Neuronal Models Research Articles

A Short Survey of the Development and Applications of Spiking Neural Networks of High Biological Plausibility

Abstract Spiking neural networks (SNNs) are inspired from natural computing, modelling with high accuracy the interactions and processes between the synapses of the neurons focusing on low response time and energy efficiency. This novel paradigm of event-based processing opens new opportunities for discovering applications and developing efficient learning methods that should highlight the advantages of SNNs such as the large memory capacity and the fast adaptation, while preserving the easy-to-use and portability of the conventional computing architectures. In this paper, we do a brief review of the developments of the past decades in the field of SNNs. We start with a brief history of the SNN and summarize the most common models of spiking neurons and methods to implement synaptic plasticity. We also classify the SNNs according to the implemented learning rules and network topology. We present the computational advantages, liabilities, and applications suitable for using SNNs in terms of energy efficiency and response time. In addition, we briefly sweep through the existing platforms and simulation frameworks for SNNs exploration. The paper ends with conclusions that show predictions of future challenges and the emerging research topics associated with SNNs.

Bi-directional Control of Synaptic Input Summation and Spike Generation by GABAergic Inputs at the Axon Initial Segment.

Differing from other subtypes of inhibitory interneuron, chandelier or axo-axoniccells form depolarizing GABAergic synapses exclusively onto the axon initial segment (AIS) of targeted pyramidal cells (PCs). However, the debate whether these AIS-GABAergic inputs produce excitation or inhibition in neuronal processing is not resolved. Using realistic NEURON modeling and electrophysiological recording of cortical layer-5 PCs, we quantitatively demonstrate that the onset-timing of AIS-GABAergic input, relative to dendritic excitatory glutamatergic inputs, determines its bi-directional regulation of the efficacy of synaptic integration and spike generation in a PC. More specifically, AIS-GABAergic inputs promote the boosting effect of voltage-activated Na+ channels on summed synaptic excitation when they precede glutamatergic inputs by >15 ms, while for nearly concurrent excitatory inputs, they primarily produce a shunting inhibition at the AIS. Thus, our findings offer an integrative mechanism by which AIS-targeting interneurons exert sophisticated regulation of the input-output function in targeted PCs.

A varying-radius cable equation for the modelling of impulse propagation in excitable fibres.

In this study, we present a varying-radius cable equation for nerve fibres taking into account the varying diameter along the neuronal segments. Finite element neuronal models utilising the classical (fixed-radius) and varying-radius cable formulations were compared using simple and realistic morphologies under intra- and extracellular electrical stimulation protocols. We found that the use of the classical cable equation to model intracellular neural electrical stimulation exhibited an error of 17% in a passive resistive cable model with abrupt change in radius from 1 to 2 μm, when compared to the known analytical solution and varying-radius cable formulation. This error was observed to increase substantially using more realistic neuron morphologies and branching structures. In the case of extracellular stimulation however, the difference between the classical and varying-radius formulations was less pronounced, but we expect this difference will increase under more complex stimulation paradigms such as high-frequency stimulation. We conclude that for computational neuroscience applications, it is essential to use the varying-radius cable equation for accurate prediction of neuronal responses under electrical stimulation.

Identification of N-methyl-D-aspartate receptor antagonists using the rat postnatal mixed cortical and hippocampal neurons

The goal of this study was to evaluate mixed cortical and hippocampal primary rat postnatal neuronal culture as in vitro tool for identification of N-methyl-D-aspartate receptor (NMDAR) antagonists and to find out, whether this model is comparable with other commonly used primary rat neuronal models differing in their origin (pure cortical vs. mixed cortical and hippocampal) and differentiation state (embryonal vs. postnatal). Induced pluripotent stem cell (iPSC) – derived human glutamatergic neurons have been included in this study as well. First, the cultures were characterized by their neuron/astrocyte composition, the mRNA expression of NR2B/NR2A NMDAR subunit ratios, and the expression of glutamate transporters (GLT1, GLAST). Then, selected endogenous steroids and synthetic neuroactive steroids that have been previously identified as negative allosteric modulators of recombinant GluN1/GluN2B NMDA receptors, were evaluated for their ability to prevent an NMDA or glutamate-induced Ca2+ influx (acute effect) and excitotoxicity over 24 h. Though the neuroprotective potential against excitotoxic stimuli varied among the models studied, postnatal mixed cortical and hippocampal culture proved to be a convenient and robust tool for NMDAR antagonist screening. The most widely used embryonal (E18) cultures offered higher cell yields but at the expense of a higher sensitivity to compounds’ cytotoxicity. iPSC-derived neurons were not found to be superior to rat cultures for screening purposes.

Selective representations of texture and motion in mouse higher visual areas

The mouse visual cortex contains interconnected higher visual areas, but their functional specializations are unclear. Here, we used a data-driven approach to examine the representations of complex visual stimuli by L2/3 neurons across mouse higher visual areas, measured using large-field-of-view two-photon calcium imaging. Using specialized stimuli, we found higher fidelity representations of texture in area LM, compared to area AL. Complementarily, we found higher fidelity representations of motion in area AL, compared to area LM. We also observed this segregation of information in response to naturalistic videos. Finally, we explored how receptive field models of visual cortical neurons could produce the segregated representations of texture and motion we observed. These selective representations could aid in behaviors such as visually guided navigation.

Ex Vivo Herpes Simplex Virus Reactivation Involves a Dual Leucine Zipper Kinase-Dependent Wave of Lytic Gene Expression That Is Independent of Histone Demethylase Activity and Viral Genome Synthesis.

Herpes simplex virus 1 (HSV-1) maintains a lifelong latent infection in neurons and periodically reactivates, resulting in the production of infectious virus. The exact cellular pathways that induce reactivation are not understood. In primary neuronal models of HSV latency, the cellular protein dual leucine zipper kinase (DLK) has been found to initiate a wave of viral gene expression known as phase I. Phase I occurs independently of both viral DNA replication and the activities of histone demethylase enzymes required to remove repressive heterochromatin modifications associated with the viral genome. In this study, we investigated whether phase I-like gene expression occurs in ganglia reactivated from infected mice. Using the combined trigger of explant-induced axotomy and inhibition of phosphatidylinositide 3-kinase (PI3K) signaling, we found that HSV lytic gene expression was induced rapidly from both sensory and sympathetic neurons. Ex vivo reactivation involved a wave of viral late gene expression that occurred independently of viral genome synthesis and histone demethylase activity and preceded the detection of infectious virus. Importantly, we found that DLK was required for the initial induction of lytic gene expression. These data confirm the essential role of DLK in inducing HSV-1 gene expression from the heterochromatin-associated genome and further demonstrate that HSV-1 gene expression during reactivation occurs via mechanisms that are distinct from lytic replication. IMPORTANCE Reactivation of herpes simplex virus from a latent infection is associated with clinical disease. To develop new therapeutics that prevent reactivation, it is important to understand how viral gene expression initiates following a reactivation stimulus. Dual leucine zipper kinase (DLK) is a cellular protein that has previously been found to be required for HSV reactivation from sympathetic neurons in vitro. Here, we show that DLK is essential for reactivation from sensory ganglia isolated from infected mice. Furthermore, we show that DLK-dependent gene expression ex vivo occurs via mechanisms that are distinct from production replication, namely, lytic gene expression that is independent of viral DNA replication and histone demethylase activity. The identification of a DLK-dependent wave of lytic gene expression from sensory ganglia will ultimately permit the development of novel therapeutics that target lytic gene expression and prevent the earliest stage of reactivation.

Bio-Inspired Models of Convolution Neurons in the Problem of Illusory Contour Recognition

This paper presents the result of designing the architecture of a neural network on bio-inspired neurons, whose task is to work out the mechanism for recognizing an illusory contour using the example of “Kanizsa’s figures”. The neural network made it possible to achieve invariance to the number of corners of the figure and does not lose recognition quality when changing the size of the illusory contour. The main application of the approach can be found in the problem of separating “figure-background” in images.

Switching behavior of the gamma power in the neuronal network modulated by the astrocytes

Understanding the dynamical neural gamma oscillation during brain state switch has been accepted as a critical step to explain attention and related human behaviors. Recent experimental studies have found a specific pulse of gamma power traces, coexisting with a pulse of functional magnetic resonance imaging with energy consumption by utilizing intracranial electroencephalography and neuroimaging. This phenomenon implies that the gamma oscillation is crucial in the success of the attention switching process after the attention task termination. However, until now no experimental evidence exists that explains how this type of gamma oscillation occurs and participates in the attention switching process. In this paper, the neuron-astrocyte-capillary network model was established, by utilizing the mathematical models of neurons and astrocytes that could explain the micro-scale behavior of neurons and investigated the formation process of this type of gamma oscillation. Our simulation results showed that the astrocyte calcium activity was pivotal in the occurrence of gamma oscillation after the attention task termination, and properly explained the underlying mechanisms of the dynamical switching of brain neuronal networks.

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.

Biophysical Models of Minipig Right Atrial Ganglionic Plexus Principal Neurons Identified from Transcriptomics Data

The Right Atrial Ganglionic Plexus (RAGP) is a component of the intrinsic cardiac nervous system. Its neurons are critical to the cardiac pacemaker response to vagal stimulation, regulating sino‐atrial node activity. We developed single‐compartment computational models of RAGP principal neurons by incorporating ion channels identified from transcriptomics data from HT‐qPCR (High Throughput quantitative Polymerase Chain Reaction) and RNA‐sequencing of 405 principal neurons in female Yucatan minipig RAGP. Out of the ~200 genes in the transcriptomics map, we identified 13 genes that encode ion channel proteins, namely: Naf (Scn1a, Nav 1.1), KDR (Kcnc1, Kv 3.1; Kcnab1, Kv 1.1), P‐, N‐, L‐, T‐type Ca2+ (Cacna1a, Cav 2.1; Cacna1b, Cav 2.2; Cacna1c, Cav 1.2; Cacna1g, Cav 3.1; Cacna1i, Cav 3.3), KA (Kcnd2) and HCN1, HCN2, HCN3, HCN4. The corresponding Hodgkin‐Huxley‐based kinetic models of these ion channel isoforms were mined from databases: Ion Channel Genealogy, Channelpedia, and ModelDB. Since each ion channel gene had several kinetic models associated with it, we ran multiple simulations on the NetPyNE software to identify a single model per gene that would best represent the channel's dynamic behaviour in the assembled electrophysiological model. A binary transcriptomic map of the 13 genes showed 101 distinct ion channel combinations. Our 505 electrophysiological models (5 sets of the 101 cells) elicited two primary firing profiles. 65% elicited a single‐spike phasic response, ~32% elicited a tonic firing in response to a current clamp stimulus, and ~3% produced tonic firing that continued after the stimulus. The firing frequencies increased by ~13 Hz for every 0.1 nA increase in the current clamp stimulus and decreased by ~11 Hz for every 0.01 S/cm2 increase in KA channel conductance. Our electrophysiological models are available to the SPARC community in NetPyNE and NIH’s O2S2PARC platform. The models exhibit phasic and tonic excitability profiles with phasic being more prevalent. We next aim to incorporate neuromodulatory inputs and simulate a network of these neuronal models.

Anatomical and Molecular Phenotypes of Fast and Slow Vagal Targets in the Intrinsic Cardiac Nervous System

The vagus nerve coordinates the communication between the brain and most of the visceral organs. Vagus nerve activity is critical to promote cardiac health and cardioprotective response in heart failure. The vagal motor outflow involves two nuclei, the nucleus ambiguous (NA) and the dorsal motor nucleus of the vagus (DMV). NA acts via nicotinic cholinergic processes to elicit fast kinetics of the intrinsic cardiac nervous system (ICN) response at the heart. By contrast, the DMV influences the ICN neurons via muscarinic cholinergic processes yielding slower kinetics of response. Interestingly, NA and DMV project to distinct neuronal populations in the ICN, providing an anatomical basis for the fast versus slow vagal action on the heart. We aim to delineate the ICN neuronal network mediating the two lanes of vagal inputs for regulating distinct cardiac functions with lane‐specific transcriptomic identities of ICN neuron targets. We performed immunohistochemistry (IHC) on the rat heart tissue sections embedded in the optimal cutting temperature (OCT) followed by confocal microscopy imaging. We found that while the ICN neurons express muscarinic and nicotinic acetylcholine receptors (AChRs) broadly across the ICN, with several neurons predominantly expressing either muscarinic or nicotinic AChRs. We used Laser Capture Microscopy (LCM) to acquire neurons that predominantly express muscarinic or nicotinic AChRs. We are profiling the transcriptomic identities of these LCM‐captured neuron classes. Our results suggest that mAChR‐only and nAChR‐only neurons differentially express neuromodulatory processes, including receptors, neurotransmitter synthesis enzymes, and neuropeptides. Further, we incorporate a systems approach combining spatial single‐cell transcriptomics, neuronal network modeling, and physiological control systems modeling to identify selective targets for novel neuromodulatory interventions that improve heart health.

Potential Drug Therapy For Fragile X Tremor/Ataxia Syndrome

Fragile X‐associated tremor/ataxia syndrome (FXTAS) is a late‐onset neurological disorder that affects carriers of the alleles for the Fragile X gene, Fragile X Mental Retardation (FMR1) due to the CGG triplet repeat expansion within the 5’UTR of FMR1. Clinical symptoms that are likely to occur are progressive ataxia, intention tremor, intranuclear inclusions, and Parkinsonian‐like motor symptoms. The goal of this research is to assess the biological efficacy of Curcumin, Muscadine Grape Seed Extract (MSKE) on the neuroprotective capacity with respect to mitochondrial damage, and their ability to restore mitochondrial health in patient with FXTAS. To establish mitochondrial dysfunction, normal human cell lines and human‐induced pluripotent cells were treated with multiple concentrations of glucose/ glucose oxidase (GluOx) at 2,12,and 24 hour time points to induce varying intensities of oxidative stress. The degrees of oxidative stress were measured by apoptosis and mitochondrial reactive oxygen species (ROS) production. Curcumin and MSKE, compounds effective against oxidative damage in mitochondria, were used to rescue glucose oxidase induced oxidative damage in both cell lines. To test the ability of these drugs to restore mitochondrial health, cell viability and cellular superoxide production were assessed by propidium iodide and the MitoSox fluorescence assay, respectively. We anticipated that GluOx at varying concentrations and time points would proportionally increase levels of apoptosis and mitochondrial ROS, reflective of mitochondrial damage, with the most severe dysfunction occurring at a dose of 25 nM and the longest duration of 24‐hr exposure. Administration of MSKE in concentrations ranging from 10‐8 to 10‐5 M in half log increments, did not reverse the oxidative defects induced in the cell lines. However, curcumin concentrations increased cell viability at the 2, 12, and 24 hour time period. Results indicate that the research design should be modified by increasing concentration of both glucose and MSKE to provide a reliable test of the hypothesis. Although, the research design will be modified to better assessed the hypothesis, the overall research is translational in that it lends flexibility to testing therapeutics in neuronal FXTAS models and expands the discovery of mitochondrial markers for the syndrome.

Multisample Online Learning for Probabilistic Spiking Neural Networks.

Spiking neural networks (SNNs) capture some of the efficiency of biological brains for inference and learning via the dynamic, online, and event-driven processing of binary time series. Most existing learning algorithms for SNNs are based on deterministic neuronal models, such as leaky integrate-and-fire, and rely on heuristic approximations of backpropagation through time that enforces constraints such as locality. In contrast, probabilistic SNN models can be trained directly via principled online, local, and update rules that have proven to be particularly effective for resource-constrained systems. This article investigates another advantage of probabilistic SNNs, namely, their capacity to generate independent outputs when queried over the same input. It is shown that the multiple generated output samples can be used during inference to robustify decisions and to quantify uncertainty-a feature that deterministic SNN models cannot provide. Furthermore, they can be leveraged for training in order to obtain more accurate statistical estimates of the log-loss training criterion and its gradient. Specifically, this article introduces an online learning rule based on generalized expectation-maximization (GEM) that follows a three-factor form with global learning signals and is referred to as GEM-SNN. Experimental results on structured output memorization and classification on a standard neuromorphic dataset demonstrate significant improvements in terms of log-likelihood, accuracy, and calibration when increasing the number of samples used for inference and training.

Hidden coexisting firing patterns and bubble-like bifurcation in HR neuron model under electromagnetic induction

When ions enter or leave cell membrane, action potentials are generated. The fluctuations or changes in action potential of neurons result in an extra electromagnetic field according to the Faraday’s law of induction. As a result, the electromagnetic field will contribute to generation of membrane potential as a feedback effect, which should not be ignored in neuron modeling. In this paper, a three-dimensional improved Hindmarsh–Rose neuron model considering the influence of electromagnetic induction is proposed, in which magnetic flux is used to describe the effect of electromagnetic induction that contributes to generation of membrane potential as a feedback memristive current. The improved neuron model has no equilibrium point and can present different types of hidden coexisting firing patterns. Besides, the neuron model can show bubble-like bifurcation modes by varying system parameters, which has not been reported in other Hindmarsh–Rose neuron models. Extensive numerical simulations are carried out and bifurcation analyses indicate that the proposed neuron model has a wide variety of hidden coexisting firing patterns and bifurcation modes. Finally, equivalent circuit is designed and performed in Pspice, and the simulation results are consistent well with the numerical ones.

Cell type-specific mechanisms of information transfer in data-driven biophysical models of hippocampal CA3 principal neurons

The transformation of synaptic input into action potential output is a fundamental single-cell computation resulting from the complex interaction of distinct cellular morphology and the unique expression profile of ion channels that define the cellular phenotype. Experimental studies aimed at uncovering the mechanisms of the transfer function have led to important insights, yet are limited in scope by technical feasibility, making biophysical simulations an attractive complementary approach to push the boundaries in our understanding of cellular computation. Here we take a data-driven approach by utilizing high-resolution morphological reconstructions and patch-clamp electrophysiology data together with a multi-objective optimization algorithm to build two populations of biophysically detailed models of murine hippocampal CA3 pyramidal neurons based on the two principal cell types that comprise this region. We evaluated the performance of these models and find that our approach quantitatively matches the cell type-specific firing phenotypes and recapitulate the intrinsic population-level variability in the data. Moreover, we confirm that the conductance values found by the optimization algorithm are consistent with differentially expressed ion channel genes in single-cell transcriptomic data for the two cell types. We then use these models to investigate the cell type-specific biophysical properties involved in the generation of complex-spiking output driven by synaptic input through an information-theoretic treatment of their respective transfer functions. Our simulations identify a host of cell type-specific biophysical mechanisms that define the morpho-functional phenotype to shape the cellular transfer function and place these findings in the context of a role for bursting in CA3 recurrent network synchronization dynamics.

Microwave radiation induces neuronal autophagy through miR-30a-5p/AMPKα2 signal pathway

The potential health hazards of microwaves have attracted much more attention. Our previous study found that 2856 MHz microwave radiation damaged synaptic plasticity and activated autophagy in neurons. However, the mechanisms underlying microwave-induced autophagy were still unclear. In the present study, we established neuronal damage models by exposing rat hippocampal neurons and rat adrenal pheochromocytoma (PC12) cell-derived neuron-like cells to 30 mW/cm2 microwaves, which resulted in miR-30a-5p (‘miR-30a’ for short) down-regulation and autophagy activation in vivo and in vitro. Bioinformatics analysis was conducted, and Beclin1, Prkaa2, Irs1, Pik3r2, Rras2, Ddit4, Gabarapl2 and autophagy-related gene 12 (Atg12) were identified as potential downstream genes of miR-30a involved in regulating autophagy. Based on our previous findings that microwave radiation could lead to abnormal energy metabolism in neurons, Prkaa2, encoding adenosine 5′-monophosphate-activated protein kinase (AMPK) α2 (AMPKα2, an important catalytic subunit of energy sensor AMPK), was selected for further analysis. Dual-luciferase reporter assay results showed that Prkaa2 was a downstream gene of miR-30a. Moreover, microwave radiation increased the expression of AMPKα2 and the phosphorylation of AMPKα (Thr172) both in vivo and in vitro. The transfection of PC12 cells with miR-30a mimics increased miR-30a levels, reduced AMPKα2 expression, suppressed AMPKα (Thr172) phosphorylation, and inhibited autophagy occurrence in neuron-like cells. Importantly, miR-30a overexpression abolished microwave-activated autophagy and inhibited microwave-induced AMPKα2 up-regulation and AMPKα (Thr172) phosphorylation. In conclusion, microwave radiation promoted the occurrence of autophagy in neurons through the miR-30a/AMPKα2 signal pathway.

Direct Discriminative Decoder Models for Analysis of High-Dimensional Dynamical Neural Data.

With the accelerated development of neural recording technology over the past few decades, research in integrative neuroscience has become increasingly reliant on data analysis methods that are scalable to high-dimensional recordings and computationally tractable. Latent process models have shown promising results in estimating the dynamics of cognitive processes using individual models for each neuron's receptive field. However, scaling these models to work on high-dimensional neural recordings remains challenging. Not only is it impractical to build receptive field models for individual neurons of a large neural population, but most neural data analyses based on individual receptive field models discard the local history of neural activity, which has been shown to be critical in the accurate inference of the underlying cognitive processes. Here, we propose a novel, scalable latent process model that can directly estimate cognitive process dynamics without requiring precise receptive field models of individual neurons or brain nodes. We call this the direct discriminative decoder (DDD) model. The DDD model consists of (1) a discriminative process that characterizes the conditional distribution of the signal to be estimated, or state, as a function of both the current neural activity and its local history, and (2) a state transition model that characterizes the evolution of the state over a longer time period. While this modeling framework inherits advantages of existing latent process modeling methods, its computational cost is tractable. More important, the solution can incorporate any information from the history of neural activity at any timescale in computing the estimate of the state process. There are many choices in building the discriminative process, including deep neural networks or gaussian processes, which adds to the flexibility of the framework. We argue that these attributes of the proposed methodology, along with its applicability to different modalities of neural data, make it a powerful tool for high-dimensional neural data analysis. We also introduce an extension of these methods, called the discriminative-generative decoder (DGD). The DGD includes both discriminative and generative processes in characterizing observed data. As a result, we can combine physiological correlates like behavior with neural data to better estimate underlying cognitive processes. We illustrate the methods, including steps for inference and model identification, and demonstrate applications to multiple data analysis problems with high-dimensional neural recordings. The modeling results demonstrate the computational and modeling advantages of the DDD and DGD methods.

Stability control of a fractional-order Morris–Lecar neuronal model via fractional-order washout filter

The bifurcation phenomenon generated in neurons is considered to be the cause of many neurological diseases, thus, lots of investigators have been dedicated to bifurcation control, but most of them only focus on the integer-order neuronal models and control methods. Recently, increasing number of research works have shown that the differential equations of fractional-order are more appropriate for description of the electrical properties of certain nerve cell membranes. In this paper, we employ a fractional-order washout filter to realize bifurcation control for fractional-order Morris–Lecar neuronal model. Computer simulation verifies the effectiveness and improved performance of the proposed method than the integer-order washout filter. In addition, the analysis of the fractional-order controller’s impacts on the model is also presented.

Cortical control of striatal fast-spiking interneuron synchrony.

Inhibitory fast-spiking interneurons in the dorsal striatum regulate actions and action strategies, including habits. Fast-spiking interneurons are widely believed to synchronize their firing due to the electrical synapses formed between these neurons. However, neuronal modelling data suggest convergent cortical input may also drive synchrony in fast-spiking interneuron networks. To better understand how fast-spiking interneuron synchrony arises, we performed dual whole-cell patch clamp electrophysiology experiments to inform a simple Bayesian network modelling cortico-fast-spiking interneuron circuitry. Dual whole-cell patch clamp electrophysiology revealed that while responsivity to corticostriatal input activation was high in fast-spiking interneurons, few of these neurons exhibited electrical coupling in adult mice. In simulations of a cortico-fast-spiking interneuron network informed by these data, the degree of glutamatergic cortical convergence onto fast-spiking interneurons significantly increased fast-spiking interneuron synchronization while manipulations of electrical coupling between these neurons exerted relatively little impact. These results suggest that the primary source of functional coordination of fast-spiking interneuron activity in adulthood arises from convergent corticostriatal input activation. KEY POINTS: Electrical synapses between striatal fast-spiking interneurons in adult mice occur in ∼8% of assayed pairs. Coincident, convergent cortical input onto fast-spiking interneurons significantly contributes to fast-spiking interneuron synchrony. Electrical synapses between fast-spiking interneurons provide only minor enhancement of fast-spiking interneuron synchrony. These results suggest a mechanism by which adult mouse fast-spiking interneurons of the striatum synchronize in the face of declining expression of the electrical synapse-forming connexin-36 protein.

FPGA implementation of piecewise linear spiking neuron and simulation of cortical neurons

Modeling and hardware implementation of artificial neurons is an important goal in the field of neuromorphic and brain-inspired computing. This paper presents a hardware implementation method of the two-dimensional piecewise linear spiking neuron (PLSN) model that can express various dynamical behaviors under different bifurcation mechanisms. The neuronal digital circuit of the PLSN model is designed and implemented on a field programmable gate array (FPGA), where all computation processes in the arithmetic tree are pipelined to maintain fast computing. The hardware is used to demonstrate 20 neurocomputational properties that are the most prominent dynamical behaviors of biological neurons. The 6 cortical neurons are also simulated using the PLSN circuit on FPGA. The results show that the proposed neuron circuit can express complex spiking and bursting behaviors of cortical neurons.