Beyond Poisson: increased spike-time regularity across primate parietal cortex

Moral considerability of brain organoids from the perspective of computational architecture

Human brain organoids equipped with complex cytoarchitecture and closed-loop feedback from virtual environments could provide insights into neural mechanisms underlying cognition. Yet organoids with certain cognitive capacities might also merit moral consideration. A precautionary approach has been proposed to address these ethical concerns by focusing on the epistemological question of whether organoids possess neural structures for morally-relevant capacities that bear resemblance to those found in human brains. Critics challenge this similarity approach on philosophical, scientific, and practical grounds but do so without a suitable alternative. Here, I introduce an architectural approach that infers the potential for cognitive-like processing in brain organoids based on the pattern of information flow through the system. The kind of computational architecture acquired by an organoid then informs the kind of cognitive capacities that could, theoretically, be supported and empirically investigated. The implications of this approach for the moral considerability of brain organoids are discussed.

Protosequences in human cortical organoids model intrinsic states in the developing cortex

Neuronal firing sequences are thought to be the basic building blocks of neural coding and information broadcasting within the brain. However, when sequences emerge during neurodevelopment remains unknown. We demonstrate that structured firing sequences are present in spontaneous activity of human brain organoids and ex vivo neonatal brain slices from the murine somatosensory cortex. We observed a balance between temporally rigid and flexible firing patterns that are emergent phenomena in human brain organoids and early postnatal murine somatosensory cortex, but not in primary dissociated cortical cultures. Our findings suggest that temporal sequences do not arise in an experience-dependent manner, but are rather constrained by an innate preconfigured architecture established during neurogenesis. These findings highlight the potential for brain organoids to further explore how exogenous inputs can be used to refine neuronal circuits and enable new studies into the genetic mechanisms that govern assembly of functional circuitry during early human brain development.

Assessing cross-contamination in spike-sorted electrophysiology data

Recent advances in extracellular electrophysiology now facilitate the recording of spikes from hundreds or thousands of neurons simultaneously. This has necessitated both the development of new computational methods for spike sorting and better methods to determine spike sorting accuracy. One longstanding method of assessing the false discovery rate (FDR) of spike sorting - the rate at which spikes are misassigned to the wrong cluster - has been the rate of inter-spike-interval (ISI) violations. Despite their near ubiquitous usage in spike sorting, our understanding of how exactly ISI violations relate to FDR, as well as best practices for using ISI violations as a quality metric, remain limited. Here, we describe an analytical solution that can be used to predict FDR from ISI violation rate. We test this model in silico through Monte Carlo simulation, and apply it to publicly available spike-sorted electrophysiology datasets. We find that the relationship between ISI violation rate and FDR is highly nonlinear, with additional dependencies on firing rate, the correlation in activity between neurons, and contaminant neuron count. Predicted median FDRs in public datasets were found to range from 3.1% to 50.0%. We find that stochasticity in the occurrence of ISI violations as well as uncertainty in cluster-specific parameters make it difficult to predict FDR for single clusters with high confidence, but that FDR can be estimated accurately across a population of clusters. Our findings will help the growing community of researchers using extracellular electrophysiology assess spike sorting accuracy in a principled manner.

Cell Replacement Therapy for Brain Repair: Recent Progress and Remaining Challenges for Treating Parkinson's Disease and Cortical Injury

Neural transplantation represents a promising approach to repairing damaged brain circuitry. Cellular grafts have been shown to promote functional recovery through "bystander effects" and other indirect mechanisms. However, extensive brain lesions may require direct neuronal replacement to achieve meaningful restoration of function. While fetal cortical grafts have been shown to integrate with the host brain and appear to develop appropriate functional attributes, the significant ethical concerns and limited availability of this tissue severely hamper clinical translation. Induced pluripotent stem cell-derived cells and tissues represent a more readily scalable alternative. Significant progress has recently been made in developing protocols for generating a wide range of neural cell types in vitro. Here, we discuss recent progress in neural transplantation approaches for two conditions with distinct design needs: Parkinson's disease and cortical injury. We discuss the current status and future application of injections of dopaminergic cells for the treatment of Parkinson's disease as well as the use of structured grafts such as brain organoids for cortical repair.

Sampling-based Bayesian inference in recurrent circuits of stochastic spiking neurons

Two facts about cortex are widely accepted: neuronal responses show large spiking variability with near Poisson statistics and cortical circuits feature abundant recurrent connections between neurons. How these spiking and circuit properties combine to support sensory representation and information processing is not well understood. We build a theoretical framework showing that these two ubiquitous features of cortex combine to produce optimal sampling-based Bayesian inference. Recurrent connections store an internal model of the external world, and Poissonian variability of spike responses drives flexible sampling from the posterior stimulus distributions obtained by combining feedforward and recurrent neuronal inputs. We illustrate how this framework for sampling-based inference can be used by cortex to represent latent multivariate stimuli organized either hierarchically or in parallel. A neural signature of such network sampling are internally generated differential correlations whose amplitude is determined by the prior stored in the circuit, which provides an experimentally testable prediction for our framework.

Descending neurons of the hoverfly respond to pursuits of artificial targets

Many animals use motion vision information to control dynamic behaviors. Predatory animals, for example, show an exquisite ability to detect rapidly moving prey, followed by pursuit and capture. Such target detection is not only used by predators but is also important in conspecific interactions, such as for male hoverflies defending their territories against conspecific intruders. Visual target detection is believed to be subserved by specialized target-tuned neurons found in a range of species, including vertebrates and arthropods. However, how these target-tuned neurons respond to actual pursuit trajectories is currently not well understood. To redress this, we recorded extracellularly from target-selective descending neurons (TSDNs) in male Eristalis tenax hoverflies. We show that they have dorso-frontal receptive fields with a preferred direction up and away from the visual midline. We reconstructed visual flow fields as experienced during pursuits of artificial targets (black beads). We recorded TSDN responses to six reconstructed pursuits and found that each neuron responded consistently at remarkably specific time points but that these time points differed between neurons. We found that the observed spike probability was correlated with the spike probability predicted from each neuron's receptive field and size tuning. Interestingly, however, the overall response rate was low, with individual neurons responding to only a small part of each reconstructed pursuit. In contrast, the TSDN population responded to substantially larger proportions of the pursuits but with lower probability. This large variation between neurons could be useful if different neurons control different parts of the behavioral output.

Logarithmically scaled, gamma distributed neuronal spiking

Naturally log-scaled quantities abound in the nervous system. Distributions of these quantities have non-intuitive properties, which have implications for data analysis and the understanding of neural circuits. Here, we review the log-scaled statistics of neuronal spiking and the relevant analytical probability distributions. Recent work using log-scaling revealed that interspike intervals of forebrain neurons segregate into discrete modes reflecting spiking at different timescales and are each well-approximated by a gamma distribution. Each neuron spends most of the time in an irregular spiking 'ground state' with the longest intervals, which determines the mean firing rate of the neuron. Across the entire neuronal population, firing rates are log-scaled and well approximated by the gamma distribution, with a small number of highly active neurons and an overabundance of low rate neurons (the 'dark matter'). These results are intricately linked to a heterogeneous balanced operating regime, which confers upon neuronal circuits multiple computational advantages and has evolutionarily ancient origins.

The dynamics and geometry of choice in premotor cortex

The brain represents sensory variables in the coordinated activity of neural populations, in which tuning curves of single neurons define the geometry of the population code. Whether the same coding principle holds for dynamic cognitive variables remains unknown because internal cognitive processes unfold with a unique time course on single trials observed only in the irregular spiking of heterogeneous neural populations. Here we show the existence of such a population code for the dynamics of choice formation in the primate premotor cortex. We developed an approach to simultaneously infer population dynamics and tuning functions of single neurons to the population state. Applied to spike data recorded during decision-making, our model revealed that populations of neurons encoded the same dynamic variable predicting choices, and heterogeneous firing rates resulted from the diverse tuning of single neurons to this decision variable. The inferred dynamics indicated an attractor mechanism for decision computation. Our results reveal a common geometric principle for neural encoding of sensory and dynamic cognitive variables.

Why is everyone talking about brain state?

The rapid and coordinated propagation of neural activity across the brain provides the foundation for complex behavior and cognition. Technical advances across neuroscience subfields have advanced understanding of these dynamics, but points of convergence are often obscured by semantic differences, creating silos of subfield-specific findings. In this review we describe how a parsimonious conceptualization of brain state as the fundamental building block of whole-brain activity offers a common framework to relate findings across scales and species. We present examples of the diverse techniques commonly used to study brain states associated with physiology and higher-order cognitive processes, and discuss how integration across them will enable a more comprehensive and mechanistic characterization of the neural dynamics that are crucial to survival but are disrupted in disease.

Spontaneous activity in cortical neurons is stereotyped and non-Poisson

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.

Encoding of environmental illumination by primate melanopsin neurons

Light regulates physiology, mood, and behavior through signals sent to the brain by intrinsically photosensitive retinal ganglion cells (ipRGCs). How primate ipRGCs sense light is unclear, as they are rare and challenging to target for electrophysiological recording. We developed a method of acute identification within the live, ex vivo retina. Using it, we found that ipRGCs of the macaque monkey are highly specialized to encode irradiance (the overall intensity of illumination) by blurring spatial, temporal, and chromatic features of the visual scene. We describe mechanisms at the molecular, cellular, and population scales that support irradiance encoding across orders-of-magnitude changes in light intensity. These mechanisms are conserved quantitatively across the ~70 million years of evolution that separate macaques from mice.

Key role of neuronal diversity in structured reservoir computing

Chaotic time series have been captured by reservoir computing models composed of a recurrent neural network whose output weights are trained in a supervised manner. These models, however, are typically limited to randomly connected networks of homogeneous units. Here, we propose a new class of structured reservoir models that incorporates a diversity of cell types and their known connections. In a first version of the model, the reservoir was composed of mean-rate units separated into pyramidal, parvalbumin, and somatostatin cells. Stability analysis of this model revealed two distinct dynamical regimes, namely, (i) an inhibition-stabilized network (ISN) where strong recurrent excitation is balanced by strong inhibition and (ii) a non-ISN network with weak excitation. These results were extended to a leaky integrate-and-fire model that captured different cell types along with their network architecture. ISN and non-ISN reservoir networks were trained to relay and generate a chaotic Lorenz attractor. Despite their increased performance, ISN networks operate in a regime of activity near the limits of stability where external perturbations yield a rapid divergence in output. The proposed framework of structured reservoir computing opens avenues for exploring how neural microcircuits can balance performance and stability when representing time series through distinct dynamical regimes.

Multiregion neuronal activity: the forest and the trees

The past decade has witnessed remarkable advances in the simultaneous measurement of neuronal activity across many brain regions, enabling fundamentally new explorations of the brain-spanning cellular dynamics that underlie sensation, cognition and action. These recently developed multiregion recording techniques have provided many experimental opportunities, but thoughtful consideration of methodological trade-offs is necessary, especially regarding field of view, temporal acquisition rate and ability to guarantee cellular resolution. When applied in concert with modern optogenetic and computational tools, multiregion recording has already made possible fundamental biological discoveries - in part via the unprecedented ability to perform unbiased neural activity screens for principles of brain function, spanning dozens of brain areas and from local to global scales.

Functional neuronal circuitry and oscillatory dynamics in human brain organoids

Human brain organoids replicate much of the cellular diversity and developmental anatomy of the human brain. However, the physiology of neuronal circuits within organoids remains under-explored. With high-density CMOS microelectrode arrays and shank electrodes, we captured spontaneous extracellular activity from brain organoids derived from human induced pluripotent stem cells. We inferred functional connectivity from spike timing, revealing a large number of weak connections within a skeleton of significantly fewer strong connections. A benzodiazepine increased the uniformity of firing patterns and decreased the relative fraction of weakly connected edges. Our analysis of the local field potential demonstrate that brain organoids contain neuronal assemblies of sufficient size and functional connectivity to co-activate and generate field potentials from their collective transmembrane currents that phase-lock to spiking activity. These results point to the potential of brain organoids for the study of neuropsychiatric diseases, drug action, and the effects of external stimuli upon neuronal networks.

A unified model of the task-evoked pupil response

The pupil dilates and reconstricts following task events. It is popular to model this task-evoked pupil response as a linear transformation of event-locked impulses, whose amplitudes are used as estimates of arousal. We show that this model is incorrect and propose an alternative model based on the physiological finding that a common neural input drives saccades and pupil size. The estimates of arousal from our model agreed with key predictions: Arousal scaled with task difficulty and behavioral performance but was invariant to small differences in trial duration. Moreover, the model offers a unified explanation for a wide range of phenomena: entrainment of pupil size and saccades to task timing, modulation of pupil response amplitude and noise with task difficulty, reaction time-dependent modulation of pupil response timing and amplitude, a constrictory pupil response time-locked to saccades, and task-dependent distortion of this saccade-locked pupil response.

What is the dynamical regime of cerebral cortex?

Many studies have shown that the excitation and inhibition received by cortical neurons remain roughly balanced across many conditions. A key question for understanding the dynamical regime of cortex is the nature of this balancing. Theorists have shown that network dynamics can yield systematic cancellation of most of a neuron's excitatory input by inhibition. We review a wide range of evidence pointing to this cancellation occurring in a regime in which the balance is loose, meaning that the net input remaining after cancellation of excitation and inhibition is comparable in size with the factors that cancel, rather than tight, meaning that the net input is very small relative to the canceling factors. This choice of regime has important implications for cortical functional responses, as we describe: loose balance, but not tight balance, can yield many nonlinear population behaviors seen in sensory cortical neurons, allow the presence of correlated variability, and yield decrease of that variability with increasing external stimulus drive as observed across multiple cortical areas.

State transitions through inhibitory interneurons in a cortical network model

Inhibitory interneurons shape the spiking characteristics and computational properties of cortical networks. Interneuron subtypes can precisely regulate cortical function but the roles of interneuron subtypes for promoting different regimes of cortical activity remains unclear. Therefore, we investigated the impact of fast spiking and non-fast spiking interneuron subtypes on cortical activity using a network model with connectivity and synaptic properties constrained by experimental data. We found that network properties were more sensitive to modulation of the fast spiking population, with reductions of fast spiking excitability generating strong spike correlations and network oscillations. Paradoxically, reduced fast spiking excitability produced a reduction of global excitation-inhibition balance and features of an inhibition stabilised network, in which firing rates were driven by the activity of excitatory neurons within the network. Further analysis revealed that the synaptic interactions and biophysical features associated with fast spiking interneurons, in particular their rapid intrinsic response properties and short synaptic latency, enabled this state transition by enhancing gain within the excitatory population. Therefore, fast spiking interneurons may be uniquely positioned to control the strength of recurrent excitatory connectivity and the transition to an inhibition stabilised regime. Overall, our results suggest that interneuron subtypes can exert selective control over excitatory gain allowing for differential modulation of global network state.

Inhibitory interneurons comprise a significant proportion of all cortical neurons and play a crucial role in sustaining normal neural activity in the brain. Although it is well established that there exist distinct subtypes of interneurons, the impact of different interneuron subtypes upon cortical function remains unclear. In this work, we explore the role of interneuron subtypes for modulating neural activity using a network model containing two of the most common interneuron subtypes. We find that one interneuron subtype, known as fast spiking interneurons, preferentially control the strength of activity between excitatory neurons to regulate changes in network state. These findings suggest that interneuron subtypes may selectively modulate cortical activity to promote different computational capabilities.

Integration, coincidence detection and resonance in networks of spiking neurons expressing Gamma oscillations and asynchronous states

Gamma oscillations are widely seen in the awake and sleeping cerebral cortex, but the exact role of these oscillations is still debated. Here, we used biophysical models to examine how Gamma oscillations may participate to the processing of afferent stimuli. We constructed conductance-based network models of Gamma oscillations, based on different cell types found in cerebral cortex. The models were adjusted to extracellular unit recordings in humans, where Gamma oscillations always coexist with the asynchronous firing mode. We considered three different mechanisms to generate Gamma, first a mechanism based on the interaction between pyramidal neurons and interneurons (PING), second a mechanism in which Gamma is generated by interneuron networks (ING) and third, a mechanism which relies on Gamma oscillations generated by pacemaker chattering neurons (CHING). We find that all three mechanisms generate features consistent with human recordings, but that the ING mechanism is most consistent with the firing rate change inside Gamma bursts seen in the human data. We next evaluated the responsiveness and resonant properties of these networks, contrasting Gamma oscillations with the asynchronous mode. We find that for both slowly-varying stimuli and precisely-timed stimuli, the responsiveness is generally lower during Gamma compared to asynchronous states, while resonant properties are similar around the Gamma band. We could not find conditions where Gamma oscillations were more responsive. We therefore predict that asynchronous states provide the highest responsiveness to external stimuli, while Gamma oscillations tend to overall diminish responsiveness.

In the awake and attentive brain, the activity of neurons is typically asynchronous and irregular. It also occasionally displays oscillations in the Gamma frequency range (30–90 Hz), which are believed to be involved in information processing. Here, we use computational models to investigate how brain circuits generate oscillations in a manner consistent with microelectrode recordings in humans. We then study how these networks respond to external input, comparing asynchronous and oscillatory states. This is tested according to several paradigms, an integrative mode, where slowly varying inputs are progressively integrated, a coincidence detection mode, where brief inputs are processed according to the phase of the oscillations, and a resonance mode where the network is probed with oscillatory inputs. Surprisingly, we find that in all cases, the presence of Gamma oscillations tends to diminish the responsiveness to external inputs. We discuss possible implications of this responsiveness decrease on information processing and propose new directions for further exploration.

Structure and function of the gap junctional network of photoreceptive ganglion cells

Intrinsically photosensitive retinal ganglion cells (ipRGCs) signal not only anterogradely to drive behavioral responses, but also retrogradely to some amacrine interneurons to modulate retinal physiology. We previously found that all displaced amacrine cells with spiking, tonic excitatory photoresponses receive gap-junction input from ipRGCs, but the connectivity patterns and functional roles of ipRGC-amacrine coupling remained largely unknown. Here, we injected PoPro1 fluorescent tracer into all six types of mouse ipRGCs to identify coupled amacrine cells, and analyzed the latter's morphological and electrophysiological properties. We also examined how genetically disrupting ipRGC-amacrine coupling affected ipRGC photoresponses. Results showed that ipRGCs couple with not just ON- and ON/OFF-stratified amacrine cells in the ganglion-cell layer as previously reported, but also OFF-stratified amacrine cells in both ganglion-cell and inner nuclear layers. M1- and M3-type ipRGCs couple mainly with ON/OFF-stratified amacrine cells, whereas the other ipRGC types couple almost exclusively with ON-stratified ones. ipRGCs transmit melanopsin-based light responses to at least 93% of the coupled amacrine cells. Some of the ON-stratifying ipRGC-coupled amacrine cells exhibit transient hyperpolarizing light responses. We detected bidirectional electrical transmission between an ipRGC and a coupled amacrine cell, although transmission was asymmetric for this particular cell pair, favoring the ipRGC-to-amacrine direction. We also observed electrical transmission between two amacrine cells coupled to the same ipRGC. In both scenarios of coupling, the coupled cells often spiked synchronously. While ipRGC-amacrine coupling somewhat reduces the peak firing rates of ipRGCs' intrinsic melanopsin-based photoresponses, it renders these responses more sustained and longer-lasting. In summary, ipRGCs' gap junctional network involves more amacrine cell types and plays more roles than previously appreciated.

Chronic recordings from the marmoset motor cortex reveals modulation of neural firing and local field potentials overlap with macaques

Objective.The common marmoset has been increasingly used in neural interfacing studies due to its smaller size, easier handling, and faster breeding compared to Old World non-human primate (NHP) species. While assessment of cortical anatomy in marmosets has shown strikingly similar layout to macaques, comprehensive assessment of electrophysiological properties underlying forelimb reaching movements in this bridge species does not exist. The objective of this study is to characterize electrophysiological properties of signals recorded from the marmoset primary motor cortex (M1) during a reach task and compare with larger NHP models such that this smaller NHP model can be used in behavioral neural interfacing studies.Approach and main results.Neuronal firing rates and local field potentials (LFPs) were chronically recorded from M1 in three adult, male marmosets. Firing rates, mu + beta and high gamma frequency bands of LFPs were evaluated for modulation with respect to movement. Firing rate and regularity of neurons of the marmoset M1 were similar to that reported in macaques with a subset of neurons showing selectivity to movement direction. Movement phases (rest vs move) was classified from both neural spiking and LFPs. Microelectrode arrays provide the ability to sample small regions of the motor cortex to drive brain-machine interfaces (BMIs). The results demonstrate that marmosets are a robust bridge species for behavioral neuroscience studies with motor cortical electrophysiological signals recorded from microelectrode arrays that are similar to Old World NHPs.Significance. As marmosets represent an interesting step between rodent and macaque models, successful demonstration that neuron modulation in marmoset motor cortex is analogous to reports in macaques illustrates the utility of marmosets as a viable species for BMI studies.

Low-dimensional firing-rate dynamics for populations of renewal-type spiking neurons

The macroscopic dynamics of large populations of neurons can be mathematically analyzed using low-dimensional firing-rate or neural-mass models. However, these models fail to capture spike synchronization effects and nonstationary responses of the population activity to rapidly changing stimuli. Here we derive low-dimensional firing-rate models for homogeneous populations of neurons modeled as time-dependent renewal processes. The class of renewal neurons includes integrate-and-fire models driven by white noise and has been frequently used to model neuronal refractoriness and spike synchronization dynamics. The derivation is based on an eigenmode expansion of the associated refractory density equation, which generalizes previous spectral methods for Fokker-Planck equations to arbitrary renewal models. We find a simple relation between the eigenvalues characterizing the timescales of the firing rate dynamics and the Laplace transform of the interspike interval density, for which explicit expressions are available for many renewal models. Retaining only the first eigenmode already yields a reliable low-dimensional approximation of the firing-rate dynamics that captures spike synchronization effects and fast transient dynamics at stimulus onset. We explicitly demonstrate the validity of our model for a large homogeneous population of Poisson neurons with absolute refractoriness and other renewal models that admit an explicit analytical calculation of the eigenvalues. The eigenmode expansion presented here provides a systematic framework for alternative firing-rate models in computational neuroscience based on spiking neuron dynamics with refractoriness.

Revisiting Persistent Neuronal Activity During Covert Spatial Attention

Persistent activity has been observed in the prefrontal cortex (PFC), in particular during the delay periods of visual attention tasks. Classical approaches based on the average activity over multiple trials have revealed that such an activity encodes the information about the attentional instruction provided in such tasks. However, single-trial approaches have shown that activity in this area is rather sparse than persistent and highly heterogeneous not only within the trials but also between the different trials. Thus, this observation raised the question of how persistent the actually persistent attention-related prefrontal activity is and how it contributes to spatial attention. In this paper, we review recent evidence of precisely deconstructing the persistence of the neural activity in the PFC in the context of attention orienting. The inclusion of machine-learning methods for decoding the information reveals that attention orienting is a highly dynamic process, possessing intrinsic oscillatory dynamics working at multiple timescales spanning from milliseconds to minutes. Dimensionality reduction methods further show that this persistent activity dynamically incorporates multiple sources of information. This novel framework reflects a high complexity in the neural representation of the attention-related information in the PFC, and how its computational organization predicts behavior.

From Univariate to Multivariate Coupling Between Continuous Signals and Point Processes: A Mathematical Framework

Time series data sets often contain heterogeneous signals, composed of both continuously changing quantities and discretely occurring events. The coupling between these measurements may provide insights into key underlying mechanisms of the systems under study. To better extract this information, we investigate the asymptotic statistical properties of coupling measures between continuous signals and point processes. We first introduce martingale stochastic integration theory as a mathematical model for a family of statistical quantities that include the phase locking value, a classical coupling measure to characterize complex dynamics. Based on the martingale central limit theorem, we can then derive the asymptotic gaussian distribution of estimates of such coupling measure that can be exploited for statistical testing. Second, based on multivariate extensions of this result and random matrix theory, we establish a principled way to analyze the low-rank coupling between a large number of point processes and continuous signals. For a null hypothesis of no coupling, we establish sufficient conditions for the empirical distribution of squared singular values of the matrix to converge, as the number of measured signals increases, to the well-known Marchenko-Pastur (MP) law, and the largest squared singular value converges to the upper end of the MP support. This justifies a simple thresholding approach to assess the significance of multivariate coupling. Finally, we illustrate with simulations the relevance of our univariate and multivariate results in the context of neural time series, addressing how to reliably quantify the interplay between multichannel local field potential signals and the spiking activity of a large population of neurons.

Applying Spike-density component analysis for high-accuracy auditory event-related potentials in children

OBJECTIVE

Overlapping neurophysiological signals are the main obstacle preventing from using cortical auditory event-related potentials (AEPs) in clinical settings. Children AEPs are particularly affected by this problem, as their cerebral cortex is still maturing. To overcome this problem, we applied a new version of Spike-density Component Analysis (SCA), an analysis method recently developed, to isolate with high accuracy the neural components of auditory responses of 8-year-old children.

METHODS

Electroencephalography was used with 33 children to record AEPs to auditory stimuli varying in spectrotemporal features. Three different analysis approaches were adopted: the standard AEP analysis procedure, SCA with template-match (SCA-TM), and SCA with half-split average consistency (SCA-HSAC).

RESULTS

SCA-HSAC most successfully allowed the extraction of AEPs for each child, revealing that the most consistent components were P1 and N2. An immature N1 component was also detected.

CONCLUSION

Superior accuracy in isolating neural components at the individual level was demonstrated for SCA-HSAC over other SCA approaches even for children AEPs.

SIGNIFICANCE

Reliable methods of extraction of neurophysiological signals at the individual level are crucial for the application of cortical AEPs for routine diagnostic exams in clinical settings both in children and adults.

Genetic vulnerability of exposures to antenatal maternal treatments in 1- to 2-month-old infants

The growth and maturation of the nervous system are vulnerable during pregnancy. The impact of antenatal exposures to maternal treatments, in the context of genetic vulnerability of the fetus, on sensorimotor functioning in early infancy remains unexplored. Statistical features of head movements obtained from resting-state sleep fMRI scans are examined in 1- to 2-month-old infants, both those at high risk (HR) for autism spectrum disorder (ASD) due to a biological sibling with ASD and at low risk (LR) (N = 56). In utero exposures include maternal prescription medications (psychotropic Rx: N = 3_HR ; N = 5_LR vs. non-psychotropic Rx: N = 11_HR ; N = 9_LR vs. none: N = 11_HR ; N = 16_LR ), psychiatric diagnoses (two or more Dx₂ : N = 5_HR ; N = 1_LR ; one Dx₁ : N = 4_HR ; N = 5_LR ; no Dx: N = 12_HR ; N = 19_LR ), infections requiring antibiotics (infection: N = 5_HR ; N = 8_LR ; no infection: N = 20_HR ; N = 22_LR ), or high fever (fever: N = 2_HR ; N = 2_LR ; no fever: N = 23_HR ; N = 27_LR ). Movements with significantly higher variability are detected in infants exposed to psychotropics (e.g., opioid analgesics) and those whose mothers had fever, and this effect is significantly worse for infants at HR for ASD. Movements are significantly less variable in HR infants with non-psychotropic exposures (e.g., antibiotics). Heightened number of psychiatric or mental health conditions is associated with noisier movements in both risk groups. Genetic vulnerability due to in utero exposure to maternal treatments is an important future approach to be advanced in the field of early mind and brain development.

Population codes of prior knowledge learned through environmental regularities

How the brain makes correct inferences about its environment based on noisy and ambiguous observations is one of the fundamental questions in Neuroscience. Prior knowledge about the probability with which certain events occur in the environment plays an important role in this process. Humans are able to incorporate such prior knowledge in an efficient, Bayes optimal, way in many situations, but it remains an open question how the brain acquires and represents this prior knowledge. The long time spans over which prior knowledge is acquired make it a challenging question to investigate experimentally. In order to guide future experiments with clear empirical predictions, we used a neural network model to learn two commonly used tasks in the experimental literature (i.e. orientation classification and orientation estimation) where the prior probability of observing a certain stimulus is manipulated. We show that a population of neurons learns to correctly represent and incorporate prior knowledge, by only receiving feedback about the accuracy of their inference from trial-to-trial and without any probabilistic feedback. We identify different factors that can influence the neural responses to unexpected or expected stimuli, and find a novel mechanism that changes the activation threshold of neurons, depending on the prior probability of the encoded stimulus. In a task where estimating the exact stimulus value is important, more likely stimuli also led to denser tuning curve distributions and narrower tuning curves, allocating computational resources such that information processing is enhanced for more likely stimuli. These results can explain several different experimental findings, clarify why some contradicting observations concerning the neural responses to expected versus unexpected stimuli have been reported and pose some clear and testable predictions about the neural representation of prior knowledge that can guide future experiments.

Enhanced Signal Detection by Adaptive Decorrelation of Interspike Intervals

Spike trains with negative interspike interval (ISI) correlations, in which long/short ISIs are more likely followed by short/long ISIs, are common in many neurons. They can be described by stochastic models with a spike-triggered adaptation variable. We analyze a phenomenon in these models where such statistically dependent ISI sequences arise in tandem with quasi-statistically independent and identically distributed (quasi-IID) adaptation variable sequences. The sequences of adaptation states and resulting ISIs are linked by a nonlinear decorrelating transformation. We establish general conditions on a family of stochastic spiking models that guarantee this quasi-IID property and establish bounds on the resulting baseline ISI correlations. Inputs that elicit weak firing rate changes in samples with many spikes are known to be more detectible when negative ISI correlations are present because they reduce spike count variance; this defines a variance-reduced firing rate coding benchmark. We performed a Fisher information analysis on these adapting models exhibiting ISI correlations to show that a spike pattern code based on the quasi-IID property achieves the upper bound of detection performance, surpassing rate codes with the same mean rate-including the variance-reduced rate code benchmark-by 20% to 30%. The information loss in rate codes arises because the benefits of reduced spike count variance cannot compensate for the lower firing rate gain due to adaptation. Since adaptation states have similar dynamics to synaptic responses, the quasi-IID decorrelation transformation of the spike train is plausibly implemented by downstream neurons through matched postsynaptic kinetics. This provides an explanation for observed coding performance in sensory systems that cannot be accounted for by rate coding, for example, at the detection threshold where rate changes can be insignificant.

Multicoding in neural information transfer suggested by mathematical analysis of the frequency-dependent synaptic plasticity in vivo

Two elements of neural information processing have primarily been proposed: firing rate and spike timing of neurons. In the case of synaptic plasticity, although spike-timing-dependent plasticity (STDP) depending on presynaptic and postsynaptic spike times had been considered the most common rule, recent studies have shown the inhibitory nature of the brain in vivo for precise spike timing, which is key to the STDP. Thus, the importance of the firing frequency in synaptic plasticity in vivo has been recognized again. However, little is understood about how the frequency-dependent synaptic plasticity (FDP) is regulated in vivo. Here, we focused on the presynaptic input pattern, the intracellular calcium decay time constants, and the background synaptic activity, which vary depending on neuron types and the anatomical and physiological environment in the brain. By analyzing a calcium-based model, we found that the synaptic weight differs depending on these factors characteristic in vivo, even if neurons receive the same input rate. This finding suggests the involvement of multifaceted factors other than input frequency in FDP and even neural coding in vivo.

Assessing within-trial and across-trial neural variability in macaque frontal eye fields and their relation to behaviour

The conventional approach to understanding neural responses underlying complex computations is to study across-trial averages of repeatedly performed computations from single neurons. When neurons perform complex computations, such as processing stimulus-related information or movement planning, it has been repeatedly shown, through measures such as the Fano factor (FF), that neural variability across trials decreases. However, multiple neurons contribute to a common computation on a single trial, rather than a single neuron contributing to a computation across multiple trials. Therefore, at the level of a single trial, the concept of FF loses significance. Here, using a combination of simulations and empirical data, we show that changes in the spiking regularity on single trials produce changes in FF. Further, at the behavioural level, the reaction time of the animal was faster when the neural spiking regularity both within and across trials was lower. Taken together, our results provide further constraints on how changes in spiking statistics help neurons optimally encode visual and saccade-related information across multiple timescales and its implication on behaviour.

Distinct Structure of Cortical Population Activity on Fast and Infraslow Timescales

Cortical activity is organized across multiple spatial and temporal scales. Most research on the dynamics of neuronal spiking is concerned with timescales of 1 ms–1 s, and little is known about spiking dynamics on timescales of tens of seconds and minutes. Here, we used frequency domain analyses to study the structure of individual neurons’ spiking activity and its coupling to local population rate and to arousal level across 0.01–100 Hz frequency range. In mouse medial prefrontal cortex, the spiking dynamics of individual neurons could be quantitatively captured by a combination of interspike interval and firing rate power spectrum distributions. The relative strength of coherence with local population often differed across timescales: a neuron strongly coupled to population rate on fast timescales could be weakly coupled on slow timescales, and vice versa. On slow but not fast timescales, a substantial proportion of neurons showed firing anticorrelated with the population. Infraslow firing rate changes were largely determined by arousal rather than by local factors, which could explain the timescale dependence of individual neurons’ population coupling strength. These observations demonstrate how neurons simultaneously partake in fast local dynamics, and slow brain-wide dynamics, extending our understanding of infraslow cortical activity beyond the mesoscale resolution of fMRI.

A Simulation Model of Neural Activity During Hand Reaching Movement

Introduction

The neural response is a noisy random process. The neural response to a sensory stimulus is completely equivalent to a list of spike times in the spike train. In previous studies, decreased neuronal response variability was observed in the cortex's various areas during motor preparatory in reaching tasks. The reasons for the reduction in Neural Variability (NV) are unclear. It could be influenced by an increased firing rate, or it could result from the intrinsic characteristic of cells during the Reaction Time (RT).

Methods

A neural response function with an underlying deterministic instantaneous firing rate signal and a random Poisson process spike generator was simulated in this research. Neural stimulation could help us understand the relationships between the complex data structures of cortical activities and their stability in detail during motor intention in arm-reaching tasks.

Results

Our measurements indicated a similar pattern of results to the cortex, a sharp reduction of the normalized variance of simulated spike trains across all trials. We also observed a reverse relationship between activity and normalized variance.

Conclusion

The present study findings could be applied to neural engineering and brain-machine interfaces for controlling external devices, like the movement of a robot arm.

Dynamic Tracking Algorithm for Time-Varying Neuronal Network Connectivity using Wide-Field Optical Image Video Sequences

Propagation of signals between neurons and brain regions provides information about the functional properties of neural networks, and thus information transfer. Advances in optical imaging and statistical analyses of acquired optical signals have yielded various metrics for inferring neural connectivity, and hence for mapping signal intercorrelation. However, a single coefficient is traditionally derived to classify the connection strength between two cells, ignoring the fact that neural systems are inherently time-variant systems. To overcome these limitations, we utilized a time-varying Pearson's correlation coefficient, spike-sorting, wavelet transform, and wavelet coherence of calcium transients from DIV 12-15 hippocampal neurons from GCaMP6s mice after applying various concentrations of glutamate. Results provide a comprehensive overview of resulting firing patterns, network connectivity, signal directionality, and network properties. Together, these metrics provide a more comprehensive and robust method of analyzing transient neural signals, and enable future investigations for tracking the effects of different stimuli on network properties.

Thalamus Modulates Consciousness via Layer-Specific Control of Cortex

Functional MRI and electrophysiology studies suggest that consciousness depends on large-scale thalamocortical and corticocortical interactions. However, it is unclear how neurons in different cortical layers and circuits contribute. We simultaneously recorded from central lateral thalamus (CL) and across layers of the frontoparietal cortex in awake, sleeping, and anesthetized macaques. We found that neurons in thalamus and deep cortical layers are most sensitive to changes in consciousness level, consistent across different anesthetic agents and sleep. Deep-layer activity is sustained by interactions with CL. Consciousness also depends on deep-layer neurons providing feedback to superficial layers (not to deep layers), suggesting that long-range feedback and intracolumnar signaling are important. To show causality, we stimulated CL in anesthetized macaques and effectively restored arousal and wake-like neural processing. This effect was location and frequency specific. Our findings suggest layer-specific thalamocortical correlates of consciousness and inform how targeted deep brain stimulation can alleviate disorders of consciousness.

Portraits of communication in neuronal networks

The brain is organized as a network of highly specialized networks of spiking neurons. To exploit such a modular architecture for computation, the brain has to be able to regulate the flow of spiking activity between these specialized networks. In this Opinion article, we review various prominent mechanisms that may underlie communication between neuronal networks. We show that communication between neuronal networks can be understood as trajectories in a two-dimensional state space, spanned by the properties of the input. Thus, we propose a common framework to understand neuronal communication mediated by seemingly different mechanisms. We also suggest that the nesting of slow (for example, alpha-band and theta-band) oscillations and fast (gamma-band) oscillations can serve as an important control mechanism that allows or prevents spiking signals to be routed between specific networks. We argue that slow oscillations can modulate the time required to establish network resonance or entrainment and, thereby, regulate communication between neuronal networks.

Intermittent Failure of Spike Propagation in Primary Afferent Neurons during Tactile Stimulation

Primary afferent neurons convey somatosensory information to the CNS. Low-threshold mechanoreceptors are classified as slow-adapting (SA) or rapid-adapting (RA) based on whether or not they spike repetitively during sustained tactile stimulation; the former are subclassified as Type 1 or 2 based on the regularity of their spiking. Recording in vivo from DRGs of mice, we observed irregular- and regular-spiking units consistent with SA1 and SA2 low-threshold mechanoreceptors, but some units, which we labeled "semiregular," did not fit cleanly into the existing classification scheme. Analysis of their spiking revealed integer-multiple patterning in which spike trains comprised a fundamental interspike interval and multiples thereof. Integer-multiple-patterned spiking was reproduced by randomly removing spikes from an otherwise regular spike train, suggesting that semiregular units represent SA2 units in which some spikes are "missing." We hypothesized that missing spikes arose from intermittent failure of spikes to initiate or to propagate. Intermittent failure of spike initiation was ruled out by several observations: integer-multiple-patterned spiking was not induced by intradermal lidocaine, was independent of stimulus modality (mechanical vs optogenetic), and could not be reproduced in a conductance-based model neuron given constant input. On the other hand, integer-multiple-patterned spiking was induced by application of lidocaine to the DRG, thus pinpointing intermittent failure of spike propagation as the basis for integer-multiple-patterned spiking. Indeed, half of all SA2 units exhibited some missing spikes, mostly at low rate (<5%), which suggests that axons are efficient in using the lowest safety factor capable of producing near-perfect propagation reliability.SIGNIFICANCE STATEMENT The impedance mismatch at axon branch points can impede spike propagation. Reliability of spike propagation across branch points remains an open question and is especially important for primary afferents whose spikes must cross a T-junction to reach the CNS. Past research on propagation reliability has relied almost entirely on simulations and in vitro experiments. Here, recording in vivo, we linked a distinctive pattern of spiking to the intermittent failure of spike propagation at the T-junction. The rarity of failures argues that safety factor is high under physiological conditions, yet the occurrence of such failures argues that safety factor is just high enough to ensure near-perfect reliability, consistent with a good balance between propagation reliability and energy efficiency.

Spike-Based Winner-Take-All Computation: Fundamental Limits and Order-Optimal Circuits

Winner-take-all (WTA) refers to the neural operation that selects a (typically small) group of neurons from a large neuron pool. It is conjectured to underlie many of the brain's fundamental computational abilities. However, not much is known about the robustness of a spike-based WTA network to the inherent randomness of the input spike trains. In this work, we consider a spike-based k-WTA model wherein n randomly generated input spike trains compete with each other based on their underlying firing rates and k winners are supposed to be selected. We slot the time evenly with each time slot of length 1 ms and model the n input spike trains as n independent Bernoulli processes. We analytically characterize the minimum waiting time needed so that a target minimax decision accuracy (success probability) can be reached. We first derive an information-theoretic lower bound on the waiting time. We show that to guarantee a (minimax) decision error ≤δ (where δ∈(0,1)), the waiting time of any WTA circuit is at least [Formula: see text]where R⊆(0,1) is a finite set of rates and TR is a difficulty parameter of a WTA task with respect to set R for independent input spike trains. Additionally, TR is independent of δ, n, and k. We then design a simple WTA circuit whose waiting time is [Formula: see text]provided that the local memory of each output neuron is sufficiently long. It turns out that for any fixed δ, this decision time is order-optimal (i.e., it matches the above lower bound up to a multiplicative constant factor) in terms of its scaling in n, k, and TR.

Quantifying encoding redundancy induced by rate correlations in Poisson neurons

Temporal correlations in neuronal spike trains are known to introduce redundancy to stimulus encoding. However, exact methods to describe how these correlations impact neural information transmission quantitatively are lacking. Here, we provide a general measure for the information carried by correlated rate modulations only, neglecting other spike correlations, and use it to investigate the effect of rate correlations on encoding redundancy. We derive it analytically by calculating the mutual information between a time-correlated, rate modulating signal and the resulting spikes of Poisson neurons. Whereas this information is determined by spike autocorrelations only, the redundancy in information encoding due to rate correlations depends on both the distribution and the autocorrelation of the rate histogram. We further demonstrate that at very small signal strengths the information carried by rate correlated spikes becomes identical to that of independent spikes, in effect measuring the signal modulation depth. In contrast, a vanishing signal correlation time maximizes information but does not generally yield the information of independent spikes. Overall, our study sheds light on the role of signal-induced temporal correlations for neural coding, by providing insight into how signal features shape redundancy and by establishing mathematical links between existing methods.

Cerebellar Purkinje cells control eye movements with a rapid rate code that is invariant to spike irregularity

The rate and temporal pattern of neural spiking each have the potential to influence computation. In the cerebellum, it has been hypothesized that the irregularity of interspike intervals in Purkinje cells affects their ability to transmit information to downstream neurons. Accordingly, during oculomotor behavior in mice and rhesus monkeys, mean irregularity of Purkinje cell spiking varied with mean eye velocity. However, moment-to-moment variations revealed a tight correlation between eye velocity and spike rate, with no additional information conveyed by spike irregularity. Moreover, when spike rate and irregularity were independently controlled using optogenetic stimulation, the eye movements elicited were well-described by a linear population rate code with 3-5 ms temporal precision. Biophysical and random-walk models identified biologically realistic parameter ranges that determine whether spike irregularity influences responses downstream. The results demonstrate cerebellar control of movements through a remarkably rapid rate code, with no evidence for an additional contribution of spike irregularity.

Information transmission of mean and variance coding in integrate-and-fire neurons

Neurons process information by translating continuous signals into patterns of discrete spike times. An open question is how much information these spike times contain about signals which modulate either the mean or the variance of the somatic currents in neurons, as is observed experimentally. Here we calculate the exact information contained in discrete spike times about a continuous signal in both encoding strategies. We show that the information content about mean modulating signals is generally substantially larger than about variance modulating signals for biological parameters. Our analysis further reveals that higher information transmission is associated with a larger proportion of nonlinear signal encoding. Our study measures the complete information content of mean and variance coding and provides a method to determine what fraction of the total information is linearly decodable.

A Tradeoff in the Neural Code across Regions and Species

Many evolutionary years separate humans and macaques, and although the amygdala and cingulate cortex evolved to enable emotion and cognition in both, an evident functional gap exists. Although they were traditionally attributed to differential neuroanatomy, functional differences might also arise from coding mechanisms. Here we find that human neurons better utilize information capacity (efficient coding) than macaque neurons in both regions, and that cingulate neurons are more efficient than amygdala neurons in both species. In contrast, we find more overlap in the neural vocabulary and more synchronized activity (robustness coding) in monkeys in both regions and in the amygdala of both species. Our findings demonstrate a tradeoff between robustness and efficiency across species and regions. We suggest that this tradeoff can contribute to differential cognitive functions between species and underlie the complementary roles of the amygdala and the cingulate cortex. In turn, it can contribute to fragility underlying human psychopathologies.

Neuronal variability and tuning are balanced to optimize naturalistic self-motion coding in primate vestibular pathways

It is commonly assumed that the brain’s neural coding strategies are adapted to the statistics of natural stimuli. Specifically, to maximize information transmission, a sensory neuron’s tuning function should effectively oppose the decaying stimulus spectral power, such that the neural response is temporally decorrelated (i.e. ‘whitened’). However, theory predicts that the structure of neuronal variability also plays an essential role in determining how coding is optimized. Here, we provide experimental evidence supporting this view by recording from neurons in early vestibular pathways during naturalistic self-motion. We found that central vestibular neurons displayed temporally whitened responses that could not be explained by their tuning alone. Rather, computational modeling and analysis revealed that neuronal variability and tuning were matched to effectively complement natural stimulus statistics, thereby achieving temporal decorrelation and optimizing information transmission. Taken together, our findings reveal a novel strategy by which neural variability contributes to optimized processing of naturalistic stimuli.

State dependence of stimulus-induced variability tuning in macaque MT

Behavioral states marked by varying levels of arousal and attention modulate some properties of cortical responses (e.g. average firing rates or pairwise correlations), yet it is not fully understood what drives these response changes and how they might affect downstream stimulus decoding. Here we show that changes in state modulate the tuning of response variance-to-mean ratios (Fano factors) in a fashion that is neither predicted by a Poisson spiking model nor changes in the mean firing rate, with a substantial effect on stimulus discriminability. We recorded motion-sensitive neurons in middle temporal cortex (MT) in two states: alert fixation and light, opioid anesthesia. Anesthesia tended to lower average spike counts, without decreasing trial-to-trial variability compared to the alert state. Under anesthesia, within-trial fluctuations in excitability were correlated over longer time scales compared to the alert state, creating supra-Poisson Fano factors. In contrast, alert-state MT neurons have higher mean firing rates and largely sub-Poisson variability that is stimulus-dependent and cannot be explained by firing rate differences alone. The absence of such stimulus-induced variability tuning in the anesthetized state suggests different sources of variability between states. A simple model explains state-dependent shifts in the distribution of observed Fano factors via a suppression in the variance of gain fluctuations in the alert state. A population model with stimulus-induced variability tuning and behaviorally constrained information-limiting correlations explores the potential enhancement in stimulus discriminability by the cortical population in the alert state.

The brain controls behavior fluidly in a wide variety of cognitive contexts that alter the precision of neural responses. We examine how neural variability changes versus the mean response as a function of the stimulus and the behavioral state. We show that this scaled variability can have qualitatively different stimulus tuning in different behavioral contexts. In alert primates, scaled variability is tuned to the direction of motion of a visual stimulus and decreases around the preferred direction of each neuron. Under anesthesia, neurons show flat scaled variability tuning and, overall, responses are significantly more variable. We develop a simple model that includes a parameter describing firing rate gain fluctuations that can explain these changes. Our results suggest that tuned decreases in scaled variability during wakefulness may be mediated by an active process that suppresses synchronization and makes information transmission more reliable.

Fast regular firings induced by intra- and inter-time delays in two clustered neuronal networks

In this paper, we consider two clustered neuronal networks with dense intra-synaptic links within each cluster and sparse inter-synaptic links between them. We focus on the effects of intra- and inter-time delays on the spiking regularity and timing in both clusters. With the aid of simulation results, we show that intermediate intra- and inter-time delays are able to separately induce fast regular firing - spiking activity with a high firing rate as well as a high spiking regularity. Moreover, when both intra- and inter-time delays are present, we find that fast regular firings are induced much more frequently than if only a single type of delay is present in the system. Our results indicate that appropriately adjusted intra- and inter-time delays can significantly facilitate fast regular firing in neuronal networks. Based on a detailed analysis, we conjecture that this is most likely when the largest value of common divisors of the intra- and inter-time delays falls into a range where fast regular firings are induced by suitable intra- or inter-time delays alone.

A Simple Model for Low Variability in Neural Spike Trains

Neural noise sets a limit to information transmission in sensory systems. In several areas, the spiking response (to a repeated stimulus) has shown a higher degree of regularity than predicted by a Poisson process. However, a simple model to explain this low variability is still lacking. Here we introduce a new model, with a correction to Poisson statistics, that can accurately predict the regularity of neural spike trains in response to a repeated stimulus. The model has only two parameters but can reproduce the observed variability in retinal recordings in various conditions. We show analytically why this approximation can work. In a model of the spike-emitting process where a refractory period is assumed, we derive that our simple correction can well approximate the spike train statistics over a broad range of firing rates. Our model can be easily plugged to stimulus processing models, like a linear-nonlinear model or its generalizations, to replace the Poisson spike train hypothesis that is commonly assumed. It estimates the amount of information transmitted much more accurately than Poisson models in retinal recordings. Thanks to its simplicity, this model has the potential to explain low variability in other areas.

"Shepherd's crook" neurons drive and synchronize the enhancing and suppressive mechanisms of the midbrain stimulus selection network

The optic tectum (TeO), or superior colliculus, is a multisensory midbrain center that organizes spatially orienting responses to relevant stimuli. To define the stimulus with the highest priority at each moment, a network of reciprocal connections between the TeO and the isthmi promotes competition between concurrent tectal inputs. In the avian midbrain, the neurons mediating enhancement and suppression of tectal inputs are located in separate isthmic nuclei, facilitating the analysis of the neural processes that mediate competition. A specific subset of radial neurons in the intermediate tectal layers relay retinal inputs to the isthmi, but at present it is unclear whether separate neurons innervate individual nuclei or a single neural type sends a common input to several of them. In this study, we used in vitro neural tracing and cell-filling experiments in chickens to show that single neurons innervate, via axon collaterals, the three nuclei that comprise the isthmotectal network. This demonstrates that the input signals representing the strength of the incoming stimuli are simultaneously relayed to the mechanisms promoting both enhancement and suppression of the input signals. By performing in vivo recordings in anesthetized chicks, we also show that this common input generates synchrony between both antagonistic mechanisms, demonstrating that activity enhancement and suppression are closely coordinated. From a computational point of view, these results suggest that these tectal neurons constitute integrative nodes that combine inputs from different sources to drive in parallel several concurrent neural processes, each performing complementary functions within the network through different firing patterns and connectivity.

Behavioral Context Determines Network State and Variability Dynamics in Monkey Motor Cortex

Variability of spiking activity is ubiquitous throughout the brain but little is known about its contextual dependance. Trial-to-trial spike count variability, estimated by the Fano Factor (FF), and within-trial spike time irregularity, quantified by the coefficient of variation (CV), reflect variability on long and short time scales, respectively. We co-analyzed FF and the local coefficient of variation (CV2) in monkey motor cortex comparing two behavioral contexts, movement preparation (wait) and execution (movement). We find that the FF significantly decreases from wait to movement, while the CV2 increases. The more regular firing (expressed by a low CV2) during wait is related to an increased power of local field potential (LFP) beta oscillations and phase locking of spikes to these oscillations. In renewal processes, a widely used model for spiking activity under stationary input conditions, both measures are related as FF ≈ CV². This expectation was met during movement, but not during wait where FF ≫ CV2². Our interpretation is that during movement preparation, ongoing brain processes result in changing network states and thus in high trial-to-trial variability (expressed by a high FF). During movement execution, the network is recruited for performing the stereotyped motor task, resulting in reliable single neuron output. Our interpretation is in the light of recent computational models that generate non-stationary network conditions.

Neural Coding of Cell Assemblies via Spike-Timing Self-Information

Cracking brain's neural code is of general interest. In contrast to the traditional view that enormous spike variability in resting states and stimulus-triggered responses reflects noise, here, we examine the "Neural Self-Information Theory" that the interspike-interval (ISI), or the silence-duration between 2 adjoining spikes, carries self-information that is inversely proportional to its variability-probability. Specifically, higher-probability ISIs convey minimal information because they reflect the ground state, whereas lower-probability ISIs carry more information, in the form of "positive" or "negative surprisals," signifying the excitatory or inhibitory shifts from the ground state, respectively. These surprisals serve as the quanta of information to construct temporally coordinated cell-assembly ternary codes representing real-time cognitions. Accordingly, we devised a general decoding method and unbiasedly uncovered 15 cell assemblies underlying different sleep cycles, fear-memory experiences, spatial navigation, and 5-choice serial-reaction time (5CSRT) visual-discrimination behaviors. We further revealed that robust cell-assembly codes were generated by ISI surprisals constituted of ~20% of the skewed ISI gamma-distribution tails, conforming to the "Pareto Principle" that specifies, for many events-including communication-roughly 80% of the output or consequences come from 20% of the input or causes. These results demonstrate that real-time neural coding arises from the temporal assembly of neural-clique members via silence variability-based self-information codes.

Slot-like capacity and resource-like coding in a neural model of multiple-item working memory

For the past decade, research on the storage limitations of working memory has been dominated by two fundamentally different hypotheses. On the one hand, the contents of working memory may be stored in a limited number of "slots," each with a fixed resolution. On the other hand, any number of items may be stored but with decreasing resolution. These two hypotheses have been invaluable in characterizing the computational structure of working memory, but neither provides a complete account of the available experimental data or speaks to the neural basis of the limitations it characterizes. To address these shortcomings, we simulated a multiple-item working memory task with a cortical network model, the cellular resolution of which allowed us to quantify the coding fidelity of memoranda as a function of memory load, as measured by the discriminability, regularity, and reliability of simulated neural spiking. Our simulations account for a wealth of neural and behavioral data from human and nonhuman primate studies, and they demonstrate that feedback inhibition lowers both capacity and coding fidelity. Because the strength of inhibition scales with the number of items stored by the network, increasing this number progressively lowers fidelity until capacity is reached. Crucially, the model makes specific, testable predictions for neural activity on multiple-item working memory tasks. NEW & NOTEWORTHY Working memory is the ability to keep information in mind and is fundamental to cognition. It is actively debated whether the storage limitations of working memory reflect a small number of storage units (slots) or a decrease in coding resolution as a limited resource is allocated to more items. In a cortical model, we found that slot-like capacity and resource-like neural coding resulted from the same mechanism, offering an integrated explanation for storage limitations.

Dethroning the Fano Factor: A Flexible, Model-Based Approach to Partitioning Neural Variability

Neurons in many brain areas exhibit high trial-to-trial variability, with spike counts that are overdispersed relative to a Poisson distribution. Recent work (Goris, Movshon, & Simoncelli, 2014 ) has proposed to explain this variability in terms of a multiplicative interaction between a stochastic gain variable and a stimulus-dependent Poisson firing rate, which produces quadratic relationships between spike count mean and variance. Here we examine this quadratic assumption and propose a more flexible family of models that can account for a more diverse set of mean-variance relationships. Our model contains additive gaussian noise that is transformed nonlinearly to produce a Poisson spike rate. Different choices of the nonlinear function can give rise to qualitatively different mean-variance relationships, ranging from sublinear to linear to quadratic. Intriguingly, a rectified squaring nonlinearity produces a linear mean-variance function, corresponding to responses with a constant Fano factor. We describe a computationally efficient method for fitting this model to data and demonstrate that a majority of neurons in a V1 population are better described by a model with a nonquadratic relationship between mean and variance. Finally, we demonstrate a practical use of our model via an application to Bayesian adaptive stimulus selection in closed-loop neurophysiology experiments, which shows that accounting for overdispersion can lead to dramatic improvements in adaptive tuning curve estimation.