Optimal interplay between synaptic strengths and network structure enhances activity fluctuations and information propagation in hierarchical modular networks
In network models of spiking neurons, the joint impact of network structure and synaptic parameters on activity propagation is still an open problem. Here we use an information-theoretical approach to investigate activity propagation in spiking networks with hierarchical modular topology. We observe that optimized pairwise information propagation emerges due to the increase of either (i) the global synaptic strength parameter or (ii) the number of modules in the network, while the network size remains constant. At the population level, information propagation of activity among adjacent modules is enhanced as the number of modules increases until a maximum value is reached and then decreases, showing that there is an optimal interplay between synaptic strength and modularity for population information flow. This is in contrast to information propagation evaluated among pairs of neurons, which attains maximum value at the maximum values of these two parameter ranges. By examining the network behavior under increase of synaptic strength and number of modules we find that these increases are associated with two different effects: (i) increase of autocorrelations among individual neurons, and (ii) increase of cross-correlations among pairs of neurons. The second effect is associated with better information propagation in the network. Our results suggest roles that link topological features and synaptic strength levels to the transmission of information in cortical networks.v
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Effect of Interpopulation Spike-Timing-Dependent Plasticity on Synchronized Rhythms in Neuronal Networks with Inhibitory and Excitatory Populations
We consider clustered small-world networks with both inhibitory (I) and excitatory (E) populations. This I-E neuronal network has adaptive dynamic I to E and E to I interpopulation synaptic strengths, governed by interpo…
Inhibitory loop robustly induces anticipated synchronization in neuronal microcircuits
We investigate the synchronization properties between two excitatory coupled neurons in the presence of an inhibitory loop mediated by an interneuron. Dynamical inhibition together with noise independently applied to eac…
DiversityStochastic Spike Synchronization in A Small-World Neural Network with Spike-Timing-Dependent Plasticity
We consider the Watts-Strogatz small-world network consisting of subthreshold neurons which exhibit noise-induced spikings. This neuronal network has adaptive dynamic synaptic strengths governed by the spike-timing-depen…
Hebbian Synaptic Modifications in Spiking Neurons that Learn
In this paper, we derive a new model of synaptic plasticity, based on recent algorithms for reinforcement learning (in which an agent attempts to learn appropriate actions to maximize its long-term average reward). We sh…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Burst Synchronization in A Scale-Free Neuronal Network with Inhibitory Spike-Timing-Dependent Plasticity
We are concerned about burst synchronization (BS), related to neural information processes in health and disease, in the Barab\'{a}si-Albert scale-free network (SFN) composed of inhibitory bursting Hindmarsh-Rose neurons…