Highly Heterogeneous Excitatory Connections Require Less Amount of Noise to Sustain Firing Activities in Cortical Networks

Abstract

Cortical networks both in vivo and in vitro sustain asynchronous irregular firings with extremely low frequency. To realize such self-sustained activity in neural network models, balance between excitatory and inhibitory activities is known to be one of the keys. In addition, recent theoretical studies have revealed that another feature commonly observed in cortical networks, i.e., sparse but strong connections and dense weak connections, plays an essential role. The previous studies, however, have not thoroughly considered the cooperative dynamics between a network of such heterogeneous synaptic connections and intrinsic noise. The noise stimuli, representing inherent nature of the neuronal activities, e.g., variability of presynaptic discharges, should be also of significant importance for sustaining the irregular firings in cortical networks. Here, we numerically demonstrate that highly heterogeneous distribution, typically a lognormal type, of excitatory-to-excitatory connections, reduces the amount of noise required to sustain the network firing activities. In the sense that noise consumes an energy resource, the heterogeneous network receiving less amount of noise stimuli is considered to realize an efficient dynamics in cortex. A noise-driven network of bi-modally distributed synapses further shows that many weak and a few very strong synapses are the key feature of the synaptic heterogeneity, supporting the network firing activity.

Effect of steady-state response versus excitatory/inhibitory balance on spiking synchronization in neural networks with log-normal synaptic weight distribution

“…Thus, spontaneous activity is said to be produced by stochastic resonance. This model of spontaneous activity has been widely used for clarifying the function of cortical neural networks and further expanded upon (Hiratani et al 2013;Omura et al 2015;Kada et al 2018;Nobukawa et al 2019Nobukawa et al , 2020. For example, a log-normal distribution of EPSPs may enhance learning and memory (Hiratani et al 2013;Omura et al 2015).…”

“…For example, a log-normal distribution of EPSPs may enhance learning and memory (Hiratani et al 2013;Omura et al 2015). Furthermore, by incorporating the dual nature of complex network structures observed in the cortex (Watanabe et al 2016) into a spiking neural network with a long-tailed EPSP distribution, the spatiotemporally complex neural activity of the real cortex was recently reproduced (Kada et al 2018;Nobukawa et al 2019). Hence, a long-tailed distribution of EPSPs in local cortical networks is an important factor for determining the spatiotemporal characteristics of neural activity and enhancing brain functionality.…”

“…2016 ) into a spiking neural network with a long-tailed EPSP distribution, the spatiotemporally complex neural activity of the real cortex was recently reproduced (Kada et al. 2018 ; Nobukawa et al. 2019 ).…”

Long-Tailed Characteristic of Spiking Pattern Alternation Induced by Log-Normal Excitatory Synaptic Distribution

“…Moreover, Kriener et al [31] reported that this spontaneous activity that was induced by the log-normal distribution of EPSPs exhibited a complex behavior with slow temporal transitions between bistable activity states. Recently, Kada et al [32] and Nobukawa et al [33] introduced a spiking neural network with a physiologically observed duality of a complex synaptic connection depending on the magnitude of EPSP. Furthermore, we reproduced the complex spatiotemporal spontaneous activity with multiple states of neural activity [33].…”