Modeling Working Memory in a Spiking Neuron Network Accompanied by Astrocytes

Abstract

We propose a novel biologically plausible computational model of working memory (WM) implemented by a spiking neuron network (SNN) interacting with a network of astrocytes. The SNN is modeled by synaptically coupled Izhikevich neurons with a non-specific architecture connection topology. Astrocytes generating calcium signals are connected by local gap junction diffusive couplings and interact with neurons via chemicals diffused in the extracellular space. Calcium elevations occur in response to the increased concentration of the neurotransmitter released by spiking neurons when a group of them fire coherently. In turn, gliotransmitters are released by activated astrocytes modulating the strength of the synaptic connections in the corresponding neuronal group. Input information is encoded as two-dimensional patterns of short applied current pulses stimulating neurons. The output is taken from frequencies of transient discharges of corresponding neurons. We show how a set of information patterns with quite significant overlapping areas can be uploaded into the neuron-astrocyte network and stored for several seconds. Information retrieval is organized by the application of a cue pattern representing one from the memory set distorted by noise. We found that successful retrieval with the level of the correlation between the recalled pattern and ideal pattern exceeding 90% is possible for the multi-item WM task. Having analyzed the dynamical mechanism of WM formation, we discovered that astrocytes operating at a time scale of a dozen of seconds can successfully store traces of neuronal activations corresponding to information patterns. In the retrieval stage, the astrocytic network selectively modulates synaptic connections in the SNN leading to successful recall. Information and dynamical characteristics of the proposed WM model agrees with classical concepts and other WM models.

Astrocytes mediate analogous memory in a multi-layer neuron–astrocyte network

“…A biologically plausible computational model of working memory implemented via SNN interaction with a network of astrocytes was first proposed in our recent work [44]. The astrocytes operate via calcium transients at a much slower time scale of a few seconds by releasing gliotransmitters that modulate synaptic transmission in neurons and, hence, their firing rate.…”

“…Our work [44], as the majority of conceptual and mathematical models of neuronal memory, operates with binary information. However, the real-world data are analogous, not binary.…”

“…In this paper, we employ our bioinspired model of SNN accompanied by astrocytes [44] and show how it can reliably store ''colored'' images for several seconds. To the best of the authors' knowledge, this is the first time that a spiking neuron-astrocyte network has been shown to be able to simulate a robust analogous memory that can be used in brain-inspired artificial intelligence frameworks.…”

“…1. Detailed description of the model structure and its parameters is found in our previous paper [44]. The key mathematical details are summarized in Appendix A.…”

“…1. The model was integrated using the 4th order Runge-Kutta method with a time step of 0.1 ms. All parameters used in this computational study are given in Table 2 and our previous paper [44]. The code is available at https://github.com/altergot/ neuro-astro-network-grayscale.…”