DynaSim: A MATLAB Toolbox for Neural Modeling and Simulation

Sherfey, Jason; Soplata, Austin E.; Ardid, Salva; Roberts, Erik A.; Stanley, David A.; Pittman-Polletta, Benjamin

doi:10.3389/fninf.2018.00010

Cited by 51 publications

(53 citation statements)

References 29 publications

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“…Simulation tools 528 All models were implemented in Matlab using the DynaSim toolbox [52] (http://dynasimtoolbox.org) and are 529 publicly available online at: http://github.com/jsherfey/PFC_models. Numerical integration was performed 530 using a 4th-order Runge-Kutta method with a fixed time step of 0.01 ms. Simulations were run for 2500ms and 531 repeated 10 times.…”

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confidence: 99%

Flexible resonance in prefrontal networks with strong feedback inhibition

Sherfey

Ardid

Haß

et al. 2018

Preprint

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Oscillations are ubiquitous features of brain dynamics that undergo task-related changes in synchrony, power, and frequency. The impact of those changes on target networks is poorly understood. In this work, we used a biophysically detailed model of prefrontal cortex (PFC) to explore the effects of varying the spike rate, synchrony, and waveform of strong oscillatory inputs on the behavior of cortical networks driven by them. Interacting populations of excitatory and inhibitory neurons with strong feedback inhibition are inhibition-based network oscillators that exhibit resonance (i.e., larger responses to preferred input frequencies). We quantified network responses in terms of mean firing rates and the population frequency of network oscillation; and characterized their behavior in terms of the natural response to asynchronous input and the resonant response to oscillatory inputs. We show that strong feedback inhibition causes the PFC to generate internal (natural) oscillations in the beta/gamma frequency range (>15 Hz) and to maximize principal cell spiking in response to external oscillations at slightly higher frequencies. Importantly, we found that the fastest oscillation frequency that can be relayed by the network maximizes local inhibition and is equal to a frequency even higher than that which maximizes the firing rate of excitatory cells; we call this phenomenon population frequency resonance. This form of resonance is shown to determine the optimal driving frequency for suppressing responses to asynchronous activity. Lastly, we demonstrate that the natural and resonant frequencies can be tuned by changes in neuronal excitability, the duration of feedback inhibition, and dynamic properties of the input. Our results predict that PFC networks are tuned for generating and selectively responding to beta-and gamma-rhythmic signals due to the natural and resonant properties of inhibition-based oscillators. They also suggest strategies for optimizing transcranial stimulation and using oscillatory networks in neuromorphic engineering.Keywords: prefrontal cortex; network oscillations; biophysically-detailed model; beta rhythm; frequency-based gating; frequency response Author Contributions: J.S. designed the study, performed the simulations, and wrote the paper. S. A., J. H., and N. K. provided extensive feedback through many discussions of the results. N. K. helped write the paper. M. H., S. A., and J. H. reviewed the paper. CC-BY-NC-ND 4.0 International licenseIt is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint (which . http://dx.doi.org/10.1101/364729 doi: bioRxiv preprint first posted online Jul. 7, 2018; 2 Author SummaryThe prefrontal cortex (PFC) flexibly encodes task-relevant representations and outputs biases to mediate higher cognitive functions. The relevant neural ensembles undergo task-related changes in oscillatory dynamics at betaand gamma frequencies. Us...

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confidence: 99%

Flexible resonance in prefrontal networks with strong feedback inhibition

Sherfey

Ardid

Haß

et al. 2018

Preprint

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“…1) and bursting neuron models based on the bursting neurons in the lobster stomatogastric ganglion (STG) (Prinz, Bucher, and Marder 2004). For each type of neuron model, we compared our software to NEURON (Hines and Carnevale 1997), a high-performance and powerful neuron simulator specialized in simulating neurons with complex morphologies and DynaSim (Sherfey et al 2018), a general-purpose simulator that can solve coupled differential equations numerically. All simulators were run on the same hardware using fixed time-step solvers: xolotl used the Exponential Euler method (Dayan and Abbott 2001), NEURON used the implicit Euler solver (Hines and Carnevale 1997) and DynaSim used C-compiled 2 nd -order Runge-Kutta integration scheme as recommended for high-performance (Sherfey et al 2018).…”

Section: Benchmarksmentioning

confidence: 99%

“…Many simulators have been designed with a focus on simulate large numbers of compartments, either as networks with many identical neurons or in a large multi-compartment neuron model (Brette et al 2007; Sherfey et al 2018; Vitay, Dinkelbach, and Hamker 2015; Delorme and Thorpe 2003). While our software is not designed for this task per se , we measured its performance as a function of the number of compartments simulated.…”

Section: Benchmarksmentioning

confidence: 99%

“…In contrast, simulators designed from the ground up in popular scientific computing languages can be easier to use and benefit from interoperability with other commonly-used tools. Simulators like DynaSim (Sherfey et al 2018), ANNarchy (Vitay, Dinkelbach, and Hamker 2015), BRIAN (Stimberg et al 2013), morphforge (Hull and Willshaw 2014), and PyNN (Davison et al 2009) allow the user to specify models with strings of equations or components that are constructed using a special syntax, that can then be translated into a faster implementation language such as C or C++ (Stimberg et al 2014a). This approach permits considerable flexibility for simulating systems of differential equations.…”

Section: Introductionmentioning

confidence: 99%

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Xolotl: An Intuitive and Approachable Neuron and Network Simulator for Research and Teaching

Gorur-Shandilya

Hoyland

Marder

2018

Preprint

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2Conductance-based models of neurons are used extensively in computational neuroscience. 3 Working with these models can be challenging due to their high dimensionality and large number 4 of parameters. Here, we present a neuron and network simulator built on a novel automatic type 5 system that binds object-oriented code written in C++ to objects in MATLAB. Our approach builds 6 on the tradition of uniting the speed of languages like C++ with the ease-of-use and feature-set 7 of scientific programming languages like MATLAB. Xolotl allows for the creation and manipula-8 tion of hierarchical models with components that are named and searchable, permitting intuitive 9 high-level programmatic control over all parts of the model. The simulator's architecture allows 10 for the interactive manipulation of any parameter in any model, and for visualizing the effects 11 of changing that parameter immediately. Xolotl is fully featured with hundreds of ion channel 12 models from the electrophysiological literature, and can be extended to include arbitrary con-13 ductances, synapses, and mechanisms. Several core features like bookmarking of parameters 14 and automatic hashing of source code facilitate reproducible and auditable research. Its ease 15 of use and rich visualization capabilities make it an attractive option in teaching environments. 16 Finally, xolotl is written in a modular fashion, includes detailed tutorials and worked examples, 17 and is freely available at https://github.com/sg-s/xolotl, enabling seamless integration into the 18 workflows of other researchers.19

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“…All simulations were run on the MATLAB-based programming platform DynaSim, a 672 framework for efficiently developing, running and analyzing large systems of coupled 673 ordinary differential equations, and evaluating their dynamics over large regions of 674 parameter space [128]. DynaSim is open-source and all models have been made publicly 675…”

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confidence: 99%

A biophysical model of striatal microcircuits suggests delta/theta-rhythmically interleaved gamma and beta oscillations mediate periodicity in motor control

Chartove

McCarthy

Pittman-Polletta

2019

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Striatal oscillatory activity is associated with movement, reward, and decision-making, and observed in several interacting frequency bands. Local field potential recordings in rodent striatum show dopamine-and reward-dependent transitions between two states: a "spontaneous" state involving β (∼15-30 Hz) and low γ (∼40-60 Hz), and a state involving θ (∼4-8 Hz) and high γ (∼60-100 Hz) in response to dopaminergic agonism and reward. The mechanisms underlying these rhythmic dynamics, their interactions, and their functional consequences are not well understood. In this paper, we propose a biophysical model of striatal microcircuits that comprehensively describes the generation and interaction of these rhythms, as well as their modulation by dopamine. Building on previous modeling and experimental work suggesting that striatal projection neurons (SPNs) are capable of generating β oscillations, we show that networks of striatal fast-spiking interneurons (FSIs) are capable of generating δ/θ (ie, 2 to 6 Hz) and γ rhythms. Under simulated low dopaminergic tone our model FSI network produces low March 8, 2020 1/50 γ band oscillations, while under high dopaminergic tone the FSI network produces high γ band activity nested within a δ/θ oscillation. SPN networks produce β rhythms in both conditions, but under high dopaminergic tone, this β oscillation is interrupted by δ/θ-periodic bursts of γ-frequency FSI inhibition. Thus, in the high dopamine state, packets of FSI γ and SPN β alternate at a δ/θ timescale. In addition to a mechanistic explanation for previously observed rhythmic interactions and transitions, our model suggests a hypothesis as to how the relationship between dopamine and rhythmicity impacts motor function. We hypothesize that high dopamine-induced periodic FSI γ-rhythmic inhibition enables switching between β-rhythmic SPN cell assemblies representing the currently active motor program, and thus that dopamine facilitates movement in part by allowing for rapid, periodic shifts in motor program execution. Author summaryStriatal oscillatory activity is associated with movement, reward, and decision-making, and observed in several interacting frequency bands. The mechanisms underlying these rhythmic dynamics, their interactions, and their functional consequences are not well understood. In this paper, we propose a biophysical model of striatal microcircuits that comprehensively describes the generation and interaction of striatal rhythms, as well as their modulation by dopamine. Our model suggests a hypothesis as to how the relationship between dopamine and rhythmicity impacts the function of the motor system, enabling rapid, periodic shifts in motor program execution.As the largest structure of the basal ganglia network, the striatum is essential to motor 2 function and decision making. It is the primary target of dopaminergic (DAergic) 3 neurons in the brain, and its activity is strongly modulated by DAergic tone. Disorders 4 of the DA and motor systems, such as Parkinson's, Huntington's, Tourette's, and many ...

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DynaSim: A MATLAB Toolbox for Neural Modeling and Simulation

Cited by 51 publications

References 29 publications

Flexible resonance in prefrontal networks with strong feedback inhibition

Flexible resonance in prefrontal networks with strong feedback inhibition

Xolotl: An Intuitive and Approachable Neuron and Network Simulator for Research and Teaching

A biophysical model of striatal microcircuits suggests delta/theta-rhythmically interleaved gamma and beta oscillations mediate periodicity in motor control

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