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Brain recordings collected at different resolutions support distinct signatures of neural coding, leading to scale-dependent theories of brain function. Here, we show that these disparate signatures emerge from a heavy-tailed, multiscale functional organization of neuronal activity observed across calcium-imaging recordings collected from the whole brains of zebrafish and C. elegans as well as from sensory regions in Drosophila, mice, and macaques. Network simulations demonstrate that this conserved hierarchical structure enhances information processing. Finally, we find that this organization is maintained despite significant cross-scale reconfiguration of cellular coordination during behavior. Our findings suggest that this nonlinear organization of neuronal activity is a universal principle conserved for its ability to adaptively link behavior to neural dynamics across multiple spatiotemporal scales while balancing functional resiliency and information processing efficiency., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Although time is a fundamental dimension of life, we do not know how brain areas cooperate to keep track and process time intervals. Notably, analyses of neural activity during learning are rare, mainly because timing tasks usually require training over many days. We investigated how the time encoding evolves when animals learn to time a 1.5 s interval. We designed a novel training protocol where rats go from naive- to proficient-level timing performance within a single session, allowing us to investigate neuronal activity from very early learning stages. We used pharmacological experiments and machine-learning algorithms to evaluate the level of time encoding in the medial prefrontal cortex and the dorsal striatum. Our results show a double dissociation between the medial prefrontal cortex and the dorsal striatum during temporal learning, where the former commits to early learning stages while the latter engages as animals become proficient in the task.

Acquiring new memories is a multistage process. Numerous studies have convincingly demonstrated that initially acquired memories are labile and are stabilized only by later consolidation processes. These multiple phases of memory formation are known to involve modification of both cellular excitability and synaptic connectivity, which in turn change neuronal activity at both the single neuron and ensemble levels. However, the specific mapping between the known phases of memory and the changes in neuronal activity at different organizational levels--the single-neuron, population representations, and ensemblestate dynamics--remains unknown. Here we address this issue in the context of conditioned taste aversion learning by continuously tracking gustatory cortex neuronal taste responses in alert male and female rats during the 24 h following a taste-malaise pairing. We found that the progression of activity changes depends on the neuronal organizational level: whereas the population response changed continuously, the population mean response amplitude and the number of taste-responsive neurons only increased during the acquisition and consolidation phases. In addition, the known quickening of the ensemble-state dynamics associated with the faster rejection of harmful foods appeared only after consolidation. Overall, these results demonstrate how complex dynamics in the different representational levels of cortical activity underlie the formation and stabilization of memory within the cortex. [ABSTRACT FROM AUTHOR]

Cross-individual variability is considered the essence of biology, preventing precise mathematical descriptions of biological motion 1-7 like the physics law of motion. Here we report that the cerebellum shapes motor kinematics by encoding dynamic motor frequencies with remarkable numerical precision and cross-individual uniformity. Using in-vivo electrophysiology and optogenetics in mice, we confirmed that deep cerebellar neurons encoded frequencies via populational tuning of neuronal firing probabilities, creating cerebellar oscillations and motions with matched frequencies. The mechanism was consistently presented in self-generated rhythmic and non-rhythmic motions triggered by a vibrational platform, or skilled tongue movements of licking in all tested mice with cross-individual uniformity. The precision and uniformity allowed us to engineer complex motor kinematics with designed frequencies. We further validated the frequency-coding function of the human cerebellum using cerebellar electroencephalography recordings and alternating-current stimulation during voluntary tapping tasks. Our findings reveal a cerebellar algorithm for motor kinematics with precision and uniformity, the mathematical foundation for brain-computer interface for motor control., Competing Interests: Declaration of interests: All authors declare no competing interests.

Neurons are diverse and can be differentiated by their morphological, electrophysiological, and molecular properties. Current morphology-based classification approaches largely rely on the dendritic tree structure or on the overall axonal projection layout. Here, we use data from public databases of neuronal reconstructions and membrane properties to study the characteristics of the axonal and dendritic trees for interneuron classification. We show that combining signal propagation patterns observed by biophysical simulations of the activity along ramified axonal trees with morphological parameters of the axonal and dendritic trees, significantly improve classification results compared to previous approaches. The classification schemes introduced here can be utilized for robust neuronal classification. Our work paves the way for understanding and utilizing form-function principles in realistic neuronal reconstructions. [ABSTRACT FROM AUTHOR]

Estimating the degree of synchrony or reliability between two or more spike trains is a frequent task in both experimental and computational neuroscience. In recent years, many different methods have been proposed that typically compare the timing of spikes on a certain time scale to be optimized by the analyst. Here, we propose the ISI-distance, a simple complementary approach that extracts information from the interspike intervals by evaluating the ratio of the instantaneous firing rates. The method is parameter free, time scale independent and easy to visualize as illustrated by an application to real neuronal spike trains obtained in vitro from rat slices. In a comparison with existing approaches on spike trains extracted from a simulated Hindemarsh-Rose network, the ISI-distance performs as well as the best time-scale-optimized measure based on spike timing. (c) 2007 Elsevier B.V. All rights reserved.

Inspired by the adaptation phenomenon of neuronal firing, we propose the regularity normalization (RN) as an unsupervised attention mechanism (UAM) which computes the statistical regularity in the implicit space of neural networks under the Minimum Description Length (MDL) principle. Treating the neural network optimization process as a partially observable model selection problem, the regularity normalization constrains the implicit space by a normalization factor, the universal code length. We compute this universal code incrementally across neural network layers and demonstrate the flexibility to include data priors such as top-down attention and other oracle information. Empirically, our approach outperforms existing normalization methods in tackling limited, imbalanced and non-stationary input distribution in image classification, classic control, procedurally-generated reinforcement learning, generative modeling, handwriting generation and question answering tasks with various neural network architectures. Lastly, the unsupervised attention mechanisms is a useful probing tool for neural networks by tracking the dependency and critical learning stages across layers and recurrent time steps of deep networks.

Machine learning and artificial intelligence have strong roots on principles of neural computation. Some examples are the structure of the first perceptron, inspired in the retina, neuroprosthetics based on ganglion cell recordings or Hopfield networks. In addition, machine learning provides a powerful set of tools to analyze neural data, which has already proved its efficacy in so distant fields of research as speech recognition, behavioral states classification, or LFP recordings. However, despite the huge technological advances in neural data reduction of dimensionality, pattern selection, and clustering during the last years, there has not been a proportional development of the analytical tools used for Time–Frequency (T–F) analysis in neuroscience. Bearing this in mind, we introduce the convenience of using non-linear, non-stationary tools, EMD algorithms in particular, for the transformation of the oscillatory neural data (EEG, EMG, spike oscillations…) into the T–F domain prior to its analysis with machine learning tools. We support that to achieve meaningful conclusions, the transformed data we analyze has to be as faithful as possible to the original recording, so that the transformations forced into the data due to restrictions in the T–F computation are not extended to the results of the machine learning analysis. Moreover, bioinspired computation such as brain–machine interface may be enriched from a more precise definition of neuronal coding where non-linearities of the neuronal dynamics are considered.

Machine learning and artificial intelligence have strong roots on principles of neural computation. Some examples are the structure of the first perceptron, inspired in the retina, neuroprosthetics based on ganglion cell recordings or Hopfield networks. In addition, machine learning provides a powerful set of tools to analyze neural data, which has already proved its efficacy in so distant fields of research as speech recognition, behavioral states classification, or LFP recordings. However, despite the huge technological advances in neural data reduction of dimensionality, pattern selection, and clustering during the last years, there has not been a proportional development of the analytical tools used for Time-Frequency (T-F) analysis in neuroscience. Bearing this in mind, we introduce the convenience of using non-linear, non-stationary tools, EMD algorithms in particular, for the transformation of the oscillatory neural data (EEG, EMG, spike oscillations...) into the T-F domain prior to its analysis with machine learning tools. We support that to achieve meaningful conclusions, the transformed data we analyze has to be as faithful as possible to the original recording, so that the transformations forced into the data due to restrictions in the T-F computation are not extended to the results of the machine learning analysis. Moreover, bioinspired computation such as brain-machine interface may be enriched from a more precise definition of neuronal coding where non-linearities of the neuronal dynamics are considered. [ABSTRACT FROM AUTHOR]

Rodents are emerging as increasingly popular models of visual functions. Yet, evidence that rodent visual cortex is capable of advanced visual processing, such as object recognition, is limited. Here we investigate how neurons located along the progression of extrastriate areas that, in the rat brain, run laterally to primary visual cortex, encode object information. We found a progressive functional specialization of neural responses along these areas, with: (1) a sharp reduction of the amount of low-level, energy-related visual information encoded by neuronal firing; and (2) a substantial increase in the ability of both single neurons and neuronal populations to support discrimination of visual objects under identity-preserving transformations (e.g., position and size changes). These findings strongly argue for the existence of a rat object-processing pathway, and point to the rodents as promising models to dissect the neuronal circuitry underlying transformation-tolerant recognition of visual objects.

Like primates, the rat brain areas thought to be involved in visual object recognition are arranged in a hierarchy.

Quantitative features of stimuli may be ordered along a magnitude continuum, or line. Magnitude refers to parameters of different types of stimulus properties. For instance, the frequency of a sound relates to sensory and continuous stimulus properties, whereas the number of items in a set is an abstract and discrete property. In addition, within a stimulus property, magnitudes need to be processed not only in one modality, but across multiple modalities. In the sensory domain, for example, magnitude applies to both to the frequency of auditory sounds and tactile vibrations. Similarly, both the number of visual items and acoustic events constitute numerical quantity, or numerosity. To support goal-directed behavior and executive functions across time, magnitudes need to be held in working memory, the ability to briefly retain and manipulate information in mind. How different types of magnitudes across multiple modalities are represented in working memory by single neurons has only recently been explored in primates. These studies show that neurons in the frontal lobe can encode the same magnitude type across sensory modalities. However, while multimodal sensory magnitude in relative comparison tasks is represented by monotonically increasing or decreasing response functions ("summation code"), multimodal numerical quantity in absolute matching tasks is encoded by neurons tuned to preferred numerosities ("labeled-line code"). These findings indicate that most likely there is not a single type of cross-modal workingmemory code for magnitudes, but rather a flexible code that depends on the stimulus dimension as well as on the task requirements. [ABSTRACT FROM AUTHOR]

Perception of color by humans and other primates is a complex problem, studied by neurophysiology, psychophysiology, psycholinguistics, and even philosophy. Being mostly trichromats, simian primates have three types of opsin proteins, expressed in cone neurons in the eye, which allow for the sensing of color as the physical wavelength of light. Further, in neural networks of the retina, the coding principle changes from three types of sensor proteins to two opponent channels: activity of one type of neuron encode the evolutionarily ancient blue-yellow axis of color stimuli, and another more recent evolutionary channel, encoding the axis of red-green color stimuli. Both color channels are distinctive in neural organization at all levels from the eye to the neocortex, where it is thought that the perception of color (as philosophical qualia) emerges from the activity of some neuron ensembles. Here, using data from neurophysiology as a starting point, we propose a hypothesis on how the perception of color can be encoded in the activity of certain neurons in the neocortex. These conceptual neurons, herein referred to as 'color neurons', code only the hue of the color of visual stimulus, similar to place cells and number neurons, already described in primate brains. A case study with preliminary, but direct, evidence for existing conceptual color neurons in the human brain was published in 2008. We predict that the upcoming studies in non-human primates will be more extensive and provide a more detailed description of conceptual color neurons. [Display omitted] • Literature in the neurophysiology of color perception in primates is systematically reviewed. • Neurophysiology-inspired hypothesis of color coding principles in primates' higher areas of the neocortex is proposed. • Literature review of the related disciplines suggests that the proposed hypothesis is not contradicted by existing psychophysiology or neurolinguistics data. • The conceptual color neurons hypothesis provides theoretical guidance to further experiments. [ABSTRACT FROM AUTHOR]

Neurons fire even in the absence of sensory stimulation or task demands. Numerous theoretical studies have modeled this spontaneous activity as a Poisson process with uncorrelated intervals between successive spikes and a variance in firing rate equal to the mean. Experimental tests of this hypothesis have yielded variable results, though most have concluded that firing is not Poisson. However, these tests say little about the ways firing might deviate from randomness. Nor are they definitive because many different distributions can have equal means and variances. Here, we characterized spontaneous spiking patterns in extracellular recordings from monkey, cat, and mouse cerebral cortex neurons using rate-normalized spike train autocorrelation functions (ACFs) and a logarithmic timescale. If activity was Poisson, this function should be flat. This was almost never the case. Instead, ACFs had diverse shapes, often with characteristic peaks in the 1-700 ms range. Shapes were stable over time, up to the longest recording periods used (51 min). They did not fall into obvious clusters. ACFs were often unaffected by visual stimulation, though some abruptly changed during brain state shifts. These behaviors may have their origin in the intrinsic biophysics and dendritic anatomy of the cells or in the inputs they receive., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Naturalistic signals were created from vibrations made by locusts walking on a Sansevieria plant. Both naturalistic and Gaussian noise signals were used to mechanically stimulate VS-3 slit-sense mechanoreceptor neurons of the spider, Cupiennius salei, with stimulus amplitudes adjusted to give similar firing rates for either stimulus. Intracellular microelectrodes recorded action potentials, receptor potential and receptor current, using current clamp and voltage clamp. Frequency response analysis showed that naturalistic stimulation contained relatively more power at low frequencies, and caused increased neuronal sensitivity to higher frequencies. In contrast, varying the amplitude of Gaussian stimulation did not change neuronal dynamics. Naturalistic stimulation contained less entropy than Gaussian, but signal entropy was higher than stimulus in the resultant receptor current, indicating addition of uncorrelated noise during transduction. The presence of added noise was supported by measuring linear information capacity in the receptor current. Total entropy and information capacity in action potentials produced by either stimulus were much lower than in earlier stages, and limited to the maximum entropy of binary signals. We conclude that the dynamics of action potential encoding in VS-3 neurons are sensitive to the form of stimulation, but entropy and information capacity of action potentials are limited by firing rate.

This study investigated the hypothesis that a simple intensive code, based on mean firing rate, could explain the cortical representation of subjective roughness intensity and its invariance with scanning speed. We examined the sensitivity of neurons in the cutaneous, finger representation of primary somatosensory cortex (S1) to a wide range of textures [1 mm high, raised-dot surfaces; spatial periods (SPs), 1.5-8.5 mm], scanned under the digit tips at different speeds (40-115 mm/s). Since subjective roughness estimates show a monotonic increase over this range and are independent of speed, we predicted that the mean firing rate of a subgroup of S1 neurons would share these properties. Single-unit recordings were made in four alert macaques (areas 3b, 1 and 2). Cells whose discharge rate showed a monotonic increase with SP, independent of speed, were particularly concentrated in area 3b. Area 2 was characterized by a high proportion of cells sensitive to speed, with or without texture sensitivity. Area 1 had intermediate properties. We suggest that area 3b and most likely area 1 play a key role in signaling roughness intensity, and that a mean rate code, signaled by both slowly and rapidly adapting neurons, is present at the level of area 3b. Finally, the substantial proportion of neurons that showed a monotonic change in discharge limited to a small range of SPs (often independent of response saturation) could play a role in discriminating smaller changes in SP. [ABSTRACT FROM AUTHOR]

We would like to thank Dolega and Dewhurst (2015) for a thought-provoking and informed deconstruction of our article, which we take as (qualified) applause from valued members of our audience. In brief, we fully concur with the theatre-free formulation offered by Dolega and Dewhurst and take the opportunity to explain why (and how) we used the Cartesian theatre metaphor. We do this by drawing an analogy between consciousness and evolution. This analogy is used to emphasize the circular causality inherent in the free energy principle (aka active inference). We conclude with a comment on the special forms of active inference that may be associated with self-awareness and how they may be especially informed by dream states. [ABSTRACT FROM AUTHOR]

In this review, I describe and contrast the representation of stimuli in visual cortical areas and in the medial temporal lobe (MTL). While cortex is characterized by a distributed and implicit coding that is optimal for recognition and storage of semantic information, the MTL shows a much sparser and explicit coding of specific concepts that is ideal for episodic memory. I will describe the main characteristics of the coding in the MTL by the so-called concept cells and will then propose a model of the formation and recall of episodic memory based on partially overlapping assemblies. [ABSTRACT FROM AUTHOR]

This perspective emphasizes that the brain-machine interface (BMI) research has the potential to clarify major mysteries of the brain and that such clarification of the mysteries by neuroscience is needed to develop BMIs. I enumerate five principal mysteries. The first is how is information encoded in the brain? This is the fundamental question for understanding what our minds are and is related to the verification of Hebb’s cell assembly theory. The second is how is information distributed in the brain? This is also a reconsideration of the functional localization of the brain. The third is what is the function of the ongoing activity of the brain? This is the problem of how the brain is active during no-task periods and what meaning such spontaneous activity has. The fourth is how does the bodily behavior affect the brain function? This is the problem of brain-body interaction, and obtaining a new body by a BMI leads to a possibility of changes in the owner’s brain. The last is to what extent can the brain induce plasticity? Most BMIs require changes in the brain’s neuronal activity to realize higher performance, and the neuronal operant conditioning inherent in the BMIs further enhances changes in the activity.

Neuronal systems exhibit impressive capabilities in decision making and action coordination by employing the encoded information about both external and internal environments. Despite the tremendous effort of neuroscientists, the exact nature of the neuronal code remains elusive. Various experimental and theoretical techniques have been used to resolve the question in recent decades, with methods of signal estimation and detection theory playing an important part. In this paper we review the particular approach which relies on the concepts of Fisher information and Cramér–Rao bound. These concepts essentially investigate the neuronal coding problem by addressing the theoretical limits on the decoding precision, be it in single neurons or in their populations. Despite the success of this approach in many instances, the underlying mathematical theory is not free of certain restrictive assumptions which might complicate the inference in some cases of interest. We recapitulate the assumptions and examine the practical extent of their validity. [ABSTRACT FROM AUTHOR]

Information theory quantifies the ultimate limits on reliable information transfer by means of the channel capacity. However, the channel capacity is known to be an asymptotic quantity, assuming unlimited metabolic cost and computational power. We investigate a single-compartment Hodgkin–Huxley type neuronal model under the spike-rate coding scheme and address how the metabolic cost and the decoding complexity affects the optimal information transmission. We find that the sub-threshold stimulation regime, although attaining the smallest capacity, allows for the most efficient balance between the information transmission and the metabolic cost. Furthermore, we determine post-synaptic firing rate histograms that are optimal from the information-theoretic point of view, which enables the comparison of our results with experimental data. [ABSTRACT FROM AUTHOR]

We present a new method to assess the information carried by temporal patterns in spike trains. The method first performs a wavelet decomposition of the spike trains, then uses Shannon information to select a subset of coefficients carrying information, and finally assesses timing information in terms of decoding performance: the ability to identify the presented stimuli from spike train patterns. We show that the method allows: 1) a robust assessment of the information carried by spike time patterns even when this is distributed across multiple time scales and time points; 2) an effective denoising of the raster plots that improves the estimate of stimulus tuning of spike trains; and 3)an assessment of the information carried by temporally coordinated spikes across neurons. Using simulated data, we demonstrate that the Wavelet-Information (WI) method performs better and is more robust to spike time-jitter, background noise, and sample size than well-established approaches, such as principal component analysis, direct estimates of information from digitized spike trains, or a metric-based method. Furthermore, when applied to real spike trains from monkey auditory cortex and from rat barrel cortex, the WI method allows extracting larger amounts of spike timing information. Importantly, the fact that the WI method incorporates multiple time scales makes it robust to the choice of partly arbitrary parameters such as temporal resolution, response window length, number of response features considered, and the number of available trials. These results highlight the potential of the proposed method for accurate and objective assessments of how spike timing encodes information. [ABSTRACT FROM AUTHOR]

Astronomers and physicists noticed centuries ago that visual spatial resolution is higher for dark than light stimuli, but the neuronal mechanisms for this perceptual asymmetry remain unknown. Here we demonstrate that the asymmetry is caused by a neuronal nonlinearity in the early visual pathway. We show that neurons driven by darks (OFF neurons) increase their responses roughly linearly with luminance decrements, independent of the background luminance. However, neurons driven by lights (ON neurons) saturate their responses with small increases in luminance and need bright backgrounds to approach the linearity of OFF neurons. We show that, as a consequence of this difference in linearity, receptive fields are larger in ON than OFF thalamic neurons, and cortical neurons are more strongly driven by darks than lights at low spatial frequencies. This ON/OFF asymmetry in linearity could be demonstrated in the visual cortex of cats, monkeys, and humans and in the cat visual thalamus. Furthermore, in the cat visual thalamus, we show that the neuronal nonlinearity is present at the ON receptive field center of ON center neurons and ON receptive field surround of OFF-center neurons, suggesting an origin at the level of the photoreceptor. These results demonstrate a fundamental difference in visual processing between ON and OFF channels and reveal a competitive advantage for OFF neurons over ON neurons at low spatial frequencies, which could be important during cortical development when retinal images are blurred by immature optics in infant eyes. [ABSTRACT FROM AUTHOR]

This paper considers the Cartesian theatre as a metaphor for the virtual reality models that the brain uses to make inferences about the world. This treatment derives from our attempts to understand dreaming and waking consciousness in terms of free energy minimization. The idea here is that the Cartesian theatre is not observed by an internal (homuncular) audience but furnishes a theatre in which fictive narratives and fantasies can be rehearsed and tested against sensory evidence. We suppose the brain is driven by the imperative to infer the causes of its sensory samples; in much the same way as scientists are compelled to test hypotheses about experimental data. This recapitulates Helmholtz's notion of unconscious inference and Gregory treatment of perception as hypothesis testing. However, we take this further and consider the active sampling of the world as the gathering of confirmatory evidence for hypotheses based on our virtual reality. The ensuing picture of consciousness (or active inference) resolves a number of seemingly hard problems in consciousness research and is internally consistent with current thinking in systems neuroscience and theoretical neurobiology. In this formalism, there is a dualism that distinguishes between the (conscious) process of inference and the (material) process that entails inference. This separation is reflected by the distinction between beliefs (probability distributions over hidden world states or res cogitans) and the physical brain states (sufficient statistics or res extensa) that encode them. This formal approach allows us to appeal to simple but fundamental theorems in information theory and statistical thermodynamics that dissolve some of the mysterious aspects of consciousness. [ABSTRACT FROM AUTHOR]

Abstract: Even the simplest behaviors depend on a large number of neurons that are distributed across many brain regions. Because electrical microstimulation can change the activity of localized subsets of neurons, it has provided valuable evidence that specific neurons contribute to particular behaviors. Here we review what has been learned about cortical function from behavioral studies using microstimulation in animals and humans. Experiments that examine how microstimulation affects the perception of stimuli have shown that the effects of microstimulation are usually highly specific and can be related to the stimuli preferred by neurons at the stimulated site. Experiments that ask subjects to detect cortical microstimulation in the absence of other stimuli have provided further insights. Although subjects typically can detect microstimulation of primary sensory or motor cortex, they are generally unable to detect stimulation of most of cortex without extensive practice. With practice, however, stimulation of any part of cortex can become detected. These training effects suggest that some patterns of cortical activity cannot be readily accessed to guide behavior, but that the adult brain retains enough plasticity to learn to process novel patterns of neuronal activity arising anywhere in cortex. [Copyright &y& Elsevier]

Although time is a fundamental dimension of life, we do not know how brain areas cooperate to keep track and process time intervals. Notably, analyses of neural activity during learning are rare, mainly because timing tasks usually require training over many days. We investigated how the time encoding evolves when animals learn to time a 1.5 s interval. We designed a novel training protocol where rats go from naive- to proficient-level timing performance within a single session, allowing us to investigate neuronal activity from very early learning stages. We used pharmacological experiments and machine-learning algorithms to evaluate the level of time encoding in the medial prefrontal cortex and the dorsal striatum. Our results show a double dissociation between the medial prefrontal cortex and the dorsal striatum during temporal learning, where the former commits to early learning stages while the latter engages as animals become proficient in the task., Competing Interests: GT, EF, EV, MC, AC, MB No competing interests declared, (© 2022, Tunes, Fermino de Oliveira et al.)

Abstract: This paper presents a theoretical review of rapid eye movement sleep with a special focus on pontine-geniculate-occipital waves and what they might tell us about the functional anatomy of sleep and consciousness. In particular, we review established ideas about the nature and purpose of sleep in terms of protoconsciousness and free energy minimization. By combining these theoretical perspectives, we discover answers to some fundamental questions about sleep: for example, why is homeothermy suspended during sleep? Why is sleep necessary? Why are we not surprised by our dreams? What is the role of synaptic regression in sleep? The imperatives for sleep that emerge also allow us to speculate about the functional role of PGO waves and make some empirical predictions that can, in principle, be tested using recent advances in the modeling of electrophysiological data. [Copyright &y& Elsevier]

Gamma-band synchronization adjusts the timing of excitatory and inhibitory inputs to a neuron. Neurons in the visual cortex are selective for stimulus orientation because of dynamic interactions between excitatory and inhibitory inputs. We hypothesized that these interactions and hence also orientation selectivity vary during the gamma cycle. We determined for each spike its phase relative to the gamma cycle. As a function of gamma phase, we then determined spike rates and their orientation selectivity. Orientation selectivity was modulated by gamma phase. The firing rate of spiking activity that occurred close to a neuron's mean gamma phase of firing was most orientation selective. This stimulus-selective signal could best be conveyed to postsynaptic neurons if it were not corrupted by noise correlations. Noise correlations between firing rates were modulated by gamma phase such that they were not statistically detectable for the spiking activity occurring close to a neuron's mean gamma phase of firing. Thus, gamma-band synchronization produces spiking activity that carries maximal stimulus selectivity and minimal noise correlation in its firing rate, and at the same time synchronizes this spiking activity for maximal impact on postsynaptic targets. [ABSTRACT FROM AUTHOR]

We present an event tree analysis of studying the dynamics of the Hodgkin-Huxley (HH) neuronal networks. Our study relies on a coarse-grained projection to event trees and to the event chains that comprise these trees by using a statistical collection of spatial-temporal sequences of relevant physiological observables (such as sequences of spiking multiple neurons). This projection can retain information about network dynamics that covers multiple features, swiftly and robustly. We demonstrate that for even small differences in inputs, some dynamical regimes of HH networks contain sufficiently higher order statistics as reflected in event chains within the event tree analysis. Therefore, this analysis is effective in discriminating small differences in inputs. Moreover, we use event trees to analyze the results computed from an efficient library-based numerical method proposed in our previous work, where a pre-computed high resolution data library of typical neuronal trajectories during the interval of an action potential (spike) allows us to avoid resolving the spikes in detail. In this way, we can evolve the HH networks using time steps one order of magnitude larger than the typical time steps used for resolving the trajectories without the library, while achieving comparable statistical accuracy in terms of average firing rate and power spectra of voltage traces. Our numerical simulation results show that the library method is efficient in the sense that the results generated by using this numerical method with much larger time steps contain sufficiently high order statistical structure of firing events that are similar to the ones obtained using a regular HH solver. We use our event tree analysis to demonstrate these statistical similarities. [ABSTRACT FROM AUTHOR]

Abstract: A wide variety of approaches to estimate the degree of synchrony between two or more spike trains have been proposed. One of the most recent methods is the ISI-distance which extracts information from the interspike intervals (ISIs) by evaluating the ratio of the instantaneous firing rates. In contrast to most previously proposed measures it is parameter free and time-scale independent. However, it is not well suited to track changes in synchrony that are based on spike coincidences. Here we propose the SPIKE-distance, a complementary measure which is sensitive to spike coincidences but still shares the fundamental advantages of the ISI-distance. In particular, it is easy to visualize in a time-resolved manner and can be extended to a method that is also applicable to larger sets of spike trains. We show the merit of the SPIKE-distance using both simulated and real data. [ABSTRACT FROM AUTHOR]

Abstract: Measures of multiple spike train synchrony are essential in order to study issues such as spike timing reliability, network synchronization, and neuronal coding. These measures can broadly be divided in multivariate measures and averages over bivariate measures. One of the most recent bivariate approaches, the ISI-distance, employs the ratio of instantaneous interspike intervals (ISIs). In this study we propose two extensions of the ISI-distance, the straightforward averaged bivariate ISI-distance and the multivariate ISI-diversity based on the coefficient of variation. Like the original measure these extensions combine many properties desirable in applications to real data. In particular, they are parameter-free, time scale independent, and easy to visualize in a time-resolved manner, as we illustrate with in vitro recordings from a cortical neuron. Using a simulated network of Hindemarsh–Rose neurons as a controlled configuration we compare the performance of our methods in distinguishing different levels of multi-neuron spike train synchrony to the performance of six other previously published measures. We show and explain why the averaged bivariate measures perform better than the multivariate ones and why the multivariate ISI-diversity is the best performer among the multivariate methods. Finally, in a comparison against standard methods that rely on moving window estimates, we use single-unit monkey data to demonstrate the advantages of the instantaneous nature of our methods. [Copyright &y& Elsevier]

The cerebral cortex presents itself as a distributed dynamical system with the characteristics of a small world network. The neuronal correlates of cognitive and executive processes often appear to consist of the coordinated activity of large assemblies of widely distributed neurons. These features require mechanisms for the selective routing of signals across densely interconnected networks, the flexible and context dependent binding of neuronal groups into functionally coherent assemblies and the task and attention dependent integration of subsystems. In order to implement these mechanisms, it is proposed that neuronal responses should convey two orthogonal messages in parallel. They should indicate (1) the presence of the feature to which they are tuned and (2) with which other neurons (specific target cells or members of a coherent assembly) they are communicating. The first message is encoded in the discharge frequency of the neurons (rate code) and it is proposed that the second message is contained in the precise timing relationships between individual spikes of distributed neurons (temporal code). It is further proposed that these precise timing relations are established either by the timing of external events (stimulus locking) or by internal timing mechanisms. The latter are assumed to consist of an oscillatory modulation of neuronal responses in different frequency bands that cover a broad frequency range from <2 Hz (delta) to >40 Hz (gamma) and ripples. These oscillations limit the communication of cells to short temporal windows whereby the duration of these windows decreases with oscillation frequency. Thus, by varying the phase relationship between oscillating groups, networks of functionally cooperating neurons can be flexibly configurated within hard wired networks. Moreover, by synchronizing the spikes emitted by neuronal populations, the saliency of their responses can be enhanced due to the coincidence sensitivity of receiving neurons in very much the same way as can be achieved by increasing the discharge rate. Experimental evidence will be reviewed in support of the coexistence of rate and temporal codes. Evidence will also be provided that disturbances of temporal coding mechanisms are likely to be one of the pathophysiological mechanisms in schizophrenia. [ABSTRACT FROM AUTHOR]

Adaptive behaviors are guided by motivation and memory. Motivational states specify goals, and memory can inform motivated behavior by providing detailed records of past experiences when goals were obtained. These 2 fundamental processes interact to guide animals to biologically relevant targets, but the neuronal mechanisms that integrate them remain unknown. To investigate these mechanisms, we recorded unit activity from the same population of hippocampal neurons as rats performed identical tasks while either food or water deprived. We compared the influence of motivational state (hunger and thirst), memory demand, and spatial behavior in 2 tasks: hippocampus-dependent contextual memory retrieval and hippocampus-independent random foraging. We found that: (i) hippocampal coding was most strongly influenced by motivational state during contextual memory retrieval, when motivational cues were required to select among remembered, goal-directed actions in the same places; (ii) the same neuronal populations were relatively unaffected by motivational state during random foraging, when hunger and thirst were incidental to behavior, and signals derived from deprivation states thus informed, but did not determine, hippocampal coding; and (iii) "prospective coding" in the contextual retrieval task was not influenced by allocentric spatial trajectory, but rather by the animal's deprivation state and the associated, non-spatial target, suggesting that hippocampal coding includes a wide range of predictive associations. The results show that beyond coding spatiotemporal context, hippocampal representations encode the relationships between internal states, the external environment, and action to provide a mechanism by which motivation and memory are coordinated to guide behavior. [ABSTRACT FROM AUTHOR]

Inspired by the adaptation phenomenon of neuronal firing, we propose the regularity normalization (RN) as an unsupervised attention mechanism (UAM) which computes the statistical regularity in the implicit space of neural networks under the Minimum Description Length (MDL) principle. Treating the neural network optimization process as a partially observable model selection problem, the regularity normalization constrains the implicit space by a normalization factor, the universal code length. We compute this universal code incrementally across neural network layers and demonstrate the flexibility to include data priors such as top-down attention and other oracle information. Empirically, our approach outperforms existing normalization methods in tackling limited, imbalanced and non-stationary input distribution in image classification, classic control, procedurally-generated reinforcement learning, generative modeling, handwriting generation and question answering tasks with various neural network architectures. Lastly, the unsupervised attention mechanisms is a useful probing tool for neural networks by tracking the dependency and critical learning stages across layers and recurrent time steps of deep networks. [ABSTRACT FROM AUTHOR]

The analysis of information coding in neurons requires methods that measure different properties of neuronal signals. In this paper we review the recently proposed measure of randomness and compare it to the coefficient of variation, which is the frequently employed measure of variability of spiking neuronal activity. We focus on the problem of the spontaneous activity of neurons, and we hypothetize that under defined conditions, spontaneous activity is more random than evoked activity. This hypothesis is supported by contrasting variability and randomness obtained from experimental recordings of olfactory receptor neurons in rats. [ABSTRACT FROM AUTHOR]

Animals process information about many stimulus features simultaneously, swiftly (in a few 100 ms), and robustly (even when individual neurons do not themselves respond reliably). When the brain carries, codes, and certainly when it decodes information, it must do so through some coarse-grained projection mechanism. How can a projection retain information about network dynamics that covers multiple features, swiftly and robustly? Here, by a coarse-grained projection to event trees and to the event chains that comprise these trees, we propose a method of characterizing dynamic information of neuronal networks by using a statistical collection of spatial-temporal sequences of relevant physiological observables (such as sequences of spiking multiple neurons). We demonstrate, through idealized point neuron simulations in small networks, that this event tree analysis can reveal, with high reliability, information about multiple stimulus features within short realistic observation times. Then, with a large-scale realistic computational model of V1, we show that coarse-grained event trees contain sufficient information, again over short observation times, for fine discrimination of orientation, with results consistent with recent experimental observation. [ABSTRACT FROM AUTHOR]

The results of a study of the bifurcation diagram of the Hindmarsh–Rose neuron model in a two-dimensional parameter space are reported. This diagram shows the existence and extent of complex bifurcation structures that might be useful to understand the mechanisms used by the neurons to encode information and give rapid responses to stimulus. Moreover, the information contained in this phase diagram provides a background to develop our understanding of the dynamics of interacting neurons. [ABSTRACT FROM AUTHOR]

How single neurons represent information about the magnitude of a stimulus remains controversial. Neurons encoding purely sensory magnitude typically show monotonic response functions ("summation coding"), and summation units are usually implemented in models of numerosity representation. In contrast, cells representing numerical quantity exhibit nonmonotonic tuning functions that peak at their preferred numerosity ("labeled-line code"), but the restricted range of tested quantities in these studies did not permit a definite answer. Here, we analyzed both behavioral and neuronal representations of a broad range of numerosities from 1 to 30 in the prefrontal cortex of monkeys. Numerosity-selective neurons showed a clear and behaviorally relevant labeled-line code for all numerosities. Moreover, both the behavioral and neuronal tuning functions obeyed the Weber-Fechner Law and were best represented on a nonlinearly compressed scale. Our single-cell study is in good agreement with functional imaging data reporting peaked tuning functions in humans, demonstrating neuronal precursors for human number competence in a nonhuman primate. Our findings also emphasize that the manner in which neurons encode and maintain magnitude information may depend on the precise task at hand as well as the type of magnitude to represent and memorize. [ABSTRACT FROM AUTHOR]

Rats whisk to explore their environment and obtain information on object features, and the responses of somatosensory cortical neurones must precisely encode aspects of whisker movements. Using trapezoidal stimuli to deflect whiskers, with a wide range of velocities and amplitudes of whisker protraction, we recorded responses from a relatively homogeneous population of isolated cells and neuronal multiunits within the postero-medial barrel sub-field of somatosensory cortex, and analysed responses in an early post-stimulus-onset window. For 92% of neurones the function relating response strength to velocity was a saturating sigmoid but there were differences between neurones in the slopes and ranges over which responses changed. Responses of other neurones were non-monotonic, with response strength decaying at very high whisker deflection velocities. Generally, barrel cortex neurones were responsive to a much wider range of whisker protraction velocities than hitherto reported, especially to much slower velocities than generally assumed to be the main range of sensitivity. This carries implications for coding of whisker deflection velocity, a parameter that appears to be a significant information-bearing element of natural whisking. The effect of amplitude of deflection upon neural responses was evident in only ∼24% of units and only when the dominant velocity effect had saturated. [ABSTRACT FROM AUTHOR]

The contribution of time to the encoding of information by the nervous system is still controversial. The olfactory system is one of the standard preparations where this issue is empirically investigated. For instance, output neurons of the antennal lobe or the olfactory bulb display odor stimulus induced temporal modulations of their firing rate at a scale of hundreds of milliseconds. The role of these temporal patterns in the encoding of odor stimuli, however, is not yet known. Here, we use optical imaging of the projection neurons of the moth antennal lobe to address this question. First, we present a biophysically derived model that provides an accurate description of the calcium response of projection neurons. On the basis of this model, we subsequently show that the calcium response of the projection neurons displays a stimulus specific temporal structure. Finally, we demonstrate that an encoding scheme that includes this temporal information boosts classification performance by 60% as compared to a purely spatial encoding. Although the putative role of combinatorial spatio-temporal encoding strategies has been the subject of debate, our results for the first time establish quantitatively that such an encoding strategy is used by the insect brain. [ABSTRACT FROM AUTHOR]