State transitions through inhibitory interneurons in a cortical network model

Latent dynamics of primary sensory cortical population activity structured by fluctuations in the local field potential

Introduction

As emerging technologies enable measurement of precise details of the activity within microcircuits at ever-increasing scales, there is a growing need to identify the salient features and patterns within the neural populations that represent physiologically and behaviorally relevant aspects of the network. Accumulating evidence from recordings of large neural populations suggests that neural population activity frequently exhibits relatively low-dimensional structure, with a small number of variables explaining a substantial fraction of the structure of the activity. While such structure has been observed across the brain, it is not known how reduced-dimension representations of neural population activity relate to classical metrics of "brain state," typically described in terms of fluctuations in the local field potential (LFP), single-cell activity, and behavioral metrics.

Methods

Hidden state models were fit to spontaneous spiking activity of populations of neurons, recorded in the whisker area of primary somatosensory cortex of awake mice. Classic measures of cortical state in S1, including the LFP and whisking activity, were compared to the dynamics of states inferred from spiking activity.

Results

A hidden Markov model fit the population spiking data well with a relatively small number of states, and putative inhibitory neurons played an outsize role in determining the latent state dynamics. Spiking states inferred from the model were more informative of the cortical state than a direct readout of the spiking activity of single neurons or of the population. Further, the spiking states predicted both the trial-by-trial variability in sensory responses and one aspect of behavior, whisking activity.

Discussion

Our results show how classical measurements of brain state relate to neural population spiking dynamics at the scale of the microcircuit and provide an approach for quantitative mapping of brain state dynamics across brain areas.

Biophysical characterization and modelling of SCN1A gain-of-function predicts interneuron hyperexcitability and a predisposition to network instability through homeostatic plasticity

SCN1A gain-of-function variants are associated with early onset developmental and epileptic encephalopathies (DEEs) that possess distinct clinical features compared to Dravet syndrome caused by SCN1A loss-of-function. However, it is unclear how SCN1A gain-of-function may predispose to cortical hyper-excitability and seizures. Here, we first report the clinical features of a patient carrying a de novo SCN1A variant (T162I) associated with neonatal-onset DEE, and then characterize the biophysical properties of T162I and three other SCN1A variants associated with neonatal-onset DEE (I236V) and early infantile DEE (P1345S, R1636Q). In voltage clamp experiments, three variants (T162I, P1345S and R1636Q) exhibited changes in activation and inactivation properties that enhanced window current, consistent with gain-of-function. Dynamic action potential clamp experiments utilising model neurons incorporating Na_v1.1. channels supported a gain-of-function mechanism for all four variants. Here, the T162I, I236V, P1345S, and R1636Q variants exhibited higher peak firing rates relative to wild type and the T162I and R1636Q variants produced a hyperpolarized threshold and reduced neuronal rheobase. To explore the impact of these variants upon cortical excitability, we used a spiking network model containing an excitatory pyramidal cell (PC) and parvalbumin positive (PV) interneuron population. SCN1A gain-of-function was modelled by enhancing the excitability of PV interneurons and then incorporating three simple forms of homeostatic plasticity that restored pyramidal cell firing rates. We found that homeostatic plasticity mechanisms exerted differential impact upon network function, with changes to PV-to-PC and PC-to-PC synaptic strength predisposing to network instability. Overall, our findings support a role for SCN1A gain-of-function and inhibitory interneuron hyperexcitability in early onset DEE. We propose a mechanism through which homeostatic plasticity pathways can predispose to pathological excitatory activity and contribute to phenotypic variability in SCN1A disorders.

Fundamental Neurochemistry Review: GABAA receptor neurotransmission and epilepsy: Principles, disease mechanisms and pharmacotherapy

Epilepsy is a common neurological disorder associated with alterations of excitation-inhibition balance within brain neuronal networks. GABA_A receptor neurotransmission is the most prevalent form of inhibitory neurotransmission and is strongly implicated in both the pathophysiology and treatment of epilepsy, serving as a primary target for antiseizure medications for over a century. It is now established that GABA exerts a multifaceted influence through an array of GABA_A receptor subtypes that extends far beyond simply negating excitatory activity. As the role of GABA_A neurotransmission within inhibitory circuits is elaborated, this will enable the development of precision therapies that correct the network dysfunction underlying epileptic pathology.

SCN1A channelopathies: Navigating from genotype to neural circuit dysfunction

The SCN1A gene is strongly associated with epilepsy and plays a central role for supporting cortical excitation-inhibition balance through the expression of Na_V1.1 within inhibitory interneurons. The phenotype of SCN1A disorders has been conceptualized as driven primarily by impaired interneuron function that predisposes to disinhibition and cortical hyperexcitability. However, recent studies have identified SCN1A gain-of-function variants associated with epilepsy, and the presence of cellular and synaptic changes in mouse models that point toward homeostatic adaptations and complex network remodeling. These findings highlight the need to understand microcircuit-scale dysfunction in SCN1A disorders to contextualize genetic and cellular disease mechanisms. Targeting the restoration of microcircuit properties may be a fruitful strategy for the development of novel therapies.

[Auditory response patterns of mouse primary auditory cortex to sound stimuli]

OBJECTIVE

To investigate the auditory response patterns of mouse primary auditory cortex (A1) neurons.

METHODS

In vivo cell-attached recordings and neural network modeling were performed to detect the changes in response patterns of A1 neurons of awake C57BL/6J mice to sound stimulation with varying lengths. A1 neuron signals were recorded for 216 neurons in 20 awake mice using a target sound stimulation sequence, and the classification and response characteristics of A1 neuron response patterns were examined using post-stimulus spike time histograms. To simulate the diversity of the A1 neuron response patterns, an A1 neuron model was established based on the Wilson-Cowan model and integral-firing model. The neuron connection weight parameters in the model were calculated by examining the micro loop structure of the pyramidal neurons, parvalbumin neurons, and somatostatin neurons in the A1 region, and the A1 neural network information coding model was constructed.

RESULTS

The Onset response neurons only had fast spike response within 10 to 40 ms after the beginning of noise stimulation (122 neurons). The Sustained response neurons had spike response continuously during the noise stimulation (26 neurons). The On-off response neurons had fast spike response after the beginning and the end of noise stimulation (40 neurons). The Offset response neurons only had fast spike response within 10 to 40 ms after the end of noise stimulation (22 neurons). In the neural network model, the Onset peak neural activities of A1 pyramidal neurons, parvalbumin neurons, and somatostatin neurons were 0.7483, 0.5236 and 0.9427, respectively, and their response half peak widths were 18.5 ms, 12 ms and 31 ms during the 100 ms noise stimulation, respectively. By changing the feedforward excitation and synaptic inhibition time constants in the model, the neurons generated numerous different types of spike train.

CONCLUSION

The auditory response of mouse A1 neurons to sound stimuli shows mainly the Onset, Sustained, On-off, and Offset response patterns.

Modernizing the NEURON Simulator for Sustainability, Portability, and Performance

The 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 NEURON's 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 NEURON's 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.