Neuronal Ensemble Activity Research Articles

Causal Interrogation of Neuronal Networks and Behavior through Virally Transduced Ivermectin Receptors.

The causal interrogation of neuronal networks involved in specific behaviors requires the spatially and temporally controlled modulation of neuronal activity. For long-term manipulation of neuronal activity, chemogenetic tools provide a reasonable alternative to short-term optogenetic approaches. Here we show that virus mediated gene transfer of the ivermectin (IVM) activated glycine receptor mutant GlyRα1AG can be used for the selective and reversible silencing of specific neuronal networks in mice. In the striatum, dorsal hippocampus, and olfactory bulb, GlyRα1AG promoted IVM dependent effects in representative behavioral assays. Moreover, GlyRα1AG mediated silencing had a strong and reversible impact on neuronal ensemble activity and c-Fos activation in the olfactory bulb. Together our results demonstrate that long-term, reversible and re-inducible neuronal silencing via GlyRα1AG is a promising tool for the interrogation of network mechanisms underlying the control of behavior and memory formation.

Selective attenuation of electrophysiological activity of the dentate gyrus in a social defeat mouse model.

Current research on stress pathology has revealed a set of molecular and cellular mechanisms through which psychosocial stress impairs brain function. However, there are few studies that have examined how chronic stress exposure alters neuronal activity patterns at a network level. Here, we recorded ensemble neuronal activity patterns of the cortico-hippocampal network from urethane-anesthetized mice that were subjected to repeated social defeat stress. In socially defeated mice, the magnitudes of local field potential signals, including theta, slow gamma, and fast gamma oscillations, were significantly reduced in the dentate gyrus, whereas they remained unchanged in the hippocampus and somatosensory cortex. In accordance with the vast majority of histological and biochemical studies, our evidence from electrophysiological investigations highlights the dentate gyrus as a key brain area that is primarily susceptible to stress-induced dysfunction.

Higher-Order Synaptic Interactions Coordinate Dynamics in Recurrent Networks.

Linking synaptic connectivity to dynamics is key to understanding information processing in neocortex. Circuit dynamics emerge from complex interactions of interconnected neurons, necessitating that links between connectivity and dynamics be evaluated at the network level. Here we map propagating activity in large neuronal ensembles from mouse neocortex and compare it to a recurrent network model, where connectivity can be precisely measured and manipulated. We find that a dynamical feature dominates statistical descriptions of propagating activity for both neocortex and the model: convergent clusters comprised of fan-in triangle motifs, where two input neurons are themselves connected. Fan-in triangles coordinate the timing of presynaptic inputs during ongoing activity to effectively generate postsynaptic spiking. As a result, paradoxically, fan-in triangles dominate the statistics of spike propagation even in randomly connected recurrent networks. Interplay between higher-order synaptic connectivity and the integrative properties of neurons constrains the structure of network dynamics and shapes the routing of information in neocortex.

Layer-specific potentiation of network GABAergic inhibition in the CA1 area of the hippocampus.

One of the most important functions of GABAergic inhibition in cortical regions is the tight control of spatiotemporal activity of principal neuronal ensembles. However, electrophysiological recordings do not provide sufficient spatial information to determine the spatiotemporal properties of inhibitory plasticity. Using Voltage Sensitive Dye Imaging (VSDI) in mouse hippocampal slices, we demonstrate that GABAA-mediated field inhibitory postsynaptic potentials undergo layer-specific potentiation upon activation of metabotropic glutamate receptors (mGlu). VSDI recordings allowed detection of pharmacologically isolated GABAA-dependent hyperpolarization signals. Bath-application of the selective group-I mGlu receptor agonist, (S)-3,5-Dihydroxyphenylglycine (DHPG), induces an enhancement of the GABAergic VSDI-recorded signal, which is more or less pronounced in different hippocampal layers. This potentiation is mediated by mGlu5 and downstream activation of IP3 receptors. Our results depict network GABAergic activity in the hippocampal CA1 region and its sub-layers, showing also a novel form of inhibitory synaptic plasticity tightly coupled to glutamatergic activity.

Disruption of Network Synchrony and Cognitive Dysfunction After Traumatic Brain Injury.

Traumatic brain injury (TBI) is a heterogeneous disorder with many factors contributing to a spectrum of severity, leading to cognitive dysfunction that may last for many years after injury. Injury to axons in the white matter, which are preferentially vulnerable to biomechanical forces, is prevalent in many TBIs. Unlike focal injury to a discrete brain region, axonal injury is fundamentally an injury to the substrate by which networks of the brain communicate with one another. The brain is envisioned as a series of dynamic, interconnected networks that communicate via long axonal conduits termed the “connectome”. Ensembles of neurons communicate via these pathways and encode information within and between brain regions in ways that are timing dependent. Our central hypothesis is that traumatic injury to axons may disrupt the exquisite timing of neuronal communication within and between brain networks, and that this may underlie aspects of post-TBI cognitive dysfunction. With a better understanding of how highly interconnected networks of neurons communicate with one another in important cognitive regions such as the limbic system, and how disruption of this communication occurs during injury, we can identify new therapeutic targets to restore lost function. This requires the tools of systems neuroscience, including electrophysiological analysis of ensemble neuronal activity and circuitry changes in awake animals after TBI, as well as computational modeling of the effects of TBI on these networks. As more is revealed about how inter-regional neuronal interactions are disrupted, treatments directly targeting these dysfunctional pathways using neuromodulation can be developed.

The Limited Utility of Multiunit Data in Differentiating Neuronal Population Activity.

To date, single neuron recordings remain the gold standard for monitoring the activity of neuronal populations. Since obtaining single neuron recordings is not always possible, high frequency or ‘multiunit activity’ (MUA) is often used as a surrogate. Although MUA recordings allow one to monitor the activity of a large number of neurons, they do not allow identification of specific neuronal subtypes, the knowledge of which is often critical for understanding electrophysiological processes. Here, we explored whether prior knowledge of the single unit waveform of specific neuron types is sufficient to permit the use of MUA to monitor and distinguish differential activity of individual neuron types. We used an experimental and modeling approach to determine if components of the MUA can monitor medium spiny neurons (MSNs) and fast-spiking interneurons (FSIs) in the mouse dorsal striatum. We demonstrate that when well-isolated spikes are recorded, the MUA at frequencies greater than 100Hz is correlated with single unit spiking, highly dependent on the waveform of each neuron type, and accurately reflects the timing and spectral signature of each neuron. However, in the absence of well-isolated spikes (the norm in most MUA recordings), the MUA did not typically contain sufficient information to permit accurate prediction of the respective population activity of MSNs and FSIs. Thus, even under ideal conditions for the MUA to reliably predict the moment-to-moment activity of specific local neuronal ensembles, knowledge of the spike waveform of the underlying neuronal populations is necessary, but not sufficient.

Homeostatic changes in neuronal network oscillations in response to continuous hypoperfusion in the mouse forebrain

Neuronal activity is highly sensitive to changes in oxygen tension. In this study, we examined the impact of hypoxic/ischemic conditions on neuronal ensemble activity patterns in the mouse brain using in vivo extracellular electrophysiological recordings from up to 8 sites in the thalamus, dorsal hippocampus, and neocortex, while cerebral hypoperfusion was induced by unilateral carotid artery occlusion. After a few minutes, the occlusion triggered a rapid change in the power of the local field oscillations. In the hippocampus, but not in the neocortex, the absolute power changes at all frequency ranges (relative to the baseline) became less pronounced with time, and no significant changes were observed 30min after the occlusion-induced hypoperfusion. We also tested whether continuous hypoperfusion induced by the occlusion for up to 1 week alters neuronal activity. In the hippocampus and the thalamus, the chronic occlusion did not lead to a reduction in the power of the local field oscillations. These results indicate that certain neuronal populations have the ability to maintain internal neurophysiological homeostasis against continuous hypoperfusion.

Nonspatial Sequence Coding in CA1 Neurons.

The ability to remember the order of life events depends on the hippocampus, but the underlying neural mechanisms remain poorly understood. Here we addressed this issue by recording neural activity in hippocampal region CA1 while rats performed a nonspatial sequence memory task. We found that hippocampal neurons code for the temporal context of items (whether odors were presented in the correct or incorrect sequential position) and that this activity is linked with memory performance. The discovery of this novel form of temporal coding in hippocampal neurons advances our fundamental understanding of the neurobiology of episodic memory and will serve as a foundation for our cross-species, multitechnique approach aimed at elucidating the neural mechanisms underlying memory impairments in aging and dementia.

Aberrant Neuronal Activity and Dysfunctional Connectivity in Aβ1-42- mediated Memory Deficits in Rats.

Alzheimer's disease (AD) is a neurodegenerative disorder that gradually induces cognitive deficit. Working memory deficits have been previously reported in AD and Amyloid-β peptide (Aβ) is regarded as a causative factor for the impairments. Accumulating evidence has identified that neuronal ensemble activity as well as functional connectivity among neurons encode and process information in normal brain. How Aβ contributes to the abnormal neuronal ensemble activity and corresponding functional connectivity which, in turn, leads to the cognitive deficits? To address the issue, we simultaneously recorded neuronal activity from medial prefrontal cortex of rats (control and Aβ injected group) when the rats performed a Y-maze working memory task, to investigate the properties of neuronal ensemble activity and functional connectivity. In the control group, a group of neurons were collectively activated and the connectivity among the neurons strengthened during the working memory task. However, in the Aβ group, the neuronal activity and connectivity never experience significant change. Our results indicate that the dysfunction of neuronal activity and connectivity may provide insight for the Aβ-induced working memory deficits.

Decoding bipedal locomotion from the rat sensorimotor cortex

Objective. Decoding forelimb movements from the firing activity of cortical neurons has been interfaced with robotic and prosthetic systems to replace lost upper limb functions in humans. Despite the potential of this approach to improve locomotion and facilitate gait rehabilitation, decoding lower limb movement from the motor cortex has received comparatively little attention. Here, we performed experiments to identify the type and amount of information that can be decoded from neuronal ensemble activity in the hindlimb area of the rat motor cortex during bipedal locomotor tasks. Approach. Rats were trained to stand, step on a treadmill, walk overground and climb staircases in a bipedal posture. To impose this gait, the rats were secured in a robotic interface that provided support against the direction of gravity and in the mediolateral direction, but behaved transparently in the forward direction. After completion of training, rats were chronically implanted with a micro-wire array spanning the left hindlimb motor cortex to record single and multi-unit activity, and bipolar electrodes into 10 muscles of the right hindlimb to monitor electromyographic signals. Whole-body kinematics, muscle activity, and neural signals were simultaneously recorded during execution of the trained tasks over multiple days of testing. Hindlimb kinematics, muscle activity, gait phases, and locomotor tasks were decoded using offline classification algorithms. Main results. We found that the stance and swing phases of gait and the locomotor tasks were detected with accuracies as robust as 90% in all rats. Decoded hindlimb kinematics and muscle activity exhibited a larger variability across rats and tasks. Significance. Our study shows that the rodent motor cortex contains useful information for lower limb neuroprosthetic development. However, brain–machine interfaces estimating gait phases or locomotor behaviors, instead of continuous variables such as limb joint positions or speeds, are likely to provide more robust control strategies for the design of such neuroprostheses.

Inference and Decoding of Motor Cortex Low-Dimensional Dynamics via Latent State-Space Models.

Motor cortex neuronal ensemble spiking activity exhibits strong low-dimensional collective dynamics (i.e., coordinated modes of activity) during behavior. Here, we demonstrate that these low-dimensional dynamics, revealed by unsupervised latent state-space models, can provide as accurate or better reconstruction of movement kinematics as direct decoding from the entire recorded ensemble. Ensembles of single neurons were recorded with triple microelectrode arrays (MEAs) implanted in ventral and dorsal premotor (PMv, PMd) and primary motor (M1) cortices while nonhuman primates performed 3-D reach-to-grasp actions. Low-dimensional dynamics were estimated via various types of latent state-space models including, for example, Poisson linear dynamic system (PLDS) models. Decoding from low-dimensional dynamics was implemented via point process and Kalman filters coupled in series. We also examined decoding based on a predictive subsampling of the recorded population. In this case, a supervised greedy procedure selected neuronal subsets that optimized decoding performance. When comparing decoding based on predictive subsampling and latent state-space models, the size of the neuronal subset was set to the same number of latent state dimensions. Overall, our findings suggest that information about naturalistic reach kinematics present in the recorded population is preserved in the inferred low-dimensional motor cortex dynamics. Furthermore, decoding based on unsupervised PLDS models may also outperform previous approaches based on direct decoding from the recorded population or on predictive subsampling.

Neural population dynamics in human motor cortex during movements in people with ALS.

The prevailing view of motor cortex holds that motor cortical neural activity represents muscle or movement parameters. However, recent studies in non-human primates have shown that neural activity does not simply represent muscle or movement parameters; instead, its temporal structure is well-described by a dynamical system where activity during movement evolves lawfully from an initial pre-movement state. In this study, we analyze neuronal ensemble activity in motor cortex in two clinical trial participants diagnosed with Amyotrophic Lateral Sclerosis (ALS). We find that activity in human motor cortex has similar dynamical structure to that of non-human primates, indicating that human motor cortex contains a similar underlying dynamical system for movement generation.

Copula regression analysis of simultaneously recorded frontal eye field and inferotemporal spiking activity during object-based working memory.

Inferotemporal (IT) neurons are known to exhibit persistent, stimulus-selective activity during the delay period of object-based working memory tasks. Frontal eye field (FEF) neurons show robust, spatially selective delay period activity during memory-guided saccade tasks. We present a copula regression paradigm to examine neural interaction of these two types of signals between areas IT and FEF of the monkey during a working memory task. This paradigm is based on copula models that can account for both marginal distribution over spiking activity of individual neurons within each area and joint distribution over ensemble activity of neurons between areas. Considering the popular GLMs as marginal models, we developed a general and flexible likelihood framework that uses the copula to integrate separate GLMs into a joint regression analysis. Such joint analysis essentially leads to a multivariate analog of the marginal GLM theory and hence efficient model estimation. In addition, we show that Granger causality between spike trains can be readily assessed via the likelihood ratio statistic. The performance of this method is validated by extensive simulations, and compared favorably to the widely used GLMs. When applied to spiking activity of simultaneously recorded FEF and IT neurons during working memory task, we observed significant Granger causality influence from FEF to IT, but not in the opposite direction, suggesting the role of the FEF in the selection and retention of visual information during working memory. The copula model has the potential to provide unique neurophysiological insights about network properties of the brain.

Local field potentials in primate motor cortex encode grasp kinetic parameters

Reach and grasp kinematics are known to be encoded in the spiking activity of neuronal ensembles and in local field potentials (LFPs) recorded from primate motor cortex during movement planning and execution. However, little is known, especially in LFPs, about the encoding of kinetic parameters, such as forces exerted on the object during the same actions. We implanted two monkeys with microelectrode arrays in the motor cortical areas MI and PMd to investigate encoding of grasp-related parameters in motor cortical LFPs during planning and execution of reach-and-grasp movements. We identified three components of the LFP that modulated during grasps corresponding to low (0.3–7Hz), intermediate (~10–~40Hz) and high (~80–250Hz) frequency bands. We show that all three components can be used to classify not only grip types but also object loads during planning and execution of a grasping movement. In addition, we demonstrate that all three components recorded during planning or execution can be used to continuously decode finger pressure forces and hand position related to the grasping movement. Low and high frequency components provide similar classification and decoding accuracies, which were substantially higher than those obtained from the intermediate frequency component. Our results demonstrate that intended reach and grasp kinetic parameters are encoded in multiple LFP bands during both movement planning and execution. These findings also suggest that the LFP is a reliable signal for the control of parameters related to object load and applied pressure forces in brain–machine interfaces.

Attentional Filtering of Visual Information by Neuronal Ensembles in the Primate Lateral Prefrontal Cortex

The activity of neurons in the primate lateral prefrontal cortex (LPFC) is strongly modulated by visual attention. Such a modulation has mostly been documented by averaging the activity of independently recorded neurons over repeated experimental trials. However, in realistic settings, ensembles of simultaneously active LPFC neurons must generate attentional signals on a single-trial basis, despite the individual and correlated variability of neuronal responses. Whether, under these circumstances, the LPFC can reliably generate attentional signals is unclear. Here, we show that the simultaneous activity of neuronal ensembles in the primate LPFC can be reliably decoded to predict the allocation of attention on a single-trial basis. Decoding was sensitive to the noise correlation structure of the ensembles. Additionally, it was resilient to distractors, predictive of behavior, and stable over weeks. Thus, LPFC neuronal ensemble activity can reliably encode attention within behavioral time frames, despite the noisy and correlated nature of neuronal activity.

How many neural oscillators we need on sub- and supra-second intervals processing in the primate brain

Processing of time intervals in short tem-poral ranges, typically in sub- and supra-second intervals, is vital for the survivalof primates and humans. Research evi-dence has already shown that the process-ing of sub- and supra-second intervals issubserved by different neural mechanisms(Buhusi and Meck, 2005). In the domainof time perception, one outstanding sci-entific question remains to be solved iswhether the interval processing is imple-mented by a central clock (supra-sensory)or specific clock (neural state-specific)representation (Ivry and Schlerf, 2008).Gupta (2014) initiated theoretical explo-rations by introducing different types ofneuronal oscillators, with three key com-ponents:pacemakerneurons,tonicinputs,and synchronized excitation/inhibition ofinter-connected neurons (Gupta, 2014).Gupta further featured three characteris-ticfactorsinanintegrated model:(1).Theencoding of the time interval is reflectedby frequency modulated neuronal spikesor spikebursts affiliated with thetemporalpoints of a given interval. (2). Our brainembraces active calibration mechanismsamong multiple oscillators and modularconnections, which could to a large extentpitagainstthelossofintervaltimingfunc-tions of the brain. (3). The neuronal oscil-lators have solid foundations in neuralsubstrates that typically characterize sen-sorimotor timing, from the changes ofmembrane potential to the functioningof high-level brain structures (Teki et al.,2011; Santos et al., 2014).The concept of the building block for“neural oscillators” in Gupta (2014) inessence could be extended and interpretedin different contexts: Firstly, the “neu-ronaltemporalunits”in, Gupta (2014)in contrast to the traditional “pacemaker-accumulator” model (Treisman, 1963;Treisman et al., 1990, 1994; Wittmann,1999), is a machinery device (inherent inthebrain)thatprocessesthetimeinforma-tion. Secondly, the “oscillators” entail the“modular” nature of neuronal clocks, byemphasizing the correspondence betweenneural substrates in temporal bindingsbetween different sensory events. In thisway and from a broad perspective, thismodel dovetails the tenets of “intrin-sic model” (Ivry and Schlerf, 2008), butallows mechanistic flexibility for the inter-actions between separate clocks. Thirdly,the oscillators could demonstrate higher-level ensemble neuronal activities (butwere relatively less addressed in cur-rentmodel),includingoscillatorygamma-band responses (Herrmann et al., 2004;Tallon-Baudry and Bertrand, 1999).Movement is the expressed time (Tekiet al., 2011). The Theoretical modelfor Gupta (2014) stems from the com-mon setting of “movement control” butencourages more potential investigationstocome.Forexample,itcouldbeextendedand validated in other timing scenarios—go beyond the area of motor control—todemonstrate its robustness. Moreover, atypicalfunctionofhumantimingsystemisto predict the imminent events and hencemake efficient perceptual decision mak-ing. The current oscillation model leavesmuch room to be improved by revealinghowtheoscillatorscouldworkdirectlyinaframework of predictive coding. This pre-dictivecodingissomehowfreeoffeedbackprocesses as assumed in Gupta (2014).Furthermore, a high-level processing ofrhythm/periodicity-based upon the infor-mation of sub- or supra-second intervals,might mobilizepopulation/ensemble neu-ronal coding as well as impose more com-plexinter-connections/overlappings ofthesupposed neuronal temporal units (withmultiple neuronal feedback loops). WhatGupta (2014) proposed essentially indi-cated a potential panorama of neuraloscillators that could address the com-plexity of timing behavior. However,how many (levels of) oscillators areneeded to elucidate those timing behav-iors and how local lateral excitatory con-nectivity (Gavornik and Shouval, 2011),interacting with the global (stochastic)processingofbeattiming,warrantsfurtherexplorations.

Shaping inhibition: activity dependent structural plasticity of GABAergic synapses.

Inhibitory transmission through the neurotransmitter γ-aminobutyric acid (GABA) shapes network activity in the mammalian cerebral cortex by filtering synaptic incoming information and dictating the activity of principal cells. The incredibly diverse population of cortical neurons that use GABA as neurotransmitter shows an equally diverse range of mechanisms that regulate changes in the strength of GABAergic synaptic transmission and allow them to dynamically follow and command the activity of neuronal ensembles. Similarly to glutamatergic synaptic transmission, activity-dependent functional changes in inhibitory neurotransmission are accompanied by alterations in GABAergic synapse structure that range from morphological reorganization of postsynaptic density to de novo formation and elimination of inhibitory contacts. Here we review several aspects of structural plasticity of inhibitory synapses, including its induction by different forms of neuronal activity, behavioral and sensory experience and the molecular mechanisms and signaling pathways involved. We discuss the functional consequences of GABAergic synapse structural plasticity for information processing and memory formation in view of the heterogenous nature of the structural plasticity phenomena affecting inhibitory synapses impinging on somatic and dendritic compartments of cortical and hippocampal neurons.

Acute neuroinflammation impairs context discrimination memory and disrupts pattern separation processes in hippocampus.

Although it is known that immune system activation can impair cognition, no study to date has linked cognitive deficits during acute neuroinflammation to dysregulation of task-relevant neuronal ensemble activity. Here, we assessed both neural circuit activity and context discrimination memory retrieval, in a within-subjects design, of male rats given systemic administration of saline or lipopolysaccharide (LPS). Rats were exposed over several days to two similar contexts: one of which was paired with weak foot shock and the other was not. After reaching criteria for discriminative freezing, rats were given systemic LPS or saline injection and tested for retrieval of context discrimination 6 h later. Importantly, LPS administration produced an acute neuroinflammatory response in dorsal hippocampus at this time (as assessed by elevation of proinflammatory cytokine mRNA levels) and abolished retrieval of the previously acquired discrimination. The impact of neuroinflammation on hippocampal CA3 and CA1 neural circuit activity was assessed using the Arc/Homer1a cellular analysis of temporal activity by fluorescence in situ hybridization imaging method. Whereas the saline-treated subjects discriminated and had low overlap of hippocampal ensembles activated in the two contexts, LPS-treated subjects did not discriminate and had greater ensemble overlap (i.e., reduced orthogonalization). Additionally, retrieval of standard contextual fear conditioning, which does not require context discrimination, was not affected by pretesting LPS administration. Together, the behavioral and circuit analyses data provide compelling evidence that LPS administration impairs context discrimination memory by disrupting cellular pattern separation processes within the hippocampus, thus linking acute neuroinflammation to disruption of specific neural circuit functions and cognitive impairment.

Investigating local and long-range neuronal network dynamics by simultaneous optogenetics, reverse microdialysis and silicon probe recordings in vivo.

The advent of optogenetics has given neuroscientists the opportunity to excite or inhibit neuronal population activity with high temporal resolution and cellular selectivity. Thus, when combined with recordings of neuronal ensemble activity in freely moving animals optogenetics can provide an unprecedented snapshot of the contribution of neuronal assemblies to (patho)physiological conditions in vivo. Still, the combination of optogenetic and silicone probe (or tetrode) recordings does not allow investigation of the role played by voltage- and transmitter-gated channels of the opsin-transfected neurons and/or other adjacent neurons in controlling neuronal activity. We demonstrate that optogenetics and silicone probe recordings can be combined with intracerebral reverse microdialysis for the long-term delivery of neuroactive drugs around the optic fiber and silicone probe. In particular, we show the effect of antagonists of T-type Ca(2+) channels, hyperpolarization-activated cyclic nucleotide-gated channels and metabotropic glutamate receptors on silicone probe-recorded activity of the local opsin-transfected neurons in the ventrobasal thalamus, and demonstrate the changes that the block of these thalamic channels/receptors brings about in the network dynamics of distant somatotopic cortical neuronal ensembles. This is the first demonstration of successfully combining optogenetics and neuronal ensemble recordings with reverse microdialysis. This combination of techniques overcomes some of the disadvantages that are associated with the use of intracerebral injection of a drug-containing solution at the site of laser activation. The combination of reverse microdialysis, silicone probe recordings and optogenetics can unravel the short and long-term effects of specific transmitter- and voltage-gated channels on laser-modulated firing at the site of optogenetic stimulation and the actions that these manipulations exert on distant neuronal populations.

Modelling of neocortical neural dynamics during human focal seizures

Recent advances in microelectrode array (MEA) recordings have made possible the simultaneous recordings of spiking activity in ensembles of neocortical single neurons during human focal seizures. As a result, a much more detailed picture of seizure initiation and spread has begun to emerge [1]. Here, we combine single neuron spiking and local field potential (LFP) data obtained from 96-MEA recordings with mathematical modelling to characterize the synaptic coupling and parameters required to sustain the neural dynamics observed in the two main types of epileptic seizures identified in the studied patients: spike-wave-complex (SWCs ~2-3 Hz) seizures and gamma-band (40-60 Hz) seizures, respectively. We model the neocortex under a 96-MEA (4mm x 4mm) with Izhikevich type neurons [2,3]. There are 28x28 minicolumns in the model. Each minicolumn is 25μm apart from each other and has 12 excitatory neurons and 4 interneurons. The neurons are synaptically connected up to a radius of 75μm and a synaptic delay of 2ms. (Connectivity drops to zero close to the MEA boundaries). Each neuron is driven by an independent Poisson input simulating background synaptic activities (i.e. high Poisson rates close to a few kHz, but individual Poisson events are of small amplitude). We simulate each of the two types of epileptic seizures by controlling the synaptic strengths amongst neurons and the Poisson drive. Dynamics similar to that in gamma-band seizures (Fig. ​(Fig.1)1) were obtained in models that combined strong mutual inhibitory synaptic couplings (gi->i), strong inhibitory to excitatory synaptic couplings (gi->e), and strong Poisson drive to both the pyramidal and interneuronal populations. The resulting single neuron spiking patterns (in both the inhibitory and excitatory cases) remained irregular, despite that they lead to sustained narrow gamma band LFP oscillations, consistent with the observations from the neuronal spiking data recorded from the patients. Dynamics similar to SWC seizures were obtained by decreasing both mutual inhibitory coupling (gi->i ) and inhibitory to excitatory coupling (gi->e), as well as by reducing the Poisson drive to the inhibitory interneuron populations. During each SWC discharge (Figure ​(Figure1),1), we observed the spread of neuronal spiking activity and recruitment of pyramidal cells starting from a focus, accompanied by strong coherent LFP. In the model, the participation of interneurons in SWC generation is minimal due to the weak gi->i,e and low Poisson drive. Our model suggests that gamma-band seizures require strong inhibitory conductance and participation of both excitatory and inhibitory populations, while SWC seizures require weaker inhibitory conductances and are mostly driven by excitatory (pyramidal) neurons. We are working towards a conductance-based network model in which intra and extracellular ionic concentrations are included to explore how disregulation of ionic concentrations can trigger each of the two types of seizures. Figure 1 Neuronal spiking raster plots (gamma-band seizure: left; SWC seizure: right).