DynaSim: a MATLAB toolbox for neural modeling and simulation

[EN] DynaSim is an open-source MATLAB/GNU Octave toolbox for rapid prototyping of neural models and batch simulation management. It is designed to speed up and simplify the process of generating, sharing, and exploring network models of neurons with one or more compartments. Models can be specified by equations directly (similar to XPP or the Brian simulator) or by lists of predefined or custom model components. The higher-level specification supports arbitrarily complex population models and networks of interconnected populations. DynaSim also includes a large set of features that simplify exploring model dynamics over parameter spaces, running simulations in parallel using both multicore processors and high-performance computer clusters, and analyzing and plotting large numbers of simulated data sets in parallel. It also includes a graphical user interface (DynaSim GUI) that supports full functionality without requiring user programming. The software has been implemented in MATLAB to enable advanced neural modeling using MATLAB, given its popularity and a growing interest in modeling neural systems. The design of DynaSim incorporates a novel schema for model specification to facilitate future interoperability with other specifications (e.g., NeuroML, SBML), simulators (e.g., NEURON, Brian, NEST), and web-based applications (e.g., Geppetto) outside MATLAB. DynaSim is freely available at http://dynasimtoolbox.org. This tool promises to reduce barriers for investigating dynamics in large neural models, facilitate collaborative modeling, and complement other tools being developed in the neuroinformatics community.This material is based upon research supported by the U.S. Army Research Office under award number ARO W911NF-12-R-0012-02, the U.S. Office of Naval Research under award number ONR MURI N00014-16-1-2832, and the National Science Foundation under award number NSF DMS-1042134 (Cognitive Rhythms Collaborative: A Discovery Network)Sherfey, JS.; Soplata, AE.; Ardid-Ramírez, JS.; Roberts, EA.; Stanley, DA.; Pittman-Polletta, BR.; Kopell, NJ. (2018). DynaSim: a MATLAB toolbox for neural modeling and simulation. Frontiers in Neuroinformatics. 12:1-15. https://doi.org/10.3389/fninf.2018.00010S11512Bokil, H., Andrews, P., Kulkarni, J. E., Mehta, S., & Mitra, P. P. (2010). Chronux: A platform for analyzing neural signals. 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Hetereogeneity in Neuronal Intrinsic Properties: A Possible Mechanism for Hub-Like Properties of the Rat Anterior Cingulate Cortex during Network Activity.

The anterior cingulate cortex (ACC) is vital for a range of brain functions requiring cognitive control and has highly divergent inputs and outputs, thus manifesting as a hub in connectomic analyses. Studies show diverse functional interactions within the ACC are associated with network oscillations in the β (20-30 Hz) and γ (30-80 Hz) frequency range. Oscillations permit dynamic routing of information within cortex, a function that depends on bandpass filter-like behavior to selectively respond to specific inputs. However, a putative hub region such as ACC needs to be able to combine inputs from multiple sources rather than select a single input at the expense of others. To address this potential functional dichotomy, we modeled local ACC network dynamics in the rat in vitro. Modal peak oscillation frequencies in the β- and γ-frequency band corresponded to GABAAergic synaptic kinetics as seen in other regions; however, the intrinsic properties of ACC principal neurons were highly diverse. Computational modeling predicted that this neuronal response diversity broadened the bandwidth for filtering rhythmic inputs and supported combination-rather than selection-of different frequencies within the canonical γ and β electroencephalograph bands. These findings suggest that oscillating neuronal populations can support either response selection (routing) or combination, depending on the interplay between the kinetics of synaptic inhibition and the degree of heterogeneity of principal cell intrinsic conductances.Wellcome Trus

Flexible resonance in prefrontal networks with strong feedback inhibition

[EN] 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.This material is based upon research supported by the U. S. Army Research Office under award number ARO W911NF-12-R-0012-02 to N. K., the U. S. Office of Naval Research under award number ONR MURI N00014-16-1-2832 to M. H., and the National Science Foundation under award number NSF DMS-1042134 (Cognitive Rhythms Collaborative: A Discovery Network) to N. K. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Sherfey, JS.; Ardid-Ramírez, JS.; Hass, J.; Hasselmo, ME.; Kopell, NJ. (2018). Flexible resonance in prefrontal networks with strong feedback inhibition. 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Biased competition in the absence of input bias revealed through corticostriatal computation

[EN] Classical accounts of biased competition require an input bias to resolve the competition between neuronal ensembles driving downstream processing. However, flexible and reliable selection of behaviorally relevant ensembles can occur with unbiased stimulation: striatal D1 and D2 spiny projection neurons (SPNs) receive balanced cortical input, yet their activity determines the choice between GO and NO-GO pathways in the basal ganglia. We here present a corticostriatal model identifying three mechanisms that rely on physiological asymmetries to effect rate- and time-coded biased competition in the presence of balanced inputs. First, tonic input strength determines which one of the two SPN phenotypes exhibits a higher mean firing rate. Second, low-strength oscillatory inputs induce higher firing rate in D2 SPNs but higher coherence between D1 SPNs. Third, high-strength inputs oscillating at distinct frequencies can preferentially activate D1 or D2 SPN populations. Of these mechanisms, only the latter accommodates observed rhythmic activity supporting rule-based decision making in prefrontal cortexWe thank T. Womelsdorf for helpful suggestions on an earlier version of the manuscript. We also thank the two reviewers for the constructive comments that enhanced the quality of the manuscript. In particular, their question regarding the resonant properties of SPNs under distinct mean input helped us to uncover how the resonance of D2 SPNs shifts in frequency space (Fig. 3E). Our research was supported by the Army Research Office (ARO) Grant W911NF-12-R-0012-02 (to N.K.). Additionally, S.A. and N.K. were supported by NSF Grant DMS-1042134, and M.M.M. was supported by the Collaborative Research in Computational Neuroscience (CRCNS) NIH Grant CRCNS 1R01N5081716Ardid-Ramírez, JS.; Sherfey, JS.; Mccarthy, MM.; Hass, J.; Pittman-Polletta, BR.; Kopell, N. (2019). Biased competition in the absence of input bias revealed through corticostriatal computation. 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Cartoon profiles of time- and population-averaged PC firing rates in response to different types of oscillatory inputs.

<p>(<b>A</b>) Response to sinusoidal drive. (i) Weak input produces a band-pass filter (BPF) response with spikes driven by near-resonant frequencies and only a fraction of cells spiking on every cycle. (ii) Strong input produces an all-pass response with a resonant peak. <b>B</b>) Response to fixed-mean square wave drive. (i) Weak input produces a low-pass filter (LPF) response with spikes driven by all frequencies below a resonant peak and all cells spiking on every cycle. (<b>C</b>) Response to fixed-amplitude square wave drive. (i) Weak input produces a high-pass filter (HPF) response. (ii) Strong input produces an all-pass response without a well-defined resonant peak.</p

Neuromodulation of firing rate resonance in PC/IN network.

<p>(<b>A</b>) Diagram showing an external sinusoidal Poisson input to the dendrites of 20 two-compartment principal cells (PCs) receiving feedback inhibition from a population of 5 fast spiking interneurons (INs). PC and IN models include conductances found in prefrontal neurons (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006357#pcbi.1006357.g007" target="_blank">Fig 7A</a> for details). (<b>B</b>) Input frequency-dependent firing rate profile showing resonance at a beta2 frequency. (<b>C</b>) The effect of knocking out individual ion currents on the resonant input frequency maximizing firing rate outputs. Removing hyperpolarizing currents (-Ks, -KCa) increased the resonant frequency, while removing depolarizing currents (-NaP) decreased the resonant frequency or (-Ca) silenced the cell altogether (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006357#pcbi.1006357.g007" target="_blank">Fig 7A</a> for ion channel key). Error bars indicate mean ± standard deviation across 10 realizations; only -KCa had a non-zero standard deviation (i.e., values that differed across realizations). (<b>D</b>) The effect of hyperpolarizing and depolarizing injected currents, I_app, on the resonant frequency mirrored the effect of knockouts on excitability.</p

Architecture of output networks.

<p>(<b>A</b>) Diagram showing feedforward excitation from external independent Poisson spike trains to the dendrites of 20 two-compartment (soma, dend) principal cells (PCs) receiving feedback inhibition from a population of 5 fast spiking interneurons (INs). All PC and IN cells have biophysics based on rat prelimbic cortex (Ion channel key: NaF = fast sodium channel; KDR = fast delayed rectifier potassium channel; NaP = persistent sodium channel; Ks = slow (M-type) potassium channel; Ca = high-threshold calcium channel; KCa = calcium-dependent potassium channel). (<b>B</b>) Diagram showing a rhythmically-driven target population of PC cells (PC_T) competing with an asynchronously-driven distractor population (PC_D) through a shared population of inhibitory IN cells.</p

Input network activity.

<p>(<b>A</b>) Asynchronous Poisson input with (i) constant instantaneous rate <i>r</i>_<i>inp</i> and (ii) raster for 100 input cells with <i>r</i>_<i>inp</i> = 10 sp/s (equivalent to 1 input cell with <i>r</i>_<i>inp</i> = 1000 sp/s). (<b>B</b>) Poisson inputs with oscillatory instantaneous rate-modulation. (i) Instantaneous rate modulated by low synchrony square wave, parameterized by pulse width <i>δ</i>_<i>inp</i> and inter-pulse frequency <i>f</i>_<i>inp</i>. (ii) raster plot produced by square wave input. (iii) High synchrony, square wave rate-modulation. (iv) sine wave modulation, parameterized by frequency <i>f</i>_<i>inp</i>. (<b>C</b>) Output measures for the PC/IN network. (i) Diagram of a PC/IN network receiving an input from (B). (ii) Plots showing the instantaneous firing rate (iFR) computed for each population using Gaussian kernel regression on the spike raster. Time-averaged firing rates are defined by the mean iFR for each population. (iii) Power spectrum showing how population frequency is defined by the spectral frequency with peak power in the iFR.</p

Meaning of symbols used in the study of resonance and gating.

<p>Meaning of symbols used in the study of resonance and gating.</p

Dependence of response profiles on input strength.

<p>(<b>A</b>) Diagram of PFC network receiving variable-strength high-synchrony square wave input. (<b>Bi</b>) Firing rate profile for PC populations given oscillatory inputs with different strengths. (<b>Bii</b>) Population frequency profile for inputs with different strengths. Horizontal dashed lines mark the natural frequencies for each drive strength. (<b>C</b>) The effect of input strength on natural and resonant frequencies. (<b>D</b>) Spike rasters and PC iFR responses showing the typical case of stronger input driving more output: (left) weaker input, less output, (right) stronger input, more output. (<b>E</b>) Spike rasters and PC iFR responses showing special case of resonance at first harmonic enabling a weaker input to drive more output: (left) weaker input, more output, (right) stronger input, less output.</p