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Dura-Bernal, Salvador

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Salvador Dura-Bernal is an Assistant Professor in the Physiology and Pharmacology department at SUNY Downstate, and a Research Scientist at the Nathan Kline Institute for Psychiatric Research. He completed his PhD and first postdoc at the University of Plymouth, UK; followed by postdocs at Johns Hopkins and SUNY Downstate. His research focuses on understanding the brain cortical circuits through large-scale biophysically-detailed simulations. Dr. Dura-Bernal developed a software tool for multiscale modeling of brain circuits, which has been used in over 40 labs worldwide. The models provide insights into cortical dynamics and disease, and help develop new hypotheses, guide experimentation and evaluate new treatments, including brain machine interfaces. He received the 2019 Robert Furchgott Scholar Award, an early career investigator prize; the 2017 Best Use of AI Award from HPCwire, a leading supercomputing publication; and has been recently selected as a Google Cloud Research Innovator. Dura-Bernal is currently the PI in 3 grants funded by the National Institutes of Health (NIH), the National Science Foundation (NSF) and the NY State Spinal Cord Injury Board.
Institutional profile
Computational Neuroscience In the Dura-Bernal lab, we use computational simulations and mathematical models to better understand neural brain function and disorders, and help develop novel treatments. We build and simulate large-scale biophysically detailed models of the brain neural circuits linking multiple scales: molecules, cells, networks and systems. We run these simulations on supercomputers to reproduce experimental data and make predictions. We develop software tools to facilitate building these models, including NetPyNE, which has been used in over 40 labs (netpyne.org). We also use machine learning to optimize, analyze and interpret neuroscience data, and develop brain-machine interfaces based on these biologically-detailed circuit models. Multiscale Modeling of Cortical Circuits Neural coding mechanisms from molecules to behavior: We integrate experimental data into detailed models to study the interactions across scales (molecular, cellular, circuit and behavior) and their role in brain function and disease. For example, how the interactions between long-range thalamocortical inputs and noradrenergic modulation of HCN current regulates M1 dynamics associated with behavior. We use models to identify and characterize neural coding mechanisms within and across scales, including synchronous firing of neuronal ensembles, phase-amplitude oscillatory coupling, dendritic and network resonance, neuromodulatory interactions (e.g. noradrenaline and dopamine), neuronal avalanches, and information flow. Models of thalamocortical circuits: We developed a model of mouse primary motor cortex circuits containing over 10,000 biophysically and morphologically realistic neurons and 30 million synapses, with neuronal density, distribution and synaptic connectivity patterns derived from experimental data; and validated different cell type-specific dynamics associated with behavior against in vivo data. In collaboration with the Nathan Kline Institute, we built a biologically-detailed model of macaque auditory thalamocortical circuits, receiving auditory input from a model of cochlea and inferior colliculus, to investigate the mechanistic origins of spatiotemporal oscillatory patterns observed in vivo. Preliminary simulation results matched in vivo macaque firing rates and LFP/CSD oscillations.

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Now showing 1 - 10 of 29
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    PublicationOpen Access
    Multiscale model of primary motor cortex circuits predicts in vivo cell-type-specific, behavioral state-dependent dynamics
    (Elsevier BV, 2023-06) Dura-Bernal, Salvador; Neymotin, Samuel A.; Suter, Benjamin A.; Dacre, Joshua; Moreira, Joao V.S.; Urdapilleta, Eugenio; Schiemann, Julia; Duguid, Ian; Shepherd, Gordon M.G.; Lytton, William W.
    Understanding cortical function requires studying multiple scales: molecular, cellular, circuit, and behavioral. We develop a multiscale, biophysically detailed 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 are constrained by experimental data. The model includes long-range inputs from seven thalamic and cortical regions and noradrenergic inputs. Connectivity depends on cell class and cortical depth at sublaminar resolution. The model accurately predicts 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 blockade and thalamus inactivation). We generate mechanistic hypotheses underlying the observed activity and analyzed low-dimensional population latent dynamics. 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 several experimental conditions and behaviors.
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    PublicationOpen Access
    Geppetto: a reusable modular open platform for exploring neuroscience data and models.
    (2018-09-10) Cantarelli, Matteo; Marin, Boris; Quintana, Adrian; Earnshaw, Matt; Court, Robert; Gleeson, Padraig; Dura-Bernal, Salvador; Silver, R Angus; Idili, Giovanni
    Geppetto is an open-source platform that provides generic middleware infrastructure for building both online and desktop tools for visualizing neuroscience models and data and managing simulations. Geppetto underpins a number of neuroscience applications, including Open Source Brain (OSB), Virtual Fly Brain (VFB), NEURON-UI and NetPyNE-UI. OSB is used by researchers to create and visualize computational neuroscience models described in NeuroML and simulate them through the browser. VFB is the reference hub for neural anatomy and imaging data including neuropil, segmented neurons, microscopy stacks and gene expression pattern data. Geppetto is also being used to build a new user interface for NEURON, a widely used neuronal simulation environment, and for NetPyNE, a Python package for network modelling using NEURON. Geppetto defines domain agnostic abstractions used by all these applications to represent their models and data and offers a set of modules and components to integrate, visualize and control simulations in a highly accessible way. The platform comprises a backend which can connect to external data sources, model repositories and simulators together with a highly customizable frontend.This article is part of a discussion meeting issue 'Connectome to behaviour: modelling at cellular resolution'.
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    PublicationOpen Access
    Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis.
    (2017-05-23) Dura-Bernal, S; Neymotin, S A; Kerr, C C; Sivagnanam, S; Majumdar, A; Francis, J T; Lytton, W W
    Biomimetic simulation permits neuroscientists to better understand the complex neuronal dynamics of the brain. Embedding a biomimetic simulation in a closed-loop neuroprosthesis, which can read and write signals from the brain, will permit applications for amelioration of motor, psychiatric, and memory-related brain disorders. Biomimetic neuroprostheses require real-time adaptation to changes in the external environment, thus constituting an example of a dynamic data-driven application system. As model fidelity increases, so does the number of parameters and the complexity of finding appropriate parameter configurations. Instead of adapting synaptic weights via machine learning, we employed major biological learning methods: spike-timing dependent plasticity and reinforcement learning. We optimized the learning metaparameters using evolutionary algorithms, which were implemented in parallel and which used an island model approach to obtain sufficient speed. We employed these methods to train a cortical spiking model to utilize macaque brain activity, indicating a selected target, to drive a virtual musculoskeletal arm with realistic anatomical and biomechanical properties to reach to that target. The optimized system was able to reproduce macaque data from a comparable experimental motor task. These techniques can be used to efficiently tune the parameters of multiscale systems, linking realistic neuronal dynamics to behavior, and thus providing a useful tool for neuroscience and neuroprosthetics.
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    PublicationOpen Access
    Effects of and TASK-like shunting current on dendritic impedance in layer 5 pyramidal-tract neurons.
    (2021-03-10) Kelley, Craig; Dura-Bernal, Salvador; Neymotin, Samuel A; Antic, Srdjan D; Carnevale, Nicholas T; Migliore, Michele; Lytton, William W
    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 multicompartment 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 hyperpolarization-activated cyclic nucleotide-gated (HCN) channels and a shunting current, such as that produced by Twik-related acid-sensitive K (TASK) channels. TASK-like channel density in this model was proportional to local HCN channel density. We found that although 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 HCN channel current () and a TASK-like shunting current shape synaptic potentials and produce degeneracy in dendritic impedance profiles, wherein different combinations of and shunting current can produce the same impedance profile. We simulated chirp current stimulation in the apical dendrites of 5 biophysically detailed multicompartment 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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    PublicationOpen Access
    Multiscale Computer Model of the Spinal Dorsal Horn Reveals Changes in Network Processing Associated with Chronic Pain.
    (2022-03-01) Medlock, Laura; Sekiguchi, Kazutaka; Hong, Sungho; Dura-Bernal, Salvador; Lytton, William W; Prescott, Steven A
    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 pathologic insults. To validate our model, we verified that it responded to the 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 to plan experiments for examining normal and pathologic SDH circuit function. We developed a multiscale computer model of the posterior part of spinal cord gray matter (spinal dorsal horn), which is involved in perceiving 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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    PublicationOpen Access
    Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics
    (Elsevier BV, 2023-11-28) Dura-Bernal, Salvador; Griffith, Erica Y.; Barczak, Annamaria; O’Connell, Monica N.; McGinnis, Tammy; Moreira, Joao V.S.; Schroeder, Charles E.; Lytton, William W.; Lakatos, Peter; Neymotin, Samuel A.
    We developed a detailed model of macaque auditory thalamocortical circuits, including primary auditory cortex (A1), medial geniculate body (MGB), and thalamic reticular nucleus, utilizing the NEURON simulator and NetPyNE tool. The A1 model simulates a cortical column with over 12,000 neurons and 25 million synapses, incorporating data on cell-type-specific neuron densities, morphology, and connectivity across six cortical layers. It is reciprocally connected to the MGB thalamus, which includes interneurons and core and matrix-layer-specific projections to A1. The model simulates multiscale measures, including physiological firing rates, local field potentials (LFPs), current source densities (CSDs), and electroencephalography (EEG) signals. Laminar CSD patterns, during spontaneous activity and in response to broadband noise stimulus trains, mirror experimental findings. Physiological oscillations emerge spontaneously across frequency bands comparable to those recorded in vivo. We elucidate population-specific contributions to observed oscillation events and relate them to firing and presynaptic input patterns. The model offers a quantitative theoretical framework to integrate and interpret experimental data and predict its underlying cellular and circuit mechanisms.
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    PublicationOpen Access
    Computer modeling for pharmacological treatments for dystonia.
    (2017-03-31) Neymotin, Samuel A; Dura-Bernal, Salvador; Moreno, Herman; Lytton, William W
    Dystonia is a movement disorder that produces involuntary muscle contractions. Current pharmacological treatments are of limited efficacy. Dystonia, like epilepsy is a disorder involving excessive activty of motor areas including motor cortex and several causal gene mutations have been identified. In order to evaluate potential novel agents for multitarget therapy for dystonia, we have developed a computer model of cortex that includes some of the complex array of molecular interactions that, along with membrane ion channels, control cell excitability.
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    PublicationOpen Access
    NetPyNE Implementation and Scaling of the Potjans-Diesmann Cortical Microcircuit Model.
    Romaro, Cecilia; Najman, Fernando Araujo; Lytton, William W; Roque, Antonio C; Dura-Bernal, Salvador
    The Potjans-Diesmann cortical microcircuit model is a widely used model originally implemented in NEST. Here, we reimplemented the model using NetPyNE, a high-level Python interface to the NEURON simulator, and reproduced the findings of the original publication. We also implemented a method for scaling the network size that preserves first- and second-order statistics, building on existing work on network theory. Our new implementation enabled the use of more detailed neuron models with multicompartmental morphologies and multiple biophysically realistic ion channels. This opens the model to new research, including the study of dendritic processing, the influence of individual channel parameters, the relation to local field potentials, and other multiscale interactions. The scaling method we used provides flexibility to increase or decrease the network size as needed when running these CPU-intensive detailed simulations. Finally, NetPyNE facilitates modifying or extending the model using its declarative language; optimizing model parameters; running efficient, large-scale parallelized simulations; and analyzing the model through built-in methods, including local field potential calculation and information flow measures.
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    PublicationOpen Access
    Theta-gamma phase amplitude coupling in a hippocampal CA1 microcircuit.
    (2023-03-23) Ponzi, Adam; Dura-Bernal, Salvador; Migliore, Michele
    Phase amplitude coupling (PAC) between slow and fast oscillations is found throughout the brain and plays important functional roles. Its neural origin remains unclear. Experimental findings are often puzzling and sometimes contradictory. Most computational models rely on pairs of pacemaker neurons or neural populations tuned at different frequencies to produce PAC. Here, using a data-driven model of a hippocampal microcircuit, we demonstrate that PAC can naturally emerge from a single feedback mechanism involving an inhibitory and excitatory neuron population, which interplay to generate theta frequency periodic bursts of higher frequency gamma. The model suggests the conditions under which a CA1 microcircuit can operate to elicit theta-gamma PAC, and highlights the modulatory role of OLM and PVBC cells, recurrent connectivity, and short term synaptic plasticity. Surprisingly, the results suggest the experimentally testable prediction that the generation of the slow population oscillation requires the fast one and cannot occur without it.
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    PublicationOpen Access
    Optimization by Adaptive Stochastic Descent.
    (2018-03-16) Kerr, Cliff C; Dura-Bernal, Salvador; Smolinski, Tomasz G; Chadderdon, George L; Wilson, David P
    When standard optimization methods fail to find a satisfactory solution for a parameter fitting problem, a tempting recourse is to adjust parameters manually. While tedious, this approach can be surprisingly powerful in terms of achieving optimal or near-optimal solutions. This paper outlines an optimization algorithm, Adaptive Stochastic Descent (ASD), that has been designed to replicate the essential aspects of manual parameter fitting in an automated way. Specifically, ASD uses simple principles to form probabilistic assumptions about (a) which parameters have the greatest effect on the objective function, and (b) optimal step sizes for each parameter. We show that for a certain class of optimization problems (namely, those with a moderate to large number of scalar parameter dimensions, especially if some dimensions are more important than others), ASD is capable of minimizing the objective function with far fewer function evaluations than classic optimization methods, such as the Nelder-Mead nonlinear simplex, Levenberg-Marquardt gradient descent, simulated annealing, and genetic algorithms. As a case study, we show that ASD outperforms standard algorithms when used to determine how resources should be allocated in order to minimize new HIV infections in Swaziland.