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Special Issue "Neural Dynamics and Information Processing"

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Institute for Adaptive & Neural Computation, School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK
Interests: computational neuroscience; machine learning; information theory; statistics

Dr. Nina Kudryashova

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Institute for Adaptive & Neural Computation, School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK
Interests: theoretical biology; computational neuroscience; nonlinear dynamics

Special Issue Information

Dear Colleagues,

Information theory has emerged as a framework for investigating how neural populations operate. However, it is difficult to scale information-theoretic methods to large neural populations or long recordings. At the same time, progress in recording techniques yields ever-increasing numbers of simultaneously recorded neurons, providing rich datasets for the analysis of underlying neural population dynamics. New modeling and methodological approaches are developed to investigate neural dynamics and exploit the advantages of information-theoretic measures in this context, dealing with the challenges of computational and sample complexities.

This Special Issue is concerned with the latest contributions to modeling neural dynamics, in conjunction with approaches from information theory, which are applied to tuning and interpreting dynamical models. We also invite research at the intersection between machine learning and neuroscience aiming to scale dynamics or information theory approaches to larger scales.

Dr. Arno Onken
Dr. Nina Kudryashova
Guest Editors

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Research

Open AccessArticle

Information Encoding in Bursting Spiking Neural Network Modulated by Astrocytes

Sergey V. Stasenko

and

Victor B. Kazantsev

Entropy 2023, 25(5), 745; https://doi.org/10.3390/e25050745 - 01 May 2023

Viewed by 820

Abstract

We investigated a mathematical model composed of a spiking neural network (SNN) interacting with astrocytes. We analysed how information content in the form of two-dimensional images can be represented by an SNN in the form of a spatiotemporal spiking pattern. The SNN includes excitatory and inhibitory neurons in some proportion, sustaining the excitation–inhibition balance of autonomous firing. The astrocytes accompanying each excitatory synapse provide a slow modulation of synaptic transmission strength. An information image was uploaded to the network in the form of excitatory stimulation pulses distributed in time reproducing the shape of the image. We found that astrocytic modulation prevented stimulation-induced SNN hyperexcitation and non-periodic bursting activity. Such homeostatic astrocytic regulation of neuronal activity makes it possible to restore the image supplied during stimulation and lost in the raster diagram of neuronal activity due to non-periodic neuronal firing. At a biological point, our model shows that astrocytes can act as an additional adaptive mechanism for regulating neural activity, which is crucial for sensory cortical representations. Full article

(This article belongs to the Special Issue Neural Dynamics and Information Processing)

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Open AccessFeature PaperArticle

Synchrony-Division Neural Multiplexing: An Encoding Model

and

Entropy 2023, 25(4), 589; https://doi.org/10.3390/e25040589 - 30 Mar 2023

Viewed by 520

Abstract

Cortical neurons receive mixed information from the collective spiking activities of primary sensory neurons in response to a sensory stimulus. A recent study demonstrated an abrupt increase or decrease in stimulus intensity and the stimulus intensity itself can be respectively represented by the synchronous and asynchronous spikes of S1 neurons in rats. This evidence capitalized on the ability of an ensemble of homogeneous neurons to multiplex, a coding strategy that was referred to as synchrony-division multiplexing (SDM). Although neural multiplexing can be conceived by distinct functions of individual neurons in a heterogeneous neural ensemble, the extent to which nearly identical neurons in a homogeneous neural ensemble encode multiple features of a mixed stimulus remains unknown. Here, we present a computational framework to provide a system-level understanding on how an ensemble of homogeneous neurons enable SDM. First, we simulate SDM with an ensemble of homogeneous conductance-based model neurons receiving a mixed stimulus comprising slow and fast features. Using feature-estimation techniques, we show that both features of the stimulus can be inferred from the generated spikes. Second, we utilize linear nonlinear (LNL) cascade models and calculate temporal filters and static nonlinearities of differentially synchronized spikes. We demonstrate that these filters and nonlinearities are distinct for synchronous and asynchronous spikes. Finally, we develop an augmented LNL cascade model as an encoding model for the SDM by combining individual LNLs calculated for each type of spike. The augmented LNL model reveals that a homogeneous neural ensemble model can perform two different functions, namely, temporal- and rate-coding, simultaneously. Full article

(This article belongs to the Special Issue Neural Dynamics and Information Processing)

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