Biology subjects

Neymotin, S. A.

Publications and source records attributed to Neymotin, S. A..

18 recordsLinked to original sources

bioRxiv2026

Biophysical modeling reveals how recording geometry shapes representations of cortical activity

Extracellular electrophysiological recordings reflect both the organization of neural activity and the geometry through which that activity is sampled. Here, we used biophysical modeling and experimental recordings from the macaque auditory cortex to determine how cortical activity is transformed across neural sources, extracellular signals, and recording geometries. In a biophysically detailed thalamocortical model, four separately instantiated cortical-column conditions spanning graded thalamic recruitment exhibited a graded response hierarchy across local field potential, current-source density, and multiunit activity, despite signal-dependent differences in the contributions of cortical neuronal populations. We then arranged independently simulated cortical columns into controlled spatial configurations and sampled the same underlying sources using virtual linear laminar and Directional and Scalable (DiSc) electrode arrays. The linear geometry primarily represented variation along cortical depth, whereas DiSc additionally represented circumferential variation associated with lateral source organization, demonstrating that recording geometry determines which spatial dimensions of common underlying activity are represented in extracellular measurements. Experimental recordings obtained with the corresponding geometries were consistent with these model-derived predictions: broadband-noise responses exhibited spatial organization shared across tone-evoked responses in both multidimensional DiSc maps and laminar profiles, despite substantial variation in frequency-dependent response magnitude across penetrations. Together, these results show that extracellular measurements represent an interaction between biological source organization and recording geometry. Biophysical forward modeling therefore provides a framework for determining which features of underlying circuit organization remain identifiable after extracellular sampling and for relating neural-interface design to the dimensions of cortical activity accessible experimentally.

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bioRxiv2026

Context-Aware Evidence-Gated Plasticity for Multi-Goal Learning in Spiking Neural Networks

Background / IntroductionBiologically inspired spiking neural networks can model adaptive behavior, but learning multiple goals is difficult because synaptic updates for different targets can interfere. We tested whether multi-timescale plasticity and context-specific credit assignment could improve continual multi-goal learning in a spiking navigation system inspired by entorhinal-hippocampal circuitry. MethodsWe developed a closed-loop spiking model containing grid-like, place-like, target-related, association, and motor-output populations. An agent navigated in a two-dimensional environment with randomized starting locations and learned through reward-modulated spike-timing dependent plasticity (STDP/RL) and a novel evidence-gated plasticity (EGP) framework. EGP accumulates candidate synaptic modifications, evaluates them using reward evidence, and consolidates only changes that improve performance. A target-context variant maintained separate proposal stores and reward evaluation for each target. ResultsSTDP/RL learned and retained a single-target navigation policy, but multi-target training produced substantial interference, including attraction to incorrect targets after learning. Across 10 connectivity seeds, target-context EGP achieved higher late-stage reward than global EGP, improved weakest-target performance, and increased the fraction of targets achieving positive reward. In a longer continual-learning simulation, reward increased for all targets, TEST-phase performance increasingly exceeded TRAIN-phase performance, and proposal magnitudes grew over learning. Dwell-time confusion analyses showed that target-context EGP reduced wrong-target attraction and improved target selectivity relative to multi-target STDP/RL. ConclusionsThese results demonstrate that spiking navigation circuits can learn goal-directed behavior using local plasticity, but robust multi-goal learning benefits from context-specific evidence-based consolidation. Target-context EGP provides a biologically motivated mechanism for reducing interference during continual reinforcement learning in spiking neural networks.

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bioRxiv2026

DeltaQ: Value-Guided Hebbian Learning in Spiking Neuronal Networks for Multi-Goal Navigation

Animals must often navigate environments where feedback about progress toward a goal is sparse or delayed, requiring internal representations of space and memory of prior experience. The hippocampal-entorhinal system is believed to support this capability through distributed spatial representations that guide goal-directed behavior. However, many computational models of these circuits focus primarily on reproducing neural dynamics rather than demonstrating how such representations support learning on navigation tasks. We present a biologically inspired spiking neuronal network (SNN) model that combines grid-cell-derived spatial representations, {Delta}Q-modulated Hebbian plasticity, and context-dependent modulation to support navigation under sparse reward conditions. Grid Cell populations generate distributed spatial codes that are transformed by an Association Cell population into more spatially selective internal representations. Learning is driven by changes in Q-values ({Delta}Q) computed from a goal-conditioned Q-table, allowing local synaptic plasticity to incorporate information about long-term navigation outcomes. For environments containing multiple navigation objectives, a Context Cell population provides task-dependent modulation that enables a shared network architecture to support distinct navigation policies. Across two complementary maze environments, the model demonstrates three core capabilities: generation of distinct spatial representations, learning of efficient navigation policies under sparse and delayed reward, and support for multiple navigation objectives within a shared environment. The results further show that contextual modulation introduces subtle task-dependent variations into a largely shared population representation, allowing identical spatial locations to support different navigation behaviors. These findings demonstrate that biologically inspired spatial representations, value-guided plasticity, and contextual modulation can jointly support flexible navigation in spiking neuronal networks, providing a bridge between mechanistic neural circuit models and functional reinforcement learning.

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bioRxiv2025

Synaptic Synchronization-Based Learning of Pattern Separation in Self-Organizing Probabilistic Spiking Neural Networks

Neuroscience-inspired neural networks bridge biology and technology, offering powerful tools to model brain function while enabling adaptive, efficient control in robotics. In this work, we present a neuroscience-inspired synaptic learning rule based on the synchronization of synaptic inputs to single excitatory neurons within a feedforward spiking neural network. The model consists of three excitatory layers and two feedback inhibitory layers, with initially low connection probabilities and weak synaptic weights assigned to the excitatory neurons. Under an unsupervised learning paradigm, stimulus patterns were presented to the network, allowing synaptic weights and connectivity to evolve dynamically across training trials. We investigated how these dynamics depended on feedback inhibition intensity and identified conditions under which the network achieved stable activity. Furthermore, we evaluated the models pattern separation efficacy and its relationship to network dynamics. The results highlight the critical role of feedback inhibition in both stabilizing the network and enhancing pattern separation. In particular, results show balanced synchronization between excitatory and inhibitory populations maximizes separation efficacy. Beyond providing a novel computational framework for understanding information processing in neural systems, this model also offers insights into cognitive disorders associated with impaired inhibition and pattern separation, such as autism and schizophrenia. Finally, we embedded the trained network within a simulated agent navigating a two-dimensional environment, where it was tasked with identifying a trained stimulus as an obstacle and avoiding it. The model offers a framework for advancing cognitive robotics by enabling novel approaches that mimic natural intelligence and support the learning of complex environmental patterns.

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bioRxiv2024

Is there a ubiquitous spectrolaminar motif of local field potential power across primate neocortex?

Mendoza-Halliday, Major et al.1 (referred to as "Mendoza-Halliday et al." for brevity), advocate for a local field potential (LFP)-based approach to functional identification of cortical layers during "laminar" (simultaneous recordings from all cortical layers) multielectrode recordings in nonhuman primates (NHPs). They describe a "ubiquitous spectrolaminar motif" in the primate neocortex: 1) 75-150 Hz power peaks in the supragranular layers, 2) 10-19 Hz power peaks in the infragranular layers and 3) the crossing point of their laminar power gradients identifies Layer 4 (L4). Identification of L4 is critical in general, but especially for Mendoza-Halliday et al. as the "motif" discovery is couched within a framework whose central hypothesis is that gamma activity originates in the supragranular layers and reflects feedforward activity, while alpha-beta activity originates in the infragranular layers and reflects feedback activity. In an impressive scientific effort, Mendoza-Halliday et al. analyzed laminar data from 14 cortical areas in 2 prior macaque studies and compared them to marmoset, mouse, and human data to further bolster the canonical nature of the motif. Identification of such canonical principles of brain operation is clearly a topic of broad scientific interest. Similarly, a reliable online method for L4 identification would be of broad scientific value for the rapidly increasing use of laminar recordings using numerous evolving technologies. Despite Mendoza-Halliday et al.s papers strengths, and its potential for scientific impact, a series of concerns that are fundamental to the analysis and interpretation of laminar activity profile data in general, and local field potential (LFP) signals in particular, led us to question its conclusions. Here, we address four key questions: Q1) Is the spectrolaminar motif ubiquitous, i.e. "found everywhere" or "universal"? Q2) Do features of the motif reliably identify Layer (L) 4? Q3) Are Mendoza-Halliday et al.s newly introduced methods (FLIP and vFLIP) reliable? And Q4) Are the proposed biophysical mechanisms underlying the motif well justified? We used new sets of data comprised of stimulus-evoked laminar response profiles from primary and higher-order auditory cortices (A1 and belt cortex), and primary visual cortex (V1) to test these questions. The rationale for using these areas as a test bed for new methods is that, in contrast to higher-order cortical areas, their laminar anatomy and physiology have already been extensively characterized by prior studies, and there is general agreement across laboratories on key matters like L4 identification. In short, we find that Mendoza-Halliday et al.s findings do not generalize well to these cortical areas. Specifically, regarding Q1: Though we can find a spectrolaminar gradient that is qualitatively consistent with Mendoza-Halliday et al., it is quantifiable in only 61-64% of our recordings, indicating that the motif is common but by no means universal (see "Evaluation of the FLIP method [...]" and "Evaluation of vFLIP [...]"). Regarding Q2: The motifs high/low frequency gradient cross point identified L4 in only 29-33% of our recordings. Regarding Q3: FLIP and vFLIP exhibit marked variability across studies, across brain areas, and spuriously detect cortical layer inversions (see "FLIPs fitting process [...]" and "Evaluation of vFLIP [...]"). Regarding Q4: the biophysical modeling findings cited to support Mendoza-Hallidays conclusions (see "Going forward - in silico [...]") do not reproduce the LFP data trends. While our findings are in many respects at odds with those of Mendoza-Halliday et al., the paper already has, and will continue to spark debate and further experimentation. Hopefully this countervailing presentation will lead to robust collegial efforts to define optimal strategies for applying laminar recording methods in future studies.

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bioRxiv2024

Mechanism of an Intrinsic Oscillation in Rat Geniculate Interneurons

Depolarizing current injections produced a rhythmic bursting of action potentials - a bursting oscillation - in a set of local interneurons in the lateral geniculate nucleus (LGN) of rats. The current dynamics underlying this firing pattern have not been determined, though this cell type constitutes an important cellular component of thalamocortical circuitry, and contributes to both pathologic and non-pathologic brain states. We thus investigated the source of the bursting oscillation using pharmacological manipulations in LGN slices in vitro and in silico. 1. Selective blockade of calcium channel subtypes revealed that high-threshold calcium currents IL and IP contributed strongly to the oscillation. 2. Increased extracellular K+ concentration (decreased K+ currents) eliminated the oscillation. 3. Selective blockade of K+ channel subtypes demonstrated that the calcium-sensitive potassium current (IAHP ) was of primary importance. A morphologically simplified, multicompartment model of the thalamic interneuron characterized the oscillation as follows: 1. The low-threshold calcium current (IT ) provided the strong initial burst characteristic of the oscillation. 2. Alternating fluxes through high-threshold calcium channels and IAHP then provided the continuing oscillations burst and interburst periods respectively. This interplay between IL and IAHP contrasts with the current dynamics underlying oscillations in thalamocortical and reticularis neurons, which primarily involve IT and IH, or IT and IAHP respectively. These findings thus point to a novel electrophysiological mechanism for generating intrinsic oscillations in a major thalamic cell type. Because local interneurons can sculpt the behavior of thalamocortical circuits, these results suggest new targets for the manipulation of ascending thalamocortical network activity.

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bioRxiv2022

Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics

We developed a biophysically-detailed model of the macaque auditory thalamocortical circuits, including primary auditory cortex (A1), medial geniculate body (MGB) and thalamic reticular nuclei (TRN), using the NEURON simulator and NetPyNE multiscale modeling tool. We simulated A1 as a cortical column with a depth of 2000 m and 200 m diameter, containing over 12k neurons and 30M synapses. Neuron densities, laminar locations, classes, morphology and biophysics, and connectivity at the long-range, local and dendritic scale were derived from published experimental data. The A1 model included 6 cortical layers and multiple populations of neurons consisting of 4 excitatory and 4 inhibitory types, and was reciprocally connected to the thalamus (MGB and TRN), mimicking anatomical connectivity. MGB included core and matrix thalamocortical neurons with layer-specific projection patterns to A1, and thalamic interneurons projecting locally. Auditory stimulus-related inputs to the MGB were simulated using phenomenological models of the cochlear/auditory nerve and the inferior colliculus. The model generated cell type and layer-specific firing rates consistent with experimentally observed ranges, and accurately simulated the corresponding local field potentials (LFPs), current source density (CSD), and electroencephalogram (EEG) signals. Laminar CSD patterns during spontaneous activity, and in response to speech input, were similar to those recorded experimentally. Physiological oscillations emerged spontaneously across frequency bands without external rhythmic inputs and were comparable to those recorded in vivo. We used the model to unravel the contributions from distinct cell type and layer-specific neuronal populations to oscillation events detected in CSD, and explored how these relate to the population firing patterns. Overall, the computational model provides a quantitative theoretical framework to integrate and interpret a wide range of experimental data in auditory circuits. It also constitutes a powerful tool to evaluate hypotheses and make predictions about the cellular and network mechanisms underlying common experimental measurements, including MUA, LFP and EEG signals.

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bioRxiv2022

Multiscale model of primary motor cortex circuits reproduces in vivo cell type-specific dynamics associated with behavior

Understanding cortical function requires studying multiple scales: molecular, cellular, circuit and behavior. We developed a biophysically detailed multiscale model of mouse primary motor cortex (M1) with over 10,000 neurons and 30 million synapses. Neuron types, densities, spatial distributions, morphologies, biophysics, connectivity and dendritic synapse locations were tightly constrained by experimental data. The model includes long-range inputs from 7 thalamic and cortical regions, as well as noradrenergic inputs from locus coeruleus. Connectivity depended on cell class and cortical depth at sublaminar resolution. The model accurately predicted in vivo layer- and cell type-specific responses (firing rates and LFP) associated with behavioral states (quiet wakefulness and movement) and experimental manipulations (noradrenaline receptor blocking and thalamus inactivation). It also enabled evaluation of multiple mechanistic hypotheses underlying the observed activity. This quantitative theoretical framework can be used to integrate and interpret M1 experimental data and sheds light on the cell type-specific multiscale dynamics associated with a range of experimental conditions and behaviors.

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bioRxiv2021

Multi-timescale biological learning algorithms train spiking neuronal network motor control

Despite being biologically unrealistic, artificial neural networks (ANNs) have been successfully trained to perform a wide range of sensory-motor behaviors. In contrast, the performance of more biologically realistic spiking neuronal network (SNN) models trained to perform similar behaviors remains relatively suboptimal. In this work, we aimed at pushing the field of SNNs forward by exploring the potential of different learning mechanisms to achieve optimal performance. Inspired by biological learning mechanisms operating at multiple timescales, we used spike-timing-dependent reinforcement learning (STDP-RL) and evolutionary strategy (EVOL) with SNNs to solve the CartPole reinforcement learning (RL) control problem. Though the role of STDP-RL in biological systems is well established, several other mechanisms, though not fully understood, work in concert during learning in vivo. Recreating accurate models that capture the interaction of STDP-RL with these diverse learning mechanisms is extremely difficult. EVOL is an alternative method, and has been successfully used in many studies to fit model neural responsiveness to electrophysiological recordings and in some cases for classification problems. One advantage of EVOL is that it may not need to capture all interacting components of synaptic plasticity, and thus provides a better alternative to STDP-RL. Here, we compared the performance of each algorithm after training, which revealed EVOL as a powerful method to training SNNs to perform sensory-motor behaviors. Our modeling opens up new capabilities for SNNs in RL and could serve as a testbed for neurobiologists aiming to understand multi-timescale learning mechanisms and dynamics in neuronal circuits.

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bioRxiv2021

Training a spiking neuronal network model of visual-motor cortex to play a virtual racket-ball game using reinforcement learning

Recent models of spiking neuronal networks have been trained to perform behaviors in static environments using a variety of learning rules, with varying degrees of biological realism. Most of these models have not been tested in dynamic visual environments where models must make predictions on future states and adjust their behavior accordingly. The models using these learning rules are often treated as black boxes, with little analysis on circuit architectures and learning mechanisms supporting optimal performance. Here we developed visual/motor spiking neuronal network models and trained them to play a virtual racket-ball game using several reinforcement learning algorithms inspired by the dopaminergic reward system. We systematically investigated how different architectures and circuit-motifs (feed-forward, recurrent, feedback) contributed to learning and performance. We also developed a new biologically-inspired learning rule that significantly enhanced performance, while reducing training time. Our models included visual areas encoding game inputs and relaying the information to motor areas, which used this information to learn to move the racket to hit the ball. Neurons in the early visual area relayed information encoding object location and motion direction across the network. Neuronal association areas encoded spatial relationships between objects in the visual scene. Motor populations received inputs from visual and association areas representing the dorsal pathway. Two populations of motor neurons generated commands to move the racket up or down. Model-generated actions updated the environment and triggered reward or punishment signals that adjusted synaptic weights so that the models could learn which actions led to reward. Here we demonstrate that our biologically-plausible learning rules were effective in training spiking neuronal network models to solve problems in dynamic environments. We used our models to dissect the circuit architectures and learning rules most effective for learning. Our model shows that learning mechanisms involving different neural circuits produce similar performance in sensory-motor tasks. In biological networks, all learning mechanisms may complement one another, accelerating the learning capabilities of animals. Furthermore, this also highlights the resilience and redundancy in biological systems.

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bioRxiv2021

Laminar dynamics of beta bursts in human motor cortex

Modulation of motor cortical activity in the beta frequency range is one of the strongest and most studied movement-related neural signals. At the single trial level, beta band activity is often characterized by transient bursting events rather than slowly modulating oscillations, suggesting a more rapid, information-encoding functional role than previously believed. Insight into how beta bursts are generated in sensorimotor circuits can provide important constraints to theories about their functional role for movement control. To this end, we leverage and extend recent developments in high precision MEG for temporally resolved laminar analysis of burst activity, combined with a neocortical circuit model that simulates the biophysical generators of the electrical currents which drive beta bursts. This approach pinpoints the generation of beta bursts in human motor cortex to distinct excitatory synaptic inputs to deep and superficial cortical layers, which drive current flow in opposite directions. These laminar dynamics of beta bursts in motor cortex align with prior invasive animal recordings within the somatosensory cortex, and suggest a conserved mechanism for somatosensory and motor cortical beta bursts. More generally, we demonstrate the ability for uncovering the laminar dynamics of event-related neural signals in human non-invasive recordings.

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bioRxiv2021

Effects of Ih and TASK-like shunting current on dendritic impedance in layer 5 pyramidal-tract neurons

Pyramidal neurons in neocortex have complex input-output relationships that depend on their morphologies, ion channel distributions, and the nature of their inputs, but which cannot be replicated by simple integrate-and-fire models. The impedance properties of their dendritic arbors, such as resonance and phase shift, shape neuronal responses to synaptic inputs and provide intraneuronal functional maps reflecting their intrinsic dynamics and excitability. Experimental studies of dendritic impedance have shown that neocortical pyramidal tract neurons exhibit distance-dependent changes in resonance and impedance phase with respect to the soma. We therefore investigated how well several biophysically-detailed multi-compartment models of neocortical layer 5 pyramidal tract neurons reproduce the location-dependent impedance profiles observed experimentally. Each model tested here exhibited location-dependent impedance profiles, but most captured either the observed impedance amplitude or phase, not both. The only model that captured features from both incorporates HCN channels and a shunting current, like that produced by Twik-related acid-sensitive K+(TASK) channels. TASK-like channel activity in this model was dependent on local peak HCN channel conductance (Ih). We found that while this shunting current alone is insufficient to produce resonance or realistic phase response, it modulates all features of dendritic impedance, including resonance frequencies, resonance strength, synchronous frequencies, and total inductive phase. We also explored how the interaction of Ih and a TASK-like shunting current shape synaptic potentials and produce degeneracy in dendritic impedance profiles, wherein different combinations of Ih and shunting current can produce the same impedance profile. New & NoteworthyWe simulated chirp current stimulation in the apical dendrites of 5 biophysically-detailed multi-compartment models of neocortical pyramidal tract neurons and found that a combination of HCN channels and TASK-like channels produced the best fit to experimental measurements of dendritic impedance. We then explored how HCN and TASK-like channels can shape the dendritic impedance as well as the voltage response to synaptic currents.

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bioRxiv2020

Taxonomy of neural oscillation events in primate auditory cortex

Electrophysiological oscillations in the brain have been shown to occur as multi-cycle events, with onset and offset dependent on behavioral and cognitive state. To provide a baseline for state-related and task-related events, we quantified oscillation features in resting-state recordings. We used two invasively-recorded electrophysiology datasets: one from human, and one from non-human primate auditory system. After removing incidentally occuring event related potentials, we used a wavelet transform based method to quantify oscillation features. We identified about 2 million oscillation events, classified within traditional frequency bands: delta, theta, alpha, beta, low gamma, gamma, high gamma. Oscillation events of 1-44 cycles were present in at least one frequency band in 90% of the time in human and non-human primate recordings. Individual oscillation events were characterized by non-constant frequency and amplitude. This result naturally contrasts with prior studies which assumed such constancy, but is consistent with evidence from event-associated oscillations. We measured oscillation event duration, frequency span, and waveform shape. Oscillations tended to exhibit multiple cycles per event, verifiable by comparing filtered to unfiltered waveforms. In addition to the clear intra-event rhythmicity, there was also evidence of inter-event rhythmicity within bands, demonstrated by finding that coefficient of variation of interval distributions and Fano Factor measures differed significantly from a Poisson distribution assumption. Overall, our study demonstrates that rhythmic, multi-cycle oscillation events dominate auditory cortical dynamics.

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bioRxiv2019

In silico hippocampal modeling for multi-target pharmacotherapy in schizophrenia

BackgroundTreatment of schizophrenia has had limited success in treating core cognitive symptoms. The evidence of multi-gene involvement suggests that multi-target therapy may be needed. Meanwhile, the complexity of schizophrenia pathophysiology and psychopathology, coupled with the species-specificity of much of the symptomatology, places limits on analysis via animal models, in vitro assays, and patient assessment. Multiscale computer modeling complements these traditional modes of study.\n\nMethodsUsing a hippocampal CA3 computer model with 1200 neurons, we examined the effects of alterations in NMDAR, HCN (Ih current), and GABAAR on information flow (measured with normalized transfer entropy), and in gamma activity in local field potential (LFP).\n\nResultsAltering NMDARs, GABAAR, Ih, individually or in combination, modified information flow in an inverted-U shape manner, with information flow reduced at low and high levels of these parameters. The strong information flow seen at the peaks were associated with an intermediate level of synchrony, seen as an intermediate level of gamma activity in the LFP, and an intermediate level of pyramidal cell excitability.\n\nConclusionsOur results are consistent with the idea that overly low or high gamma power is associated with pathological information flow and information processing. These data suggest the need for careful titration of schizophrenia pharmacotherapy to avoid extremes that alter information flow in different ways. These results also identify gamma power as a potential biomarker for monitoring pathology and multi-target pharmacotherapy.\n\nAUTHOR SUMMARYCurrently, there are no good treatments for the cognitive symptoms of schizophrenia. We used a biophysically realistic computational model of hippocampal CA3 to investigate the effect of potential pharmacotherapeutic targets on the dynamics of CA3 activity and information processing to predict multi-target drug treatments for schizophrenia. We found an inverted-U shaped relationship between information flow and drug target manipulations, as well as between information flow and gamma power. Our study suggests that neuronal excitability and synchrony may be tuned between extremes to enhance information flow and information processing. It further predicts the need for careful titration of schizophrenia drugs, whether used individually or in drug cocktails.

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bioRxiv2019

A supragranular nexus for the effects of neocortical beta events on human tactile perception

Transient neocortical events with high spectral power in the 15-29Hz beta band are among the most reliable predictors of sensory perception: High prestimulus beta event rates in primary somatosensory lead to sensory suppression, most effective at 100-300ms prestimulus latency. However, the synaptic and neuronal mechanisms inducing betas perceptual effects have not been completely localized. We combined human MEG with neural modeling designed to account for these macroscale signals to interpret the cellular and circuit mechanisms that underlie the influence of beta on tactile detection. Extending prior studies, we modeled the hypothesis that higher-order thalamic bursts, sufficient for beta event generation in cortex, recruit supragranular GABAB inhibition acting on a 300ms time scale to suppress sensory information. Consistency between model and MEG data supported this hypothesis and led to a further prediction, validated in our data, that stimuli are perceived when beta events occur simultaneously with tactile stimulation. The post-event suppressive mechanism explains an array of studies that associate beta with decreased processing, while the during-event mechanism may demand a reinterpretation of the role of beta events in the context of coincident timing.\n\nSignificance statementSomatosensory beta events - transient 15-29Hz oscillations in electromagnetic recordings - are thought to be generated when \"top-down\" bursts of spikes presumably originating in higher-order thalamus arrive in upper layers of somatosensory cortex. Physiological evidence had shown that the immediate action of these top-down projections should be excitatory; however, after a beta event, sensory perception is noticeably inhibited for approximately 300ms. The source of this post-event sensory suppression, in particular, had been unresolved. Using a detailed computational model of somatosensory cortex, we find evidence for the hypothesis that these bursts couple indirectly to GABAB inhibition in upper layers of cortex, and that beta events first briefly disinhibit sensory relay before a longer period of inhibition.

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bioRxiv2019

Human Neocortical Neurosolver (HNN): A new software tool for interpreting the cellular and network origin of human MEG/EEG data

Magneto- and electro-encephalography (MEG/EEG) non-invasively record human brain activity with millisecond resolution providing reliable markers of healthy and disease states. Relating these macroscopic signals to underlying cellular- and circuit-level generators is a limitation that constrains using MEG/EEG to reveal novel principles of information processing or to translate findings into new therapies for neuropathology. To address this problem, we built Human Neocortical Neurosolver (HNN, https://hnn.brown.edu) software. HNN has a graphical user interface designed to help researchers and clinicians interpret the neural origins of MEG/EEG. HNNs core is a neocortical circuit model that accounts for biophysical origins of electrical currents generating MEG/EEG. Data can be directly compared to simulated signals and parameters easily manipulated to develop/test hypotheses on a signals origin. Tutorials teach users to simulate commonly measured signals, including event related potentials and brain rhythms. HNNs ability to associate signals across scales makes it a unique tool for translational neuroscience research.

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bioRxiv2018

NetPyNE: a tool for data-driven multiscale modeling of brain circuits

Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and disparate experimental datasets at multiple scales. The NetPyNE tool (www.netpyne.org) provides both programmatic and graphical interfaces to develop data-driven multiscale network models in NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide specifications at a high level via a standardized declarative language, e.g., a connectivity rule, instead of tens of loops to create millions of cell-to-cell connections. Users can then generate the NEURON network, run efficiently parallelized simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis - connectivity matrices, voltage traces, raster plots, local field potentials, and information theoretic measures. NetPyNE also facilitates model sharing by exporting and importing using NeuroML and SONATA standardized formats. NetPyNE is already being used to teach computational neuroscience students and by modelers to investigate different brain regions and phenomena.

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bioRxiv2017

Long-range inputs and H-current regulate different modes of operation in a multiscale model of mouse M1 microcircuits

We developed a biophysically detailed multiscale model of mouse primary motor cortex (M1) with over 10,000 neurons and 35 million synapses. We focused on intratelencephalic (IT) and pyramidal-tract (PT) neurons of layer 5 (L5), which were modeled at high multicompartment resolution. Wiring densities were based on prior detailed measures from mouse slice, and depended on cell class and cortical depth at sublaminar resolution. Prominent phase-amplitude-coupled delta and gamma activity emerged from the network. Spectral Granger causality analysis revealed the dynamics of information flow through populations at different frequencies. Stimulation of motor vs sensory long-range inputs to M1 demonstrated distinct intra- and inter-laminar dynamics and PT output. Manipulating PT Ih altered PT activity, supporting the hypothesis that Ih neuromodulation is involved in translating motor planning into execution. Our model sheds light on the multiscale dynamics of cell-type-specific M1 circuits and how connectivity relates to dynamics.

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