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Neurostimulation protocols are increasingly used as therapeutic interventions, including for brain injury. In addition to the direct activation of neurons, these stimulation protocols are also likely to have downstream effects on those neurons' synaptic outputs. It is well known that alterations in the strength of synaptic connections (long-term potentiation, LTP; long-term depression, LTD) are sensitive to the frequency of stimulation used for induction; however, little is known about the contribution of the temporal pattern of stimulation to the downstream synaptic plasticity that may be induced by neurostimulation in the injured brain. We explored interactions of the temporal pattern and frequency of neurostimulation in the normal cerebral cortex and after mild traumatic brain injury (mTBI), to inform therapies to strengthen or weaken neural circuits in injured brains, as well as to better understand the role of these factors in normal brain plasticity. Whole-cell (WC) patch-clamp recordings of evoked postsynaptic potentials in individual neurons, as well as field potential (FP) recordings, were made from layer 2/3 of visual cortex in response to stimulation of layer 4, in acute slices from control (naive), sham operated, and mTBI rats. We compared synaptic plasticity induced by different stimulation protocols, each consisting of a specific frequency (1 Hz, 10 Hz, or 100 Hz), continuity (continuous or discontinuous), and temporal pattern (perfectly regular, slightly irregular, or highly irregular). At the individual neuron level, dramatic differences in plasticity outcome occurred when the highly irregular stimulation protocol was used at 1 Hz or 10 Hz, producing an overall LTD in controls and shams, but a robust overall LTP after mTBI. Consistent with the individual neuron results, the plasticity outcomes for simultaneous FP recordings were similar, indicative of our results generalizing to a larger scale synaptic network than can be sampled by individual WC recordings alone. In addition to the differences in plasticity outcome between control (naive or sham) and injured brains, the dynamics of the changes in synaptic responses that developed during stimulation were predictive of the final plasticity outcome. Our results demonstrate that the temporal pattern of stimulation plays a role in the polarity and magnitude of synaptic plasticity induced in the cerebral cortex while highlighting differences between normal and injured brain responses. Moreover, these results may be useful for optimization of neurostimulation therapies to treat mTBI and other brain disorders, in addition to providing new insights into downstream plasticity signaling mechanisms in the normal brain. [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]

We examine how the structure in neural activity and connectivity is related to the computations a network performs. We distinguish two general types of structure that we term geometry and modularity. Geometry and modularity can be determined both at the level of neural activity or connectivity. We harness these concepts to synthetically review recent modeling works on three classes of computations. One major challenge of neuroscience is identifying structure in seemingly disorganized neural activity. Different types of structure have different computational implications that can help neuroscientists understand the functional role of a particular brain area. Here, we outline a unified approach to characterize structure by inspecting the representational geometry and the modularity properties of the recorded activity and show that a similar approach can also reveal structure in connectivity. We start by setting up a general framework for determining geometry and modularity in activity and connectivity and relating these properties with computations performed by the network. We then use this framework to review the types of structure found in recent studies of model networks performing three classes of computations. [ABSTRACT FROM AUTHOR]

Individuals with autism spectrum disorder (ASD) exhibit a diverse range of behavioral features and genetic backgrounds, but whether different genetic forms of autism involve convergent pathophysiology of brain function is unknown. Here, we analyze evidence for convergent deficits in neural circuit function across multiple transgenic mouse models of ASD. We focus on sensory areas of neocortex, where circuit differences may underlie atypical sensory processing, a central feature of autism. Many distinct circuit-level theories for ASD have been proposed, including increased excitation-inhibition (E-I) ratio and hyperexcitability, hypofunction of parvalbumin (PV) interneuron circuits, impaired homeostatic plasticity, degraded sensory coding, and others. We review these theories and assess the degree of convergence across ASD mouse models for each. Behaviorally, our analysis reveals that innate sensory detection behavior is heightened and sensory discrimination behavior is impaired across many ASD models. Neurophysiologically, PV hypofunction and increased E-I ratio are prevalent but only rarely generate hyperexcitability and excess spiking. Instead, sensory tuning and other aspects of neural coding are commonly degraded and may explain impaired discrimination behavior. Two distinct phenotypic clusters with opposing neural circuit signatures are evident across mouse models. Such clustering could suggest physiological subtypes of autism, which may facilitate the development of tailored therapeutic approaches.

Our memories help us plan for the future. In some cases, we use memories to repeat the choices that led to preferable outcomes in the past. The success of these memory-guided decisions depends on close interactions between the hippocampus and medial prefrontal cortex. In other cases, we need to use our memories to deduce hidden connections between the present and past situations to decide the best choice of action based on the expected outcome. Our recent study investigated neural underpinnings of such inferential decisions by monitoring neural activity in the medial prefrontal cortex and hippocampus in rats. We identified several neural activity patterns indicating awake memory trace reactivation and restructuring of functional connectivity among multiple neurons. We also found that these patterns occurred concurrently with the ongoing hippocampal activity when rats recalled past events but not when they planned new adaptive actions. Here, we discussed how these computational properties might contribute to success in inferential decision-making and propose a working model on how the medial prefrontal cortex changes its interaction with the hippocampus depending on whether it reflects on the past or looks into the future. [ABSTRACT FROM AUTHOR]

As a main structure of the limbic system, the hippocampus plays a critical role in pain perception and chronicity. The ventral hippocampal CA1 (vCA1) is closely associated with negative emotions such as anxiety, stress, and fear, yet how vCA1 neurons encode nociceptive information remains unclear. Using in vivo electrophysiological recording, we characterized vCA1 pyramidal neuron subpopulations that exhibited inhibitory or excitatory responses to plantar stimuli and were implicated in encoding stimuli modalities in naïve rats. Functional heterogeneity of the vCA1 pyramidal neurons was further identified in neuropathic pain conditions: the proportion and magnitude of the inhibitory response neurons paralleled mechanical allodynia and contributed to the confounded encoding of innocuous and noxious stimuli, whereas the excitatory response neurons were still instrumental in the discrimination of stimulus properties. Increased theta power and theta-spike coupling in vCA1 correlated with nociceptive behaviors. Optogenetic inhibition of vCA1 pyramidal neurons induced mechanical allodynia in naïve rats, whereas chemogenetic reversal of the overall suppressed vCA1 activity had analgesic effects in rats with neuropathic pain. These results provide direct evidence for the representations of nociceptive information in vCA1. [ABSTRACT FROM AUTHOR]

We study how stimulus information can be represented in the dynamical signatures of an oscillatory model of neural activity--a model whose activity can be modulated by input akin to signals involved in working memory (WM). We developed a neural field model, tuned near an oscillatory instability, in which the WM-like input can modulate the frequency and amplitude of the oscillation. Our neural field model has a spatial-like domain in which an input that preferentially targets a point--a stimulus feature--on the domain will induce feature-specific activity changes. These feature-specific activity changes affect both the mean rate of spikes and the relative timing of spiking activity to the global field oscillation--the phase of the spiking activity. Fromthese two dynamical signatures, we define both a spike rate code and an oscillatory phase code. We assess the performance of these two codes to discriminate stimulus features using an information-theoretic analysis. We show that global WM input modulations can enhance phase code discrimination while simultaneously reducing rate code discrimination. Moreover, we find that the phase code performance is roughly two orders of magnitude larger than that of the rate code defined for the same model solutions. The results of ourmodel have applications to sensory areas of the brain, to which prefrontal areas send inputs reflecting the content of WM. These WM inputs to sensory areas have been established to induce oscillatory changes similar to ourmodel. Our model results suggest amechanismby whichWMsignals may enhance sensory information represented in oscillatory activity beyond the comparatively weak representations based on the mean rate activity. [ABSTRACT FROM AUTHOR]

Brain signal patterns generated in the central nervous system of brain-computer interface (BCI) users are closely related to BCI paradigms and neural coding. In BCI systems, BCI paradigms and neural coding are critical elements for BCI research. However, so far there have been few references that clearly and systematically elaborated on the definition and design principles of the BCI paradigm as well as the definition and modeling principles of BCI neural coding. Therefore, these contents are expounded and the existing main BCI paradigms and neural coding are introduced in the review. Finally, the challenges and future research directions of BCI paradigm and neural coding were discussed, including user-centered design and evaluation for BCI paradigms and neural coding, revolutionizing the traditional BCI paradigms, breaking through the existing techniques for collecting brain signals and combining BCI technology with advanced AI technology to improve brain signal decoding performance. It is expected that the review will inspire innovative research and development of the BCI paradigm and neural coding. [ABSTRACT FROM AUTHOR]

When a neuron breaks silence, it can emit action potentials in a number of patterns. Some responses are so sudden and intense that electrophysiologists felt the need to single them out, labelling action potentials emitted at a particularly high frequency with a metonym – bursts. Is there more to bursts than a figure of speech? After all, sudden bouts of high‐frequency firing are expected to occur whenever inputs surge. The burst coding hypothesis advances that the neural code has three syllables: silences, spikes and bursts. We review evidence supporting this ternary code in terms of devoted mechanisms for burst generation, synaptic transmission and synaptic plasticity. We also review the learning and attention theories for which such a triad is beneficial. [ABSTRACT FROM AUTHOR]

Spiking neural networks (SNNs) are considered to be biologically plausible and can yield high energy efficiency when implemented on neuromorphic hardware due to their highly sparse asynchronous binary event-driven nature. Recently, surrogate gradient (SG) approaches have enabled SNNs to be trained from scratch with backpropagation (BP) algorithms under a deep learning framework. However, a popular SG approach known as straight-through estimator (STE), which only propagates the same gradient information, does not take into account the activation differences between the membrane potentials and output spikes. To address this issue, we propose surrogate gradient scaling (SGS), which scales up or down the gradient information of the membrane potential according to the sign of the gradient of the spiking neuron output and the difference between the membrane potential and the output of the spiking neuron. This SGS approach can also be applied to unimodal functions that propagate different gradient information from the output spikes to the input membrane potential. In addition, SNNs trained directly from scratch suffer from poor generalization performance, and we introduce Lipschitz regularization (LR), which is incorporated into the loss function. It not only improves the generalization performance of SNNs but also makes them more robust to noise. Extensive experimental results on several popular benchmark datasets (CIFAR10, CIFAR100 and CIFAR10-DVS) show that our approach not only outperforms the SOTA but also has lower inference latency. Remarkably, our SNNs can lead to 34 × , 29 × , and 17 × computation energy savings compared to standard Artificial neural networks (ANNs) on above three datasets. [ABSTRACT FROM AUTHOR]

The biophysical properties of existing optogenetic tools constrain the scale, speed, and fidelity of precise optogenetic control. Here, we use structure-guided mutagenesis to engineer opsins that exhibit very high potency while retaining fast kinetics. These new opsins enable large-scale, temporally and spatially precise control of population neural activity. We extensively benchmark these new opsins against existing optogenetic tools and provide a detailed biophysical characterization of a diverse family of opsins under two-photon illumination. This establishes a resource for matching the optimal opsin to the goals and constraints of patterned optogenetics experiments. Finally, by combining these new opsins with optimized procedures for holographic photostimulation, we demonstrate the simultaneous coactivation of several hundred spatially defined neurons with a single hologram and nearly double that number by temporally interleaving holograms at fast rates. These newly engineered opsins substantially extend the capabilities of patterned illumination optogenetic paradigms for addressing neural circuits and behavior.

A surprising finding of recent studies in mouse is the dominance of widespread movement-related activity throughout the brain, including in early sensory areas. In awake subjects, failing to account for movement risks misattributing movement-related activity to other (e.g., sensory or cognitive) processes. In this article, we (1) review task designs for separating task-related and movement-related activity, (2) review three "case studies" in which not considering movement would have resulted in critically different interpretations of neuronal function, and (3) discuss functional couplings that may prevent us from ever fully isolating sensory, motor, and cognitive-related activity. Our main thesis is that neural signals related to movement are ubiquitous, and therefore ought to be considered first and foremost when attempting to correlate neuronal activity with task-related processes.

We study how stimulus information can be represented in the dynamical signatures of an oscillatory model of neural activity—a model whose activity can be modulated by input akin to signals involved in working memory (WM). We developed a neural field model, tuned near an oscillatory instability, in which the WM-like input can modulate the frequency and amplitude of the oscillation. Our neural field model has a spatial-like domain in which an input that preferentially targets a point—a stimulus feature—on the domain will induce feature-specific activity changes. These feature-specific activity changes affect both the mean rate of spikes and the relative timing of spiking activity to the global field oscillation—the phase of the spiking activity. From these two dynamical signatures, we define both a spike rate code and an oscillatory phase code. We assess the performance of these two codes to discriminate stimulus features using an information-theoretic analysis. We show that global WM input modulations can enhance phase code discrimination while simultaneously reducing rate code discrimination. Moreover, we find that the phase code performance is roughly two orders of magnitude larger than that of the rate code defined for the same model solutions. The results of our model have applications to sensory areas of the brain, to which prefrontal areas send inputs reflecting the content of WM. These WM inputs to sensory areas have been established to induce oscillatory changes similar to our model. Our model results suggest a mechanism by which WM signals may enhance sensory information represented in oscillatory activity beyond the comparatively weak representations based on the mean rate activity.

Although much is known about how single neurons in the hippocampus represent an animal’s position, how circuit interactions contribute to spatial coding is less well understood. Using a novel statistical estimator and theoretical modeling, both developed in the framework of maximum entropy models, we reveal highly structured CA1 cell-cell interactions in male rats during open field exploration. The statistics of these interactions depend on whether the animal is in a familiar or novel environment. In both conditions the circuit interactions optimize the encoding of spatial information, but for regimes that differ in the informativeness of their spatial inputs. This structure facilitates linear decodability, making the information easy to read out by downstream circuits. Overall, our findings suggest that the efficient coding hypothesis is not only applicable to individual neuron properties in the sensory periphery, but also to neural interactions in the central brain. [ABSTRACT FROM AUTHOR]

Fractional calculus and fractional-order calculus are arranged in lineage as regards the mathematical models with complexity-theoretical tenets capable of capturing the subtle molecular dynamics by the integration of power-law convolution kernels into time- and space-related derivatives emerging in equations concerning the Magnetic Resonance Imaging (MRI) phenomena to which the fractional models of diffusion and relaxation are applied. Endowed with an intricate level of complexity and a unique physical and structural scaffolding at molecular and cellular levels with numerous synapses forming elaborate neural networks which entail in-depth probing and computing of patterns and signatures in individual cells and neurons, human brain as a heterogeneous medium is constituted of tissues with cells of different sizes and shapes, distributed across an extra-cellular space. Characterization of the unique brain cells is sought after to unravel the connections between different cells and tissues for accurate, reliable, robust and optimal models and computing. Accordingly, Diffusion Magnetic Resonance Imaging (DMRI), as a noninvasive and experimental imaging technique with clinical and research applications, provides a measure related to the diffusion characteristics of water in biological tissues, particularly in the brain tissues. Compatible with these aspects and beyond the diffusion coefficients' measurement, DMRI technique aims to exceed the spatial resolution of the MRI images and draw inferences from the microstructural properties of the related medium. Thus, novel tools become essential for the description of the biological (organelles, membranes, macromolecules and so on) and neurological (axons, dendrites, neurons and so forth) tissues' complexity. Mathematical model-based computational analyses with multifaceted methods to extract information from the DMRI with SpinDoctor into neuronal dynamics can provide quantitative parametric instruments in order to reflect the tissue properties focusing on the precise link between the tissue microstructure and signals acquired by employing advanced medical imaging technologies. Coalesced with accurate neuron geometry models as well as numerical DMRI simulations, a novel extended and multifaceted predictive mathematical model based on SpinDoctor and Bloch–Torrey partial differential equation (BTPDE) with the Caputo fractional-order derivative (FOD) with three-parameter (α , β , δ) Mittag-Leffler function (MLF) has been proposed and developed in our study by extending for the application on Brain Neuron Spin Unit dataset with the relevant multi-stage application-related steps. The feedforward neural networks (FFNNs) with BFGS Quasi-Newton equation, as one of the artificial neural network (ANN) algorithms, are applied on BTPDE with Caputo fractional-order derivative for the neurons and their algorithmic complexity is computed by building a BTPDE with Caputo FOD Neuron model based on different fractional orders. The fractional-order degree of the proposed and developed model is applied in relation to their corresponding complexity degrees. Consequently, experimentation and observations from the simulation-driven FFNN (with BFGS Quasi-Newton equation) learning scheme applied to the Bloch–Torrey PDE–Caputo FOD with MLF Neuron model (named as FFNN–BTPDE–CFODMLF Neuron model) proposed in this study, are made. Thus, by investigating whether the mathematical models based on the accurate neuron geometry models obtained can be optimized by comparing the errors in order to define the order parameter and identify to what optimal extent the errors are in relation to the prediction results with a particular focus on the neuron model, we have been able to estimate and predict brain microstructure through DMRI, accentuating mathematical and medical contributions based on the exploitation and corroboration of powerful modeling as well as computational capabilities. [ABSTRACT FROM AUTHOR]

In neuromorphic computing, the coding method of spiking neurons serves as the foundation and is crucial for various aspects of network operation. Existing mainstream coding methods, such as rate coding and temporal coding, have different focuses, and each has its own advantages and limitations. This paper proposes a novel coding scheme called activeness coding that integrates the strengths of both rate and temporal coding methods. It encompasses precise timing information of the most recent neuronal spike as well as the historical firing rate information. The results of basic characteristic tests demonstrate that this encoding method accurately expresses input information and exhibits robustness. Furthermore, an unsupervised learning method based on activeness-coding triplet spike-timing dependent plasticity (STDP) is introduced, with the MNIST classification task used as an example to assess the performance of this encoding method in solving cognitive tasks. Test results show an improvement in accuracy of approximately 4.5%. Additionally, activeness coding also exhibits potential advantages in terms of resource conservation. Overall, activeness offers a promising approach for spiking neural network encoding with implications for various applications in the field of neural computation. [ABSTRACT FROM AUTHOR]

Individuals with autism spectrum disorder (ASD) exhibit a diverse range of behavioral features and genetic backgrounds, but whether different genetic forms of autism involve convergent pathophysiology of brain function is unknown. Here, we analyze evidence for convergent deficits in neural circuit function across multiple transgenic mouse models of ASD. We focus on sensory areas of neocortex, where circuit differences may underlie atypical sensory processing, a central feature of autism. Many distinct circuit-level theories for ASD have been proposed, including increased excitation-inhibition (E-I) ratio and hyperexcitability, hypofunction of parvalbumin (PV) interneuron circuits, impaired homeostatic plasticity, degraded sensory coding, and others. We review these theories and assess the degree of convergence across ASD mouse models for each. Behaviorally, our analysis reveals that innate sensory detection behavior is heightened and sensory discrimination behavior is impaired across many ASD models. Neurophysiologically, PV hypofunction and increased E-I ratio are prevalent but only rarely generate hyperexcitability and excess spiking. Instead, sensory tuning and other aspects of neural coding are commonly degraded and may explain impaired discrimination behavior. Two distinct phenotypic clusters with opposing neural circuit signatures are evident across mouse models. Such clustering could suggest physiological subtypes of autism, which may facilitate the development of tailored therapeutic approaches. [ABSTRACT FROM AUTHOR]

Our understanding of the neural basis of somatosensation is based largely on studies of the whisker system of mice and rats and the hands of macaque monkeys. Results across these animal models are often interpreted as providing direct insight into human somatosensation. Work on these systems has proceeded in parallel, capitalizing on the strengths of each model, but has rarely been considered as a whole. This lack of integration promotes a piecemeal understanding of somatosensation. Here, we examine the functions and morphologies of whiskers of mice and rats, the hands of macaque monkeys, and the somatosensory neuraxes of these three species. We then discuss how somatosensory information is encoded in their respective nervous systems, highlighting similarities and differences. We reflect on the limitations of these models of human somatosensation and consider key gaps in our understanding of the neural basis of somatosensation.

Individuals with autism spectrum disorder (ASD) exhibit a diverse range of behavioral features and genetic backgrounds, but whether different genetic forms of autism involve convergent pathophysiology of brain function is unknown. Here, we analyze evidence for convergent deficits in neural circuit function across multiple transgenic mouse models of ASD. We focus on sensory areas of neocortex, where circuit differences may underlie atypical sensory processing, a central feature of autism. Many distinct circuit-level theories for ASD have been proposed, including increased excitation–inhibition (E–I) ratio and hyperexcitability, hypofunction of parvalbumin (PV) interneuron circuits, impaired homeostatic plasticity, degraded sensory coding, and others. We review these theories and assess the degree of convergence across ASD mouse models for each. Behaviorally, our analysis reveals that innate sensory detection behavior is heightened and sensory discrimination behavior is impaired across many ASD models. Neurophysiologically, PV hypofunction and increased E–I ratio are prevalent but only rarely generate hyperexcitability and excess spiking. Instead, sensory tuning and other aspects of neural coding are commonly degraded and may explain impaired discrimination behavior. Two distinct phenotypic clusters with opposing neural circuit signatures are evident across mouse models. Such clustering could suggest physiological subtypes of autism, which may facilitate the development of tailored therapeutic approaches.

The identity of sensory stimuli is encoded in the spatio-temporal patterns of responses of the encoding neural population. For stimuli to be discriminated reliably, differences in population responses must be accurately decoded by downstream networks. Several methods to compare patterns of responses have been used by neurophysiologists to characterize the accuracy of the sensory responses studied. Among the most widely used analyses, we note methods based on Euclidean distances or on spike metric distances. Methods based on artificial neural networks and machine learning that recognize and/or classify specific input patterns have also gained popularity. Here, we first compare these three strategies using datasets from three different model systems: the moth olfactory system, the electrosensory system of gymnotids, and leaky-integrate-and-fire (LIF) model responses. We show that the input-weighting procedure inherent to artificial neural networks allows the efficient extraction of information relevant to stimulus discrimination. To combine the convenience of methods such as spike metric distances but leverage the advantages of weighting the inputs, we propose a measure based on geometric distances where each dimension is weighted proportionally to how informative it is. We show that the result of this Weighted Euclidian Distance (WED) analysis performs as well or better than the artificial neural network we tested and outperforms the more traditional spike distance metrics. We applied information theoretic analysis to LIF responses and compared their encoding accuracy with the discrimination accuracy quantified through this WED analysis. We show a high degree of correlation between discrimination accuracy and information content, and that our weighting procedure allowed the efficient use of information present to perform the discrimination task. We argue that our proposed measure provides the flexibility and ease of use sought by neurophysiologists while providing a more powerful way to extract relevant information than more traditional methods. [ABSTRACT FROM AUTHOR]

Abstract Background Flight and freezing are two vital defensive behaviors that mice display to avoid natural enemies. When they are exposed to innate threats, visual cues are processed and transmitted by the visual system into the emotional nuclei and finally transmitted to the periaqueductal gray (PAG) to induce defensive behaviors. However, how the dorsal PAG (dPAG) encodes the two defensive behaviors is unclear. Methods Multi‐array electrodes were implanted in the dPAG nuclei of C57BL/6 mice. Two kinds of visual stimuli (looming and sweeping) were used to induce defensive behaviors in mice. Neural signals under different defense behaviors were recorded, and the encoding characteristics of the two behaviors were extracted and analyzed from spike firing and frequency oscillations. Finally, synchronization of neural activity during the defense process was analyzed. Results The neural activity between flight and freezing behaviors showed different firing patterns, and the differences in the inter‐spike interval distribution were mainly reflected in the 2–10 ms period. The frequency band activities under both defensive behaviors were concentrated in the theta band; the active frequency of flight was ~8 to 10 Hz, whereas that of freezing behavior was ~6 to 8 Hz. The network connection density under both defense behaviors was significantly higher than the period before and after defensive behavior occurred, indicating that there was a high synchronization of neural activity during the defense process. Conclusions The dPAG nuclei of mice have different coding features between flight and freezing behaviors; during strong looming stimulation, fast neuro‐instinctive decision making is required while encountering weak sweeping stimulation, and computable planning late behavior is predicted in the early stage. The frequency band activities under both defensive behaviors were concentrated in the theta band. There was a high synchronization of neural activity during the defense process, which may be a key factor triggering different defensive behaviors.

Merkel cells (MCs) and associated primary sensory afferents of the whisker follicle-sinus complex, accurately code whisker self-movement, angle, and whisk phase during whisking. However, little is known about their roles played in cortical encoding of whisker movement. To this end, the spiking activity of primary somatosensory barrel cortex (wS1) neurons was measured in response to varying the whisker deflection amplitude and velocity in transgenic mice with previously established reduced mechanoelectrical coupling at MC-associated afferents. Under reduced MC activity, wS1 neurons exhibited increased sensitivity to whisker deflection. This appeared to arise from a lack of variation in response magnitude to varying the whisker deflection amplitude and velocity. This latter effect was further indicated by weaker variation in the temporal profile of the evoked spiking activity when either whisker deflection amplitude or velocity was varied. Nevertheless, under reduced MC activity, wS1 neurons retained the ability to differentiate stimulus features based on the timing of their first post-stimulus spike. Collectively, results from this study suggest that MCs contribute to cortical encoding of both whisker amplitude and velocity, predominantly by tuning wS1 response magnitude, and by patterning the evoked spiking activity, rather than by tuning wS1 response latency.

Dexterous object manipulation depends critically on information about forces normal and tangential to the fingerpads, and also on torque associated with object orientation at grip surfaces. We investigated how torque information is encoded by human tactile afferents in the fingerpads and compared them to 97 afferents recorded in monkeys (n =3; 2 females) in our previous study. Human data included slowly-adapting Type-II (SA-II) afferents, which are absent in the glabrous skin of monkeys. Torques of different magnitudes (3.5-7.5 mNm) were applied in clockwise and anticlockwise directions to a standard central site on the fingerpads of 34 human subjects (19 females). Torques were superimposed on a 2, 3, or 4 N background normal force. Unitary recordings were made from fastadapting Type-I (FA-I, n =39), and slowly-adapting Type-I (SA-I, n= 31) and Type-II (SA-II, n =13) afferents supplying the fingerpads via microelectrodes inserted into the median nerve. All three afferent types encoded torque magnitude and direction, with torque sensitivity being higher with smaller normal forces. SA-I afferent responses to static torque were inferior to dynamic stimuli in humans, while in monkeys the opposite was true. In humans this might be compensated by the addition of sustained SA-II afferent input, and their capacity to increase or decrease firing rates with direction of rotation. We conclude that the discrimination capacity of individual afferents of each type was inferior in humans than monkeys which could be because of differences in fingertip tissue compliance and skin friction. [ABSTRACT FROM AUTHOR]

We describe an integrative model that encodes associations between related concepts in the human hippocampal formation, constituting the skeleton of episodic memories. The model, based on partially overlapping assemblies of "concept cells," contrast markedly with the well‐established notion of pattern separation, which relies on conjunctive, context dependent single neuron responses, instead of the invariant, context independent responses found in the human hippocampus. We argue that the model of partially overlapping assemblies is better suited to cope with memory capacity limitations, that the finding of different types of neurons and functions in this area is due to a flexible and temporary use of the extraordinary machinery of the hippocampus to deal with the task at hand, and that only information that is relevant and frequently revisited will consolidate into long‐term hippocampal representations, using partially overlapping assemblies. Finally, we propose that concept cells are uniquely human and that they may constitute the neuronal underpinnings of cognitive abilities that are much further developed in humans compared to other species. [ABSTRACT FROM AUTHOR]

Cortical neurons receive mixed information from the collective spiking activities of primary sensory neurons in response to a sensory stimulus. A recent study demonstrated an abrupt increase or decrease in stimulus intensity and the stimulus intensity itself can be respectively represented by the synchronous and asynchronous spikes of S1 neurons in rats. This evidence capitalized on the ability of an ensemble of homogeneous neurons to multiplex, a coding strategy that was referred to as synchrony-division multiplexing (SDM). Although neural multiplexing can be conceived by distinct functions of individual neurons in a heterogeneous neural ensemble, the extent to which nearly identical neurons in a homogeneous neural ensemble encode multiple features of a mixed stimulus remains unknown. Here, we present a computational framework to provide a system-level understanding on how an ensemble of homogeneous neurons enable SDM. First, we simulate SDM with an ensemble of homogeneous conductance-based model neurons receiving a mixed stimulus comprising slow and fast features. Using feature-estimation techniques, we show that both features of the stimulus can be inferred from the generated spikes. Second, we utilize linear nonlinear (LNL) cascade models and calculate temporal filters and static nonlinearities of differentially synchronized spikes. We demonstrate that these filters and nonlinearities are distinct for synchronous and asynchronous spikes. Finally, we develop an augmented LNL cascade model as an encoding model for the SDM by combining individual LNLs calculated for each type of spike. The augmented LNL model reveals that a homogeneous neural ensemble model can perform two different functions, namely, temporal- and rate-coding, simultaneously. [ABSTRACT FROM AUTHOR]

• A new neuron model with passive dendrite is reconstructed in this article from two levels and in three forms. • Two types of stimulation are performed on this model, which contain the constant electrode current and the synaptic one. • Four coding ways are proposed to encode the spike train where the first-to-spike-time coding one is shown to be the best. Spike is the basic unit in the neuron communication, and different selections of the stimulation locations on the neuron might cause different spike trains, which infers that the spike trains may determine the information of the stimulation locations. The research on this subject deserves intensive attention, whether by numerical experiments or by electrophysiological ones. In this article, to answer the question of how does the spike train encode the stimulus location, by combining the cable model with the leaky integral firing model, a new neuron model called leaky integral firing model with passive dendrite is reconstructed from two levels (i.e., space and time) and in three forms (i.e., the conceptual model, the circuit model, and the mathematical model). Two types of stimulation are performed on this new model, which contain the constant electrode current and the synaptic one, where the latter is also divided into the excitatory current and the inhibitory one. Four coding ways are employed to encode the spike train, among them, by numerical experiments and some theoretical verification, it is shown that the first-to-spike-time coding method is the best one, which could clearly reflect the information of the stimulus position. To be more specific, the closer the stimulation location is to the axon hillock, the shorter the first-to-spike-time is. The neuron model proposed in this paper and the relating encoding methods for the stimulus location could also be applied to the brain-computer interface or constructing new types of neural networks. [ABSTRACT FROM AUTHOR]

The identity of sensory stimuli is encoded in the spatio-temporal patterns of responses of the encoding neural population. For stimuli to be discriminated reliably, differences in population responses must be accurately decoded by downstream networks. Several methods to compare patterns of responses have been used by neurophysiologists to characterize the accuracy of the sensory responses studied. Among the most widely used analyses, we note methods based on Euclidean distances or on spike metric distances. Methods based on artificial neural networks and machine learning that recognize and/or classify specific input patterns have also gained popularity. Here, we first compare these three strategies using datasets from three different model systems: the moth olfactory system, the electrosensory system of gymnotids, and leaky-integrate-and-fire (LIF) model responses. We show that the input-weighting procedure inherent to artificial neural networks allows the efficient extraction of information relevant to stimulus discrimination. To combine the convenience of methods such as spike metric distances but leverage the advantages of weighting the inputs, we propose a measure based on geometric distances where each dimension is weighted proportionally to how informative it is. We show that the result of this Weighted Euclidian Distance (WED) analysis performs as well or better than the artificial neural network we tested and outperforms the more traditional spike distance metrics. We applied information theoretic analysis to LIF responses and compared their encoding accuracy with the discrimination accuracy quantified through this WED analysis. We show a high degree of correlation between discrimination accuracy and information content, and that our weighting procedure allowed the efficient use of information present to perform the discrimination task. We argue that our proposed measure provides the flexibility and ease of use sought by neurophysiologists while providing a more powerful way to extract relevant information than more traditional methods.

According to the efficient coding hypothesis, sensory neurons are adapted to provide maximal information about the environment, given some biophysical constraints. In early visual areas, stimulus-induced modulations of neural activity (or tunings) are predominantly single-peaked. However, periodic tuning, as exhibited by grid cells, has been linked to a significant increase in decoding performance. Does this imply that the tuning curves in early visual areas are sub-optimal? We argue that the time scale at which neurons encode information is imperative to understand the advantages of single-peaked and periodic tuning curves, respectively. Here, we show that the possibility of catastrophic (large) errors creates a trade-off between decoding time and decoding ability. We investigate how decoding time and stimulus dimensionality affect the optimal shape of tuning curves for removing catastrophic errors. In particular, we focus on the spatial periods of the tuning curves for a class of circular tuning curves. We show an overall trend for minimal decoding time to increase with increasing Fisher information, implying a trade-off between accuracy and speed. This trade-off is reinforced whenever the stimulus dimensionality is high, or there is ongoing activity. Thus, given constraints on processing speed, we present normative arguments for the existence of the single-peaked tuning organization observed in early visual areas.

Listeners with hearing loss often struggle to understand speech in noise, even with a hearing aid. To better understand the auditory processing deficits that underlie this problem, we made large-scale brain recordings from gerbils, a common animal model for human hearing, while presenting a large database of speech and noise sounds. We first used manifold learning to identify the neural subspace in which speech is encoded and found that it is low-dimensional and that the dynamics within it are profoundly distorted by hearing loss. We then trained a deep neural network (DNN) to replicate the neural coding of speech with and without hearing loss and analyzed the underlying network dynamics. We found that hearing loss primarily impacts spectral processing, creating nonlinear distortions in cross-frequency interactions that result in a hypersensitivity to background noise that persists even after amplification with a hearing aid. Our results identify a new focus for efforts to design improved hearing aids and demonstrate the power of DNNs as a tool for the study of central brain structures.

A key challenge for neural systems is to extract relevant information from the environment and make appropriate behavioral responses. The larval zebrafish offers an exciting opportunity for studying these sensing processes and sensory-motor transformations. Prey hunting is an instinctual behavior of zebrafish that requires the brain to extract and combine different attributes of the sensory input and form appropriate motor outputs. Due to its small size and transparency the larval zebrafish brain allows optical recording of whole-brain activity to reveal the neuralmechanisms involved in prey hunting and capture. In this review we discuss howthe larval zebrafish brain processes visual information to identify and locate prey, the neural circuits governing the generation of motor commands in response to prey, how hunting behavior can be modulated by internal states and experience, and some outstanding questions for the field. [ABSTRACT FROM AUTHOR]

Introduction: Information transmission and representation in both natural and artificial networks is dependent on connectivity between units. Biological neurons, in addition, modulate synaptic dynamics and post-synaptic membrane properties, but how these relate to information transmission in a population of neurons is still poorly understood. A recent study investigated local learning rules and showed how a spiking neural network can learn to represent continuous signals. Our study builds on their model to explore how basic membrane properties and synaptic delays affect information transfer. Methods: The system consisted of three input and output units and a hidden layer of 300 excitatory and 75 inhibitory leaky integrate-and-fire (LIF) or adaptive integrate-and-fire (AdEx) units. After optimizing the connectivity to accurately replicate the input patterns in the output units, we transformed the model to more biologically accurate units and included synaptic delay and concurrent action potential generation in distinct neurons. We examined three different parameter regimes which comprised either identical physiological values for both excitatory and inhibitory units (Comrade), more biologically accurate values (Bacon), or the Comrade regime whose output units were optimized for low reconstruction error (HiFi). We evaluated information transmission and classification accuracy of the network with four distinct metrics: coherence, Granger causality, transfer entropy, and reconstruction error. Results: Biophysical parameters showed a major impact on information transfer metrics. The classification was surprisingly robust, surviving very low firing and information rates, whereas information transmission overall and particularly low reconstruction error were more dependent on higher firing rates in LIF units. In AdEx units, the firing rates were lower and less information was transferred, but interestingly the highest information transmission rates were no longer overlapping with the highest firing rates. Discussion: Our findings can be reflected on the predictive coding theory of the cerebral cortex and may suggest information transfer qualities as a phenomenological quality of biological cells. [ABSTRACT FROM AUTHOR]

First order cutaneous neurons allow object recognition, texture discrimination, and sensorimotor feedback. Their function is well-investigated under passive stimulation while their role during active touch or sensorimotor control is understudied. To understand how human perception and sensorimotor controlling strategy depend on cutaneous neural signals under active tactile exploration, the finite element (FE) hand and Izhikevich neural dynamic model were combined to predict the cutaneous neural dynamics and the resulting perception during a discrimination test. Using in-vivo microneurography generated single afferent recordings, 75% of the data was applied for the model optimization and another 25% was used for validation. By using this integrated numerical model, the predicted tactile neural signals of the single afferent fibers agreed well with the microneurography test results, achieving the out-of-sample values of 0.94 and 0.82 for slowly adapting type I (SAI) and fast adapting type I unit (FAI) respectively. Similar discriminating capability with the human subject was achieved based on this computational model. Comparable performance with the published numerical model on predicting the cutaneous neural response under passive stimuli was also presented, ensuring the potential applicability of this multi-level numerical model in studying the human tactile sensing mechanisms during active touch. The predicted population-level 1st order afferent neural signals under active touch suggest that different coding strategies might be applied to the afferent neural signals elicited from different cutaneous neurons simultaneously. [ABSTRACT FROM AUTHOR]

Neurons can code for multiple variables simultaneously and neuroscientists are often interested in classifying neurons based on their receptive field properties. Statistical models provide powerful tools for determining the factors influencing neural spiking activity and classifying individual neurons. However, as neural recording technologies have advanced to produce simultaneous spiking data from massive populations, classical statistical methods often lack the computational efficiency required to handle such data. Machine learning (ML) approaches are known for enabling efficient large scale data analyses; however, they typically require massive training sets with balanced data, along with accurate labels to fit well. Additionally, model assessment and interpretation are often more challenging for ML than for classical statistical methods. To address these challenges, we develop an integrated framework, combining statistical modeling and machine learning approaches to identify the coding properties of neurons from large populations. In order to demonstrate this framework, we apply these methods to data from a population of neurons recorded from rat hippocampus to characterize the distribution of spatial receptive fields in this region.

Franklin R Amthor, Christianne E Strang Department of Psychology, The University of Alabama at Birmingham, Birmingham, AL, 35294, USACorrespondence: Franklin R AmthorDepartment of Psychology, The University of Alabama at Birmingham, 1300 University Blvd, Birmingham, AL, 35294-1170, USATel/Fax +1 205 934-2694Email amthorfr@uab.eduPurpose: Transcranial alternating current stimulation (tACS) is a stimulation protocol used for learning enhancement and mitigation of cognitive dysfunction. Correlated firing has been postulated to be a meta-code that links neuronal spike responses associated with a single entity and may be an important component of high-level cognitive functions. Thus, changes in the covariance firing structure of CNS neurons such as retinal ganglion cells are one potential mechanism by which tACS can exert its effects.Materials and Methods: We used microelectrode arrays to record light-evoked spike responses of 24 retinal ganglion cells in 7 rabbit eyecup preparations and analyzed the covariance between 30 pairs of neighboring retinal ganglion cells before, during, and after 10-minute application of alternating currents of 1 microampere at 10 or 20 Hz.Results: tACS stimulation significantly changed the covariance structure of correlated firing in 60% of simultaneously recorded retinal ganglion cells. Application of tACS in the retinal preparation increased cross-covariance in 26% of cell pairs, an effect usually associated with increased light-evoked ganglion cell firing. tACS associated decreases in cross-covariance occurred in 37% of cell pairs. Increased covariance was more common in response to the first, 10-minute application of tACS in isolated retina preparation. Changes in covariance were rare after repeated stimulation, and more likely to result in decreased covariance.Conclusion: Retinal ganglion cell correlated firing is modulated by 1 microampere tACS currents showing that electrical stimulation can significantly and persistently change the structure of the correlated firing of simultaneously recorded rabbit retinal ganglion cells.Keywords: retina, tACS mechanisms, CNS, in vitro model, neural coding, neuromodulation, correlated firing, cross covariance

Christianne E Strang, Franklin R Amthor Department of Psychology, The University of Alabama at Birmingham, Birmingham, AL, 35294-1170, USACorrespondence: Franklin R AmthorDepartment of Psychology, The University of Alabama at Birmingham, Birmingham, AL, 35294-1170, USA, Tel +1 205 934-2694, Fax +1 205 975-6110, Email amthorfr@uab.eduPurpose: Transcranial alternating current stimulation (tACS) is used as a brain stimulation mechanism to enhance learning, ameliorate some psychiatric disorders, and modify behavior. This study assessed the effects of near threshold tACS-like currents on Off-center and On-Off retinal ganglion cell responsiveness in the rabbit retina eyecup preparation as a model for central nervous system effects.Materials and Methods: We made extracellular recordings in the isolated rabbit eyecup preparation using single electrodes and microelectrode arrays to measure light-evoked spike responses in different classes of Off-center and On-Off retinal ganglion cells before, during, and after brief applications of alternating currents of 1– 2 microamperes, at frequencies of 10, 20, 30, and 40 Hz.Results: tACS application sculpted the light-evoked response profiles without directly driving spiking activity of the 20 Off-center and On-Off ganglion cells we recorded from. During tACS application, Off responses were significantly enhanced for 6 cells and significantly suppressed for 14 cells, but after tACS application, Off responses were significantly enhanced for 7 cells and suppressed for 12 cells. The Off responses of the remaining two cells returned to baseline. On responses were less affected during and after tACS.Conclusion: tACS sculpts Off-center and On-Off retinal ganglion cell responsiveness. The dissimilarity of effects in different cells within the same class and the differential effects on the On and Off components of the light response within the same cell are consistent with the hypothesis that tACS acts at threshold on amacrine cells in the inner plexiform layer.Keywords: retina, tACS mechanisms, CNS, in vitro model, neural coding, neuromodulation

A key challenge for neural systems is to extract relevant information from the environment and make appropriate behavioral responses. The larval zebrafish offers an exciting opportunity for studying these sensing processes and sensory-motor transformations. Prey hunting is an instinctual behavior of zebrafish that requires the brain to extract and combine different attributes of the sensory input and form appropriate motor outputs. Due to its small size and transparency the larval zebrafish brain allows optical recording of whole-brain activity to reveal the neural mechanisms involved in prey hunting and capture. In this review we discuss how the larval zebrafish brain processes visual information to identify and locate prey, the neural circuits governing the generation of motor commands in response to prey, how hunting behavior can be modulated by internal states and experience, and some outstanding questions for the field.

IntroductionInformation transmission and representation in both natural and artificial networks is dependent on connectivity between units. Biological neurons, in addition, modulate synaptic dynamics and post-synaptic membrane properties, but how these relate to information transmission in a population of neurons is still poorly understood. A recent study investigated local learning rules and showed how a spiking neural network can learn to represent continuous signals. Our study builds on their model to explore how basic membrane properties and synaptic delays affect information transfer.MethodsThe system consisted of three input and output units and a hidden layer of 300 excitatory and 75 inhibitory leaky integrate-and-fire (LIF) or adaptive integrate-and-fire (AdEx) units. After optimizing the connectivity to accurately replicate the input patterns in the output units, we transformed the model to more biologically accurate units and included synaptic delay and concurrent action potential generation in distinct neurons. We examined three different parameter regimes which comprised either identical physiological values for both excitatory and inhibitory units (Comrade), more biologically accurate values (Bacon), or the Comrade regime whose output units were optimized for low reconstruction error (HiFi). We evaluated information transmission and classification accuracy of the network with four distinct metrics: coherence, Granger causality, transfer entropy, and reconstruction error.ResultsBiophysical parameters showed a major impact on information transfer metrics. The classification was surprisingly robust, surviving very low firing and information rates, whereas information transmission overall and particularly low reconstruction error were more dependent on higher firing rates in LIF units. In AdEx units, the firing rates were lower and less information was transferred, but interestingly the highest information transmission rates were no longer overlapping with the highest firing rates.DiscussionOur findings can be reflected on the predictive coding theory of the cerebral cortex and may suggest information transfer qualities as a phenomenological quality of biological cells.

Accumulating evidence across multiple sensory modalities suggests that the thalamus does not simply relay information from the periphery to the cortex. Here we review recent findings showing that vestibular neurons within the ventral posteriolateral area of the thalamus perform nonlinear transformations on their afferent input that determine our subjective awareness of motion. Specifically, these neurons provide a substrate for previous psychophysical observations that perceptual discrimination thresholds are much better than predictions from Weber's law. This is because neural discrimination thresholds, which are determined from both variability and sensitivity, initially increase but then saturate with increasing stimulus amplitude, thereby matching the previously observed dependency of perceptual self-motion discrimination thresholds. Moreover, neural response dynamics give rise to unambiguous and optimized encoding of natural but not artificial stimuli. Finally, vestibular thalamic neurons selectively encode passively applied motion when occurring concurrently with voluntary (i.e., active) movements. Taken together, these results show that the vestibular thalamus plays an essential role towards generating motion perception as well as shaping our vestibular sense of agency that is not simply inherited from afferent input.

Tactile objects have both local geometry (shape) and broader macroscopic texture, but how these different spatial scales are simultaneously encoded during active touch is unknown. In the whisker system, we tested for a shared code based on localized whisker micromotions (stick-slips) and slip-evoked spikes. We trained mice to discriminate smooth from rough surfaces, including ridged gratings and sandpaper. Whisker slips locked to ridges and evoked temporally precise spikes (