Biology subjects

Lytton, W. W.

Publications and source records attributed to Lytton, W. W..

At least 19 recordsLinked to original sources

bioRxiv2026

Excitatory delay-coupling explains in-phase and antiphase functional connectivity

Coordinated oscillatory activity between brain regions underpins cognition, yet the phase relationships governing this coordination remain poorly understood. Using scalp EEG from 31 participants performing a motor learning task, we show that inter-site phase clustering occurs predominantly at in-phase or antiphase relationships, with the transition between them governed by conduction delay. Homologous interhemispheric pairs remain in-phase despite long distances, consistent with faster callosal conduction. A minimal model of two delay-coupled excitatory populations reproduces these features without parameter tuning; stability analysis shows that in-phase and antiphase oscillations arise from competing instabilities, with delay determining which dominates. Task performance shifts a frontoparietal network toward in-phase connectivity, a modulation captured by the Phase Relationship Index (PRI) but missed by conventional clustering metrics. Preliminary evidence from independent datasets suggests these patterns generalise. These findings challenge the assumption that zero-lag connectivity reflects volume conduction, offer a mechanistic account linking conduction delay to phase organisation, and introduce PRI as a metric sensitive to functionally relevant connectivity changes invisible to existing measures.

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bioRxiv2025

Computer models predict differential dendritic vulnerability with ischemia and spreading depression

Ischemia, whether abrupt or chronic, limits ATP production and disrupts ATP-dependent homeostatic mechanisms, leading to alterations in both intracellular and extracellular ion concentrations. Inadequate neuronal ATP triggers K+ release and increased extracellular K+ depolarizes neurons, leading to additional K+ release; this positive feedback phenomenon is known as spreading depolarization (SD). When the depolarizing effects are strong enough, the cells undergo depolarization blockade, known as spreading depression. Excess extracellular K+ increases energy demand from the Na+-K+ pump, producing a pathological confluence of increased demand with reduced delivery of energy. The resulting changes have profound effects at subcellular, cellular, and network scales of brain function. We hypothesized that consequences of ischemic or SD homeostatic failure would differ on the subcellular scale, with differences between disjunct dendritic regions of a hippocampal CA1 pyramidal neuron. To evaluate the interplay between morphology and ion concentrations, we used a mechanistic simulation incorporating neuronal morphology, pumps, exchangers, voltage-, and Ca2+-sensitive ion channels. In both cases, calcium accumulation was greatest in the basilar dendrites, suggesting these dendrites would show the greatest effects of excitotoxicity. By contrast, ischemia, but not SD showed that distal apical dendrites were exposed to greater intracellular chloride concentrations, which may lead to dendritic beading.

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bioRxiv2025

Self-organized and self-sustained ensemble activity patterns in simulation of mouse primary motor cortex

The idea of self-organized signal processing in the cerebral cortex has become a focus of research since Beggs and Plentz 1 reported avalanches in local field potential recordings from organotypic cultures and acute slices of rat somatosensory cortex. How the cortex intrinsically organizes signals remains unknown. A current hypothesis was proposed by the condensed matter physicists Bak, Tang, and Wiesenfeld 2 when they conjectured that if neuronal avalanche activity followed inverse power law distributions, then brain activity may be set around phase transitions within self-organized signals. We asked if we would observe self-organized signals in an isolated slice of our data driven detailed simulation of the mouse primary motor cortex? If we did, would we observe avalanches with power-law distributions in size and duration and what would they look like? Our results demonstrate that a brief unstructured stimulus (100ms, 57A current) to a small subset of neurons (about 181 of more than 10,000) in a simulated mouse primary motor cortex slice results in self-organized and self-sustained avalanches with power-law size and duration distributions and values similar to those reported from in vivo and in vitro experiments. We observed 4 cross-layer and cross-neuron population patterns, 3 of which displayed a dominant rhythmic component. Avalanches were each composed of one or more of the 4 population patterns.

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bioRxiv2024

Computational Models of Age-Associated Cognitive Slowing

BackgroundCognitive slowing accompanies normal aging, yet understanding of the mechanisms of slowing is limited at the network and neuronal level. Relating the pathophysiological factors responsible for cognitive slowing, and interpreting its relationship to working memory, requires multiscale computer modeling. ObjectiveThe aim of this research is to explore multiple mechanisms of cognitive slowing using computational modeling of the cortex to link neuronal activity with cognitive content. MethodWe developed multiscale computer models of a simple cognitive task - Condition 1 of the Stroop recognition task - using the Nengo system, a cognitive simulation environment with a semantic pointer architecture developed to model cognitive tasks using spiking neural networks. We explored how changes associated with aging such as increased input noise, axonal loss, neuronal loss, and feedback would affect the function of the models. ResultsAxonal loss and increased input noise produced profound slowing. High levels of neuronal loss severely impaired memory and paradoxically decreased slowing via the ability to respond more quickly by "releasing" a prior memory. Increased feedback improved memory at the cost of increased slowing. ConclusionOur simulations suggest that significant slowing could be caused by white matter loss (axonal loss) or input signal degradation (which could be caused by visual or other afferent system worsening). As neuronal loss markedly decreased the duration of working memory, we propose that physiological feedback is increased to preserve working memory at the cost of further cognitive slowing.

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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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bioRxiv2024

Decreased cellular excitability of pyramidal tract neurons in primary motor cortex leads to paradoxically increased network activity in simulated parkinsonian motor cortex

Primary motor cortex (M1) layer 5B pyramidal tract (PT5B) neurons develop intrinsic pathology in rodent and primate Parkinsons disease (PD) models. We used computer simulation to predict how decreased PT5B neuron excitability, identified with current injection in vitro, would change activity patterns of the M1 network. Using NEURON/NetPyNE, we implemented computer simulations of PT5B neurons based on control and 6-OHDA-treated mouse slice data. Parkinsonian PT5B neurons, in an otherwise unmodified simulated M1 network, produced major changes in LFP oscillatory power: an order of magnitude increase in beta band power around 15 Hz in the rest state. This demonstrated that relatively small changes in PT5B neuron excitability would alter oscillatory patterns of activity throughout the M1 circuit, increasing beta band power, a signature of PD pathophysiology. Dysfunction in PT5B neurons, the final-common-pathway to brainstem and spinal cord, provides a new target to treat PD motor symptoms.

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bioRxiv2022

Modernizing the NEURON Simulator for Sustainability, Portability, and Performance

AO_SCPLOWBSTRACTC_SCPLOWThe need for reproducible, credible, multiscale biological modeling has led to the development of standardized simulation platforms, such as the widely-used NEURON environment for computational neuroscience. Developing and maintaining NEURON over several decades has required attention to the competing needs of backwards compatibility, evolving computer architectures, the addition of new scales and physical processes, accessibility to new users, and efficiency and flexibility for specialists. In order to meet these challenges, we have now substantially modernized NEURON, providing continuous integration, an improved build system and release workflow, and better documentation. With the help of a new source-to-source compiler of the NMODL domain-specific language we have enhanced NEURONs ability to run efficiently, via the CoreNEURON simulation engine, on a variety of hardware platforms, including GPUs. Through the implementation of an optimized in-memory transfer mechanism this performance optimized backend is made easily accessible to users, providing training and model-development paths from laptop to workstation to supercomputer and cloud platform. Similarly, we have been able to accelerate NEURONs reaction-diffusion simulation performance through the use of just-in-time compilation. We show that these efforts have led to a growing developer base, a simpler and more robust software distribution, a wider range of supported computer architectures, a better integration of NEURON with other scientific workflows, and substantially improved performance for the simulation of biophysical and biochemical models.

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bioRxiv2022

Large-scale biophysically detailed model of somatosensory thalamocortical circuits in NetPyNE

The primary somatosensory cortex (S1) of mammals is critically important in the perception of touch and related sensorimotor behaviors. In 2015, the Blue Brain Project developed a groundbreaking rat S1 microcircuit simulation with over 31,000 neurons with 207 morpho-electrical neuron types, and 37 million synapses, incorporating anatomical and physiological information from a wide range of experimental studies. We have implemented this highly-detailed and complex S1 model in NetPyNE, using the data available in the Neocortical Microcircuit Collaboration Portal. NetPyNE provides a Python high-level interface to NEURON and allows defining complicated multiscale models using an intuitive declarative standardized language. It also facilitates running parallel simulations, automates the optimization and exploration of parameters using supercomputers, and provides a wide range of built-in analysis functions. This will make the S1 model more accessible and simpler to scale, modify and extend in order to explore research questions or interconnect to other existing models. Despite some implementation differences, the NetPyNE model preserved the original cell morphologies, electrophysiological responses and spatial distribution for all 207 cell types; and the connectivity properties of all 1941 pathways, including synaptic dynamics and short-term plasticity (STP). The NetPyNE S1 simulations produced reasonable physiological firing rates and activity patterns across all populations. The network generated a 1 Hz oscillation comparable to the original model in vitro-like state. By then reducing the extracellular calcium concentration, the model reproduced the original S1 in vivo-like states with asynchronous activity. These results validate the original study using a new modeling tool. Simulated local field potentials (LFPs) exhibited realistic oscillatory patterns and features, including distance- and frequency-dependent attenuation. The model was extended by adding thalamic circuits, including 6 distinct thalamic populations with intrathalamic, thalamocortical and corticothalamic connectivity derived from experimental data. The thalamic model reproduced single known cell and circuit-level dynamics, including burst and tonic firing modes and oscillatory patterns, providing a more realistic input to cortex and enabling study of thalamocortical interactions. Overall, our work provides a widely accessible, data-driven and biophysically-detailed model of the somatosensory thalamocortical circuits that can be employed as a community tool for researchers to study neural dynamics, function and disease.

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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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bioRxiv2022

Multiscale computer modeling of spreading depolarization in brain slices

Spreading depolarization (SD) is a slow-moving wave of neuronal depolarization accompanied by a breakdown of ion concentration homeostasis, followed by long periods of neuronal silence (spreading depression), and associated with several neurological conditions. We developed multiscale (ions to tissue slice) computer models of SD in brain slices using the NEURON simulator: 36,000 neurons (2 voltage-gated ion channels; 3 leak channels; 3 ion exchangers/pumps) in the extracellular space (ECS) of a slice (1 mm sides, varying thickness) with ion (K+, Cl-, Na+) and O2 diffusion and equilibration with a surrounding bath. Glia and neurons cleared K+ from the ECS via Na+/K+ pumps. SD propagated through the slices at realistic speeds of 2-4 mm/min, which increased by as much as 50% in models incorporating the effects of hypoxia or propionate. In both cases, the speedup was mediated principally by ECS shrinkage. Our model allows us to make testable predictions, including: 1. SD can be inhibited by enlarging ECS volume; 2. SD velocity will be greater in areas with greater neuronal density, total neuronal volume, or larger/more dendrites; 3. SD is all-or-none: initiating K+ bolus properties have little impact on SD speed; 4. Slice thickness influences SD due to relative hypoxia in the slice core, exacerbated by SD in a pathological cycle; 5. SD and high neuronal spike rates will be observed in the core of the slice. Cells in the periphery of the slice near an oxygenated bath will resist SD. SignificanceSpreading depolarization (SD) is a slow moving wave of electrical and ionic imbalances in brain tissue and is a hallmark of several neurological disorders. We developed a multiscale computer model of brain slices with realistic neuronal densities, ions, and oxygenation. Our model shows that SD is exacerbated by and causes hypoxia, resulting in strong SD dependence on slice thickness. Our model also predicts that the velocity of SD propagation is not dependent on its initiation, but instead on tissue properties, including the amount of extracellular space and the total area of neuronal membrane, suggesting faster SD following ischemic stroke or traumatic brain injury.

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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

Multiscale computer model of the spinal dorsal horn reveals changes in network processing associated with chronic pain

Pain-related sensory input is processed in the spinal dorsal horn (SDH) before being relayed to the brain. That processing profoundly influences whether stimuli are correctly or incorrectly perceived as painful. Significant advances have been made in identifying the types of excitatory and inhibitory neurons that comprise the SDH, and there is some information about how neuron types are connected, but it remains unclear how the overall circuit processes sensory input or how that processing is disrupted under chronic pain conditions. To explore SDH function, we developed a computational model of the circuit that is tightly constrained by experimental data. Our model comprises conductance-based neuron models that reproduce the characteristic firing patterns of spinal neurons. Excitatory and inhibitory neuron populations, defined by their expression of genetic markers, spiking pattern, or morphology, were synaptically connected according to available qualitative data. Using a genetic algorithm, synaptic weights were tuned to reproduce projection neuron firing rates (model output) based on primary afferent firing rates (model input) across a range of mechanical stimulus intensities. Disparate synaptic weight combinations could produce equivalent circuit function, revealing degeneracy that may underlie heterogeneous responses of different circuits to perturbations or pathological insults. To validate our model, we verified that it responded to reduction of inhibition (i.e. disinhibition) and ablation of specific neuron types in a manner consistent with experiments. Thus validated, our model offers a valuable resource for interpreting experimental results and testing hypotheses in silico to plan experiments for examining normal and pathological SDH circuit function. Significance StatementWe developed a multiscale computer model of the posterior part of spinal cord gray matter (spinal dorsal horn), involved in perception of touch and pain. The model reproduces several experimental observations and makes predictions about how specific types of spinal neurons and synapses influence projection neurons that send information to the brain. Misfiring of these projection neurons can produce anomalous sensations associated with chronic pain. Our computer model will not only assist in planning future experiments, but will also be useful for developing new pharmacotherapy for chronic pain disorders, connecting the effect of drugs acting at the molecular scale with emergent properties of neurons and circuits that shape the pain experience.

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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

Local Glutamate-Mediated Dendritic Plateau Potentials Change the State of the Cortical Pyramidal Neuron

Dendritic spikes in thin dendritic branches (basal and oblique dendrites) of pyramidal neurons are traditionally inferred from spikelets measured in the cell body. Here, we used laser-spot voltage-sensitive dye imaging in cortical pyramidal neurons (rat brain slices) to investigate the voltage waveforms of dendritic potentials occurring in response to spatially-restricted glutamatergic inputs. Local dendritic potentials lasted 200-500 ms and propagated to the cell body where they caused sustained 10-20 mV depolarizations. Plateau potentials propagating from dendrite to soma, and action potentials propagating from soma to dendrite, created complex voltage waveforms in the middle of the thin basal dendrite, comprised of local sodium spikelets, local plateau potentials, and back-propagating action potentials, superimposed on each other. Our model replicated these experimental observations and made predictions, which were tested in experiments. Dendritic plateau potentials occurring in basal and oblique branches put pyramidal neurons into an activated neuronal state ("prepared state"), characterized by depolarized membrane potential and faster membrane responses. The prepared state provides a time window of 200-500 ms during which cortical neurons are particularly excitable and capable of following afferent inputs. At the network level, this predicts that sets of cells with simultaneous plateaus would provide cellular substrate for the formation of functional neuronal ensembles. New & NoteworthyIn cortical pyramidal neurons, we recorded glutamate-mediated dendritic plateau potentials using voltage imaging, and created a computer model that recreated experimental measures from dendrite and cell body. Our model made new predictions, which were then tested in experiments. Plateau potentials profoundly change neuronal state -- a plateau potential triggered in one basal dendrite depolarizes the soma and shortens membrane time constant, making the cell more susceptible to firing triggered by other afferent inputs.

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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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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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