extensive codebase. Existing code search tools often rely on keyword- or syntactic-based methods and struggle to fully grasp the semantics and intent behind code snippets. In this paper, we propose a novel hybrid C2B model that combines CodeT5 and bidirectional long short-term memory (Bi-LSTM) for source code search and recommendation. Our proposed C2B hybrid model leverages CodeT5's domain-specific pretraining and Bi-LSTM's contextual understanding to improve code representation and capture sequential dependencies. As a proof-of-concept application, we implemented the proposed C2B hybrid model as a deep neural code search tool and empirically evaluated the model on the large-scale dataset of CodeSearchNet. The experimental findings showcase that our methodology proficiently retrieves pertinent code snippets and surpasses the performance of prior state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

People form impressions about others during daily social encounters and infer personality traits from others' behaviors. Such trait inference is thought to rely on two universal dimensions: competence and warmth. These two dimensions can be used to construct a 'social cognitive map' organizing massive information obtained from social encounters efficiently. Originating from spatial cognition, the neural codes supporting the representation and navigation of spatial cognitive maps have been widely studied. Recent studies suggest similar neural mechanism subserves the map-like architecture in social cognition as well. Here we investigated how spatial codes operate beyond the physical environment and support the representation and navigation of social cognitive map. We designed a social value space defined by two dimensions of competence and warmth. Behaviorally, participants were able to navigate to a learned location from random starting locations in this abstract social space. At the neural level, we identified the representation of distance in the precuneus, fusiform gyrus, and middle occipital gyrus. We also found partial evidence of grid-like representation patterns in the medial prefrontal cortex and entorhinal cortex. Moreover, the intensity of grid-like response scaled with the performance of navigating in social space and social avoidance trait scores. Our findings suggest a neurocognitive mechanism by which social information can be organized into a structured representation, namely cognitive map and its relevance to social well-being. [ABSTRACT FROM AUTHOR]

With the advancement of internet communication and telemedicine, people are increasingly turning to the web for various healthcare activities. With an ever-increasing number of diseases and symptoms, diagnosing patients becomes challenging. In this work, we build a diagnosis assistant to assist doctors, which identifies diseases based on patient–doctor interaction. During diagnosis, doctors utilize both symptomatology knowledge and diagnostic experience to identify diseases accurately and efficiently. Inspired by this, we investigate the role of medical knowledge in disease diagnosis through doctor–patient interaction. We propose a two-channel, knowledge-infused, discourse-aware disease diagnosis model (KI-DDI), where the first channel encodes patient–doctor communication using a transformer-based encoder, while the other creates an embedding of symptom-disease using a graph attention network (GAT). In the next stage, the conversation and knowledge graph embeddings are infused together and fed to a deep neural network for disease identification. Furthermore, we first develop an empathetic conversational medical corpus comprising conversations between patients and doctors, annotated with intent and symptoms information. The proposed model demonstrates a significant improvement over the existing state-of-the-art models, establishing the crucial roles of (a) a doctor's effort for additional symptom extraction (in addition to patient self-report) and (b) infusing medical knowledge in identifying diseases effectively. Many times, patients also show their medical conditions, which acts as crucial evidence in diagnosis. Therefore, integrating visual sensory information would represent an effective avenue for enhancing the capabilities of diagnostic assistants. [ABSTRACT FROM AUTHOR]

The behavioral and neural effects of the endogenous release of acetylcholine following stimulation of the nucleus basalis (NB) of Meynert have been recently examined in two male monkeys (Qi et al., 2021). Counterintuitively, NB stimulation enhanced behavioral performance while broadening neural tuning in the prefrontal cortex (PFC). The mechanism by which a weaker mnemonic neural code could lead to better performance remains unclear. Here, we show that increased neural excitability in a simple continuous bump attractor model can induce broader neural tuning and decrease bump diffusion, provided neural rates are saturated. Increased memory precision in the model overrides memory accuracy, improving overall task performance. Moreover, we show that bump attractor dynamics can account for the nonuniform impact of neuromodulation on distractibility, depending on distractor distance from the target. Finally, we delve into the conditions under which bump attractor tuning and diffusion balance in biologically plausible heterogeneous network models. In these discrete bump attractor networks, we show that reducing spatial correlations or enhancing excitatory transmission can improve memory precision. Altogether, we provide a mechanistic understanding of how cholinergic neuromodulation controls spatial working memory through perturbed attractor dynamics in the PFC. [ABSTRACT FROM AUTHOR]

Simple Summary: Many individuals report difficulty perceiving speech in background noise. Research has shown that one factor in this type of processing is the effect of noise on the brain's response to speech. Different types of noise affect the strength of the neural encoding of speech to varying degrees, with greater informational masking causing greater degradation. Previous research has focused on the effects of attention in noise, which tends to enhance the neural encoding of the speech signal via inhibitory mechanisms. However, there is a pre-attentive stage of inhibition, sensory gating, which has been linked to speech perception in noise. The impact of noise on the neural response during this phase remains unclear. This preliminary study investigated the gating response, recorded via high-density EEG in fifteen normal-hearing adults, in quiet and noise conditions with low and high informational masking. Scalp maps were generated to observe brain activation in each condition. Significant gating occurred in quiet, while noise conditions resulted in a significantly decreased gating response. The gating function was significantly degraded in noise with less informational masking content, coinciding with a reduced activation of inhibitory gating networks. These findings illustrate the adverse effect of noise on pre-attentive inhibition related to speech perception. Cortical auditory evoked potentials (CAEPs) indicate that noise degrades auditory neural encoding, causing decreased peak amplitude and increased peak latency. Different types of noise affect CAEP responses, with greater informational masking causing additional degradation. In noisy conditions, attention can improve target signals' neural encoding, reflected by an increased CAEP amplitude, which may be facilitated through various inhibitory mechanisms at both pre-attentive and attentive levels. While previous research has mainly focused on inhibition effects during attentive auditory processing in noise, the impact of noise on the neural response during the pre-attentive phase remains unclear. Therefore, this preliminary study aimed to assess the auditory gating response, reflective of the sensory inhibitory stage, to repeated vowel pairs presented in background noise. CAEPs were recorded via high-density EEG in fifteen normal-hearing adults in quiet and noise conditions with low and high informational masking. The difference between the average CAEP peak amplitude evoked by each vowel in the pair was compared across conditions. Scalp maps were generated to observe general cortical inhibitory networks in each condition. Significant gating occurred in quiet, while noise conditions resulted in a significantly decreased gating response. The gating function was significantly degraded in noise with less informational masking content, coinciding with a reduced activation of inhibitory gating networks. These findings illustrate the adverse effect of noise on pre-attentive inhibition related to speech perception. [ABSTRACT FROM AUTHOR]

SUMMARY: Pre‐harvest sprouting (PHS) is a significant threat to global food security due to its association with losses in both yield and quality. Among the genes involved in PHS resistance in wheat, PHS‐3D (TaMyb10‐D) plays a crucial role. Here, we characterized the sequence variations of TaMyb10 genes in 416 bread wheat and 302 Aegilops tauschii accessions. Within TaMyb10‐A sequences, we identified a deletion ranging from 214 to 305 bp in the signal and amino acid coding region, present in 61.3% of the accessions. Similarly, 79.3% of the TaMyb10‐B sequences within the third exon region exhibited a 19 bp deletion. Additionally, 40.8% of the accessions lacked the 2.4 Mb fragment (in/del mutations) on Chr3D, where TaMyb10‐D/PHS‐3D was located. Interestingly, the geographical distribution of accessions showed little correlation with the divergence of TaMyb10. TaMyb10‐A‐IIIDele, TaMyb10‐B‐IVDele, and TaMyb10‐D‐VDele genotypes were prevalent in wheat populations across continents. Despite their structural variations, the five distinct protein types exhibited comparable ability to bind the promoters of downstream genes in the flavonoid and ABA pathways, such as CHS, DFR, and NCED. Furthermore, the combination of TaMyb10 homologs was significantly associated with grain color and germination percentages. Accessions exclusively harboring TaMyb10‐D displayed red seed color and reduced germination percentages, indicating the predominant role of TaMyb10‐D compared to TaMyb10‐A and TaMyb10‐B. This comprehensive investigation enhances our understanding of the structural variations and functional divergence of TaMyb10, providing valuable insights and resources for improving PHS resistance in wheat. Significance Statement: TaPHS‐3D (TaMyb10‐D) plays a crucial role among the genes implicated in pre‐harvest sprouting resistance in wheat. Our study revealed a significant correlation between the combination of TaMyb10 homologs, grain color, and germination. Notably, TaMyb10‐D exhibited a more substantial functional impact than TaMyb10‐A and TaMyb10‐B. These findings deepen our understanding of the structural variations and functional distinctions within TaMyb10, providing valuable insights and resources for improving PHS resistance in wheat. [ABSTRACT FROM AUTHOR]

Code smells are poorly designed code structures indicating that the code may need to be refactored. Recognizing code smells in practice is complex, and researchers strive to develop automatic code smell detectors. An obstacle to developing these solutions is the datasets' limitations. Manually labeled datasets were collected to investigate the developers' perceptions of code smells. They are characterized by a high label disagreement that hurts the performance of Machine Learning (ML) models trained using them. Furthermore, all large, manually labeled datasets are developed for Java. We recently created a novel dataset for C# to alleviate these issues. This paper evaluates ML code smell detection approaches on our novel dataset. We consider two feature representations to train ML models: (1) code metrics and (2) CodeT5 embeddings. This study is the first to consider the CodeT5 state-of-the-art neural source code embedding for code smell detection in C#. To prove the effectiveness of ML, we consider multiple metrics-based heuristics as alternatives. In our experiments, the best-performing approach was the ML classifier trained on code metrics (F-measure of 0.87 for Long Method and 0.91 for Large Class detection). However, the performance improvement over CodeT5 features is negligible if we consider the advantages of automatically inferring features. Finally, our ML model surpassed less experienced annotators and nearly matched the most experienced annotator, suggesting it can assist less experienced developers under tight deadlines. To the best of our knowledge, this is the first study to compare the performance of automatic smell detectors against human performance. [ABSTRACT FROM AUTHOR]

When looking at faces, humans invariably move their eyes to a consistent preferred first fixation location on the face. While most people have the preferred fixation location just below the eyes, a minority have it between the nose-tip and mouth. Not much is known about whether these long-term differences in the preferred fixation location are associated with distinct neural representations of faces. To study this, we used a gaze-contingent face adaptation aftereffect paradigm to test in two groups of observers, one with their mean preferred fixation location closer to the eyes (upper lookers) and the other closer to the mouth (lower lookers). In this task, participants were required to maintain their gaze at either their own group's mean preferred fixation location or that of the other group during adaptation and testing. The two possible fixation locations were 3.6° apart on the face. We measured the face adaptation aftereffects when the adaptation and testing happened while participants maintained fixation at either the same or different locations on the face. Both groups showed equally strong adaptation effects when the adaptation and testing happened at the same fixation location. Crucially, only the upper lookers showed a partial transfer of the FAE across the two fixation locations, when adaptation occurred at the eyes. Lower lookers showed no spatial transfer of the FAE irrespective of the adaptation position. Given the classic finding that neural tuning is increasingly position invariant as one moves higher in the visual hierarchy, this result suggests that differences in the preferred fixation location are associated with distinct neural representations of faces. [ABSTRACT FROM AUTHOR]

This article presents an update on known and unknown biological codes. While the genetic code has been recognized as a code for decades, most other codes were hidden in the shadow of this hallmark discovery. It was the dawn of the new millennium when the histone language and code were proclaimed in the years 2000 and 2001, respectively, marking the start of an explosion in the number of published codes across all biological disciplines since then. Actually, there are hundreds to thousands of biological codes, but only a fraction—although still a substantial number—have been explicitly described as such. A first assessment of those codes allows for a simple categorization into structural and regulatory codes, at the same time maintaining Marcello Barbieri´s initial differentiation of organic versus neural codes. The article reviews prominent codes and showcases restriction-modification and toxin-antitoxin systems as code systems that have gone unrecognized until today. This article should read as a motivation for all those who want to discover ever more new codes—from literature or in the field. [ABSTRACT FROM AUTHOR]

Prior studies have demonstrated that deep learning-based approaches can enhance the performance of source code vulnerability detection by training neural networks to learn vulnerability patterns in code representations. However, due to limitations in code representation and neural network design, the validity and practicality of the model still need to be improved. Additionally, due to differences in programming languages, most methods lack cross-language detection generality. To address these issues, in this paper, we analyze the shortcomings of previous code representations and neural networks. We propose a novel hierarchical code representation that combines Concrete Syntax Trees (CST) with Program Dependence Graphs (PDG). Furthermore, we introduce a Tree-Graph-Gated- Attention (TGGA) network based on gated recurrent units and attention mechanisms to build a Hierarchical Code Representation learning-based Vulnerability Detection (HCRVD) system. This system enables cross-language vulnerability detection at the function-level. The experiments show that HCRVD surpasses many competitors in vulnerability detection capabilities. It benefits from the hierarchical code representation learning method, and outperforms baseline in cross-language vulnerability detection by 9.772% and 11.819% in the C/C++ and Java datasets, respectively. Moreover, HCRVD has certain ability to detect vulnerabilities in unknown programming languages and is useful in real open-source projects. HCRVD shows good validity, generality and practicality. [ABSTRACT FROM AUTHOR]

The article titled "Elegy for Neural Coding in Understanding Cognition: Brains are Not Computers" challenges the prevailing belief that the brain functions like a computer. The author argues that the metaphor of neural coding, which divorces the brain from its causal structure, is problematic and does not adequately explain cognitive events, thinking, and memory. The article highlights the complexity and randomness of neural processes and emphasizes that human behavior cannot be explained solely through information processing. The author suggests that researchers should move away from the information processing metaphor and focus on observing and measuring phenomena instead. [Extracted from the article]

Background: A major challenge of evolutionary biology is making underlying concepts accessible to wide audiences. One method for doing so is to utilise multi-media formats that have potential to engage and inform through entertainment. This pilot study outlines and discusses a sonification concert that integrated musical performance with a range of evolutionary concepts and ideas fundamental to an understanding of evolution, such as protein sequences. We aimed to showcase sound-art objects and live-coding performances created using sonification as a mechanism for presenting complex biological processes to both researcher and non-researchers. We sought to evaluate the effectiveness of this art-adjacent practice for public engagement with evolutionary biology research, and also to gather feedback to guide future events. Toward this end, we held a live concert showcasing biologically-based algorithmic music exploring links between evolutionary biology research, sound art, and musical performance. The event had three main acts: a generative audio-visual piece giving an artistic representation of SARS coronavirus based on a parameter-mapping sonification of protein sequence of the replicase polyprotein; a pre-recorded string ensemble demonstrating the effects of codon selection on translation speed using parameter-mapping sonification; and a live-coded music piece interactively sonifying protein structures. Results: Our event attracted 90 attendees. We evaluated success using direct observation and written feedback forms with a 58% response rate: 95% of respondents stated they had enjoyed the event and 63% indicated they were inspired by it. Conclusions: Presenting the sonic outputs of sonification research in a concert format showed good potential for the pursuit of public engagement with evolutionary biology research, demonstrating the ability to engage curiosity and inspire an audience while also conveying scientific content alongside the nuanced and complex world of modern evolutionary biology. [ABSTRACT FROM AUTHOR]

From deciding which meal to prepare for our guests to trading off the proenvironmental effects of climate protection measures against their economic costs, we often must consider the consequences of our actions for the well-being of others (welfare). Vexingly, the tastes and views of others can vary widely. To maximize welfare according to the utilitarian philosophical tradition, decision-makers facing conflicting preferences of others should choose the option that maximizes the sum of the subjective value (utility) of the entire group. This notion requires comparing the intensities of preferences across individuals. However, it remains unclear whether such comparisons are possible at all and (if they are possible) how they might be implemented in the brain. Here, we show that female and male participants can both learn the preferences of others by observing their choices and represent these preferences on a common scale to make utilitarian welfare decisions. On the neural level, multivariate support vector regressions revealed that a distributed activity pattern in the ventromedial prefrontal cortex (VMPFC), a brain region previously associated with reward processing, represented the preference strength of others. Strikingly, also the utilitarian welfare of others was represented in the VMPFC and relied on the same neural code as the estimated preferences of others. Together, our findings reveal that humans can behave as if they maximized utilitarian welfare using a specific utility representation and that the brain enables such choices by repurposing neural machinery processing the reward others receive. [ABSTRACT FROM AUTHOR]

The aging process has broad physiological impacts, including a significant decline in sensory function, which threatens both physical health and quality of life. One ideal model to study aging, neuronal function, and gene expression is the nematode Caenorhabditis elegans, which has a short lifespan and relatively simple, thoroughly mapped nervous system and genome. Previous works have identified that mechanosensory neuronal structure changes with age, but importantly, the actual age-related changes in the function and health of neurons, as well as the underlying genetic mechanisms responsible for these declines, are not fully understood. While advanced techniques such as single-cell RNA-sequencing have been developed to quantify gene expression, it is difficult to relate this information to functional changes in aging due to a lack of tools available. To address these limitations, we present a platform capable of measuring both physiological function and its associated gene expression throughout the aging process in individuals. Using our pipeline, we investigate the age-related changes in function of the mechanosensing ALM neuron in C. elegans, as well as some relevant gene expression patterns (mec-4 and mec-10). Using a series of devices for animals of different ages, we examined subtle changes in neuronal function and found that while the magnitude of neuronal response to a large stimulus declines with age, sensory capability does not significantly decline with age; further, gene expression is well maintained throughout aging. Additionally, we examine PVD, a harsh-touch mechanosensory neuron, and find that it exhibits a similar age-related decline in magnitude of neuronal response. Together, our data demonstrate that our strategy is useful for identifying genetic factors involved in the decline in neuronal health. We envision that this framework could be applied to other systems as a useful tool for discovering new biology. [ABSTRACT FROM AUTHOR]

In primates, foveal and peripheral vision have distinct neural architectures and functions. However, it has been debated if selective attention operates via the same or different neural mechanisms across eccentricities. We tested these alternative accounts by examining the effects of selective attention on the steady-state visually evoked potential (SSVEP) and the fronto-parietal signal measured via EEG from human subjects performing a sustained visuospatial attention task. With a negligible level of eye movements, both SSVEP and SND exhibited the heterogeneous patterns of attentional modulations across eccentricities. Specifically, the attentional modulations of these signals peaked at the parafoveal locations and such modulations wore off as visual stimuli appeared closer to the fovea or further away towards the periphery. However, with a relatively higher level of eye movements, the heterogeneous patterns of attentional modulations of these neural signals were less robust. These data demonstrate that the top-down influence of covert visuospatial attention on early sensory processing in human cortex depends on eccentricity and the level of saccadic responses. Taken together, the results suggest that sustained visuospatial attention operates differently across different eccentric locations, providing new understanding of how attention augments sensory representations regardless of where the attended stimulus appears. [ABSTRACT FROM AUTHOR]

The perception of a continuous phantom in a sensory domain in the absence of an external stimulus is explained as a maladaptive compensation of aberrant predictive coding, a proposed unified theory of brain functioning. If this were true, these changes would occur not only in the domain of the phantom percept but in other sensory domains as well. We confirm this hypothesis by using tinnitus (continuous phantom sound) as a model and probe the predictive coding mechanism using the established local–global oddball paradigm in both the auditory and visual domains. We observe that tinnitus patients are sensitive to changes in predictive coding not only in the auditory but also in the visual domain. We report changes in well-established components of event-related EEG such as the mismatch negativity. Furthermore, deviations in stimulus characteristics were correlated with the subjective tinnitus distress. These results provide an empirical confirmation that aberrant perceptions are a symptom of a higher-order systemic disorder transcending the domain of the percept. [ABSTRACT FROM AUTHOR]

Through its fermentative capacities, Saccharomyces cerevisiae was central in the development of civilisation during the Neolithic period, and the yeast remains of importance in industry and biotechnology, giving rise to bona fide domesticated populations. Here, we conduct a population genomic study of domesticated and wild populations of S. cerevisiae. Using coalescent analyses, we report that the effective population size of yeast populations decreased since the divergence with S. paradoxus. We fitted models of distributions of fitness effects to infer the rate of adaptive (ωa) and non‐adaptive (ωna) non‐synonymous substitutions in protein‐coding genes. We report an overall limited contribution of positive selection to S. cerevisiae protein evolution, albeit with higher rates of adaptive evolution in wild compared to domesticated populations. Our analyses revealed the signature of background selection and possibly Hill–Robertson interference, as recombination was found to be negatively correlated with ωna and positively correlated with ωa. However, the effect of recombination on ωa was found to be labile, as it is only apparent after removing the impact of codon usage bias on the synonymous site frequency spectrum and disappears if we control for the correlation with ωna, suggesting that it could be an artefact of the decreasing population size. Furthermore, the rate of adaptive non‐synonymous substitutions is significantly correlated with the residue solvent exposure, a relation that cannot be explained by the population's demography. Together, our results provide a detailed characterisation of adaptive mutations in protein‐coding genes across S. cerevisiae populations. [ABSTRACT FROM AUTHOR]

Essential tremor (ET) is the most prevalent movement disorder, characterized primarily by action tremor, an involuntary rhythmic movement with a specific frequency. However, the neuronal mechanism underlying the coding of tremor frequency remains unexplored. Here, we used in vivo electrophysiology, optogenetics, and simultaneous motion tracking in the Grid2dupE3 mouse model to investigate whether and how neuronal activity in the olivocerebellum determines the frequency of essential tremor. We report that tremor frequency was encoded by the temporal coherence of population neuronal firing within the olivocerebellums of these mice, leading to frequency-dependent cerebellar oscillations and tremors. This mechanism was precise and generalizable, enabling us to use optogenetic stimulation of the deep cerebellar nuclei to induce frequency-specific tremors in wild-type mice or alter tremor frequencies in tremor mice. In patients with ET, we showed that deep brain stimulation of the thalamus suppressed tremor symptoms but did not eliminate cerebellar oscillations measured by electroencephalgraphy, indicating that tremor-related oscillations in the cerebellum do not require the reciprocal interactions with the thalamus. Frequency-disrupting transcranial alternating current stimulation of the cerebellum could suppress tremor amplitudes, confirming the frequency modulatory role of the cerebellum in patients with ET. These findings offer a neurodynamic basis for the frequency-dependent stimulation of the cerebellum to treat essential tremor. Editor's summary: Understanding how essential tremor (ET) is encoded within the cerebello-thalamo-cortical network could help to optimize brain stimulation for ET treatment. Here, Wang et al. show that tremor frequency is encoded by coherent population activity in the cerebellum. This activity occurred spontaneously in a mouse model of essential tremor and could be elicited by optogenetic stimulation to induce tremor in wild-type mice. Cerebellar oscillations in patients with ET occurred independently of thalamic activity and could be modulated by transcranial alternating current stimulation (tACS) to disrupt tremors. These findings indicate that disrupting cerebellar oscillations by noninvasive tACS could be a viable strategy for ET therapy, warranting further validation and optimization. —Daniela Neuhofer [ABSTRACT FROM AUTHOR]

Rapid processing of tactile information is essential to human haptic exploration and dexterous object manipulation. Conventional electronic skins generate frames of tactile signals upon interaction with objects. Unfortunately, they are generally ill-suited for efficient coding of temporal information and rapid feature extraction. In this work, we report a neuromorphic tactile system that uses spike timing, especially the first-spike timing, to code dynamic tactile information about touch and grasp. This strategy enables the system to seamlessly code highly dynamic information with millisecond temporal resolution on par with the biological nervous system, yielding dynamic extraction of tactile features. Upon interaction with objects, the system rapidly classifies them in the initial phase of touch and grasp, thus paving the way to fast tactile feedback desired for neuro-robotics and neuro-prosthetics. [ABSTRACT FROM AUTHOR]

Pheromones are specialized chemical messengers used for inter-individual communication within the same species, playing crucial roles in modulating behaviors and physiological states. The detection mechanisms of these signals at the peripheral organ and their transduction to the brain have been unclear. However, recent identification of pheromone molecules, their corresponding receptors, and advancements in neuroscientific technology have started to elucidate these processes. In mammals, the detection and interpretation of pheromone signals are primarily attributed to the vomeronasal system, which is a specialized olfactory apparatus predominantly dedicated to decoding socio-chemical cues. In this mini-review, we aim to delineate the vomeronasal signal transduction pathway initiated by specific vomeronasal receptor-ligand interactions in mice. First, we catalog the previously identified pheromone ligands and their corresponding receptor pairs, providing a foundational understanding of the specificity inherent in pheromonal communication. Subsequently, we examine the neural circuits involved in processing each pheromone signal. We focus on the anatomical pathways, the sexually dimorphic and physiological state-dependent aspects of signal transduction, and the neural coding strategies underlying behavioral responses to pheromonal cues. These insights provide further critical questions regarding the development of innate circuit formation and plasticity within these circuits. [ABSTRACT FROM AUTHOR]

A challenging goal of neural coding is to characterize the neural representations underlying visual perception. To this end, multi-unit activity (MUA) of macaque visual cortex was recorded in a passive fixation task upon presentation of faces and natural images. We analyzed the relationship between MUA and latent representations of state-of-the-art deep generative models, including the conventional and feature-disentangled representations of generative adversarial networks (GANs) (i.e., z- and w-latents of StyleGAN, respectively) and language-contrastive representations of latent diffusion networks (i.e., CLIP-latents of Stable Diffusion). A mass univariate neural encoding analysis of the latent representations showed that feature-disentangled w representations outperform both z and CLIP representations in explaining neural responses. Further, w-latent features were found to be positioned at the higher end of the complexity gradient which indicates that they capture visual information relevant to high-level neural activity. Subsequently, a multivariate neural decoding analysis of the feature-disentangled representations resulted in state-of-the-art spatiotemporal reconstructions of visual perception. Taken together, our results not only highlight the important role of feature-disentanglement in shaping high-level neural representations underlying visual perception but also serve as an important benchmark for the future of neural coding. Author summary: Neural coding seeks to understand how the brain represents the world by modeling the relationship between stimuli and internal neural representations thereof. This field focuses on predicting brain responses to stimuli (neural encoding) and deciphering information about stimuli from brain activity (neural decoding). Recent advances in generative adversarial networks (GANs; a type of machine learning model) have enabled the creation of photorealistic images. Like the brain, GANs also have internal representations of the images they create, referred to as "latents". More recently, a new type of feature-disentangled "w-latent" of GANs has been developed that more effectively separates different image features (e.g., color; shape; texture). In our study, we presented such GAN-generated pictures to a macaque with cortical implants and found that the underlying w-latents were accurate predictors of high-level brain activity. We then used these w-latents to reconstruct the perceived images with high fidelity. The remarkable similarities between our predictions and the actual targets indicate alignment in how w-latents and neural representations represent the same stimulus, even though GANs have never been optimized on neural data. This implies a general principle of shared encoding of visual phenomena, emphasizing the importance of feature disentanglement in deeper visual areas. [ABSTRACT FROM AUTHOR]

To elucidate the combinatorial architecture of neural codes, the neural ideal , an algebraic object, was introduced. Represented in its canonical form, provides a succinct characterization of the inherent receptive field architecture within the code. The polynomials in are also instrumental in determining the relationships among the neurons' receptive fields. Consequently, the computation of the collection of canonical forms is pivotal. In this paper, based on the study of relations between pseudo-monomials, the authors present a computationally efficient iterative algorithm for the canonical forms of the neural ideal. Additionally, we introduce a new relationship among the neurons' receptive fields, which can be characterized by if-and-only-if statements, relating both to and to a larger ideal of a code . [ABSTRACT FROM AUTHOR]