“Live” neuron and optimal learning rule

Emelyanov-Yaroslavsky, L. B.; Potapov, V. I.

doi:10.1007/bf00201803

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…where ξ 1 and ξ 2 are constants: 1 ≥ ξ 1 > 0 quantifies the credibility score loss (forgetting), while the term with ξ 2 > 0 means that every time the agent i activates, its score increases proportional to the weight τ ki (t) of its influence on k. If i is not active, m i (t) = 0, its score decays. Development of complex network theory motivated many models, where the links and nodes are coupled [46,47,[74][75][76][77][78][79][80][81]; see [82] for an extensive review. Neurophysiological motivation for studying such models comes from the synaptic plasticity of neuronal connections that can change on various time scales [65,83].…”

Section: Credibility Scoresmentioning

confidence: 99%

Emergence of Leadership in Communication

Allahverdyan¹,

Galstyan²

2016

PLoS ONE

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We study a neuro-inspired model that mimics a discussion (or information dissemination) process in a network of agents. During their interaction, agents redistribute activity and network weights, resulting in emergence of leader(s). The model is able to reproduce the basic scenarios of leadership known in nature and society: laissez-faire (irregular activity, weak leadership, sizable inter-follower interaction, autonomous sub-leaders); participative or democratic (strong leadership, but with feedback from followers); and autocratic (no feedback, one-way influence). Several pertinent aspects of these scenarios are found as well—e.g., hidden leadership (a hidden clique of agents driving the official autocratic leader), and successive leadership (two leaders influence followers by turns). We study how these scenarios emerge from inter-agent dynamics and how they depend on behavior rules of agents—in particular, on their inertia against state changes.

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Section: Credibility Scoresmentioning

confidence: 99%

Emergence of Leadership in Communication

Allahverdyan¹,

Galstyan²

2016

PLoS ONE

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“…There is a large body of work on collective activity patterns in neuronal systems [30][31][32][33][34][35]. This activity relates to proactive functions of the brain, e.g., attention and memory [36,37].…”

Section: Introductionmentioning

confidence: 99%

Memory-induced mechanism for self-sustaining activity in networks

Allahverdyan

Steeg²,

Galstyan³

2015

Phys. Rev. E

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We study a mechanism of activity sustaining on networks inspired by a well-known model of neuronal dynamics. Our primary focus is the emergence of self-sustaining collective activity patterns, where no single node can stay active by itself, but the activity provided initially is sustained within the collective of interacting agents. In contrast to existing models of self-sustaining activity that are caused by (long) loops present in the network, here we focus on tree-like structures and examine activation mechanisms that are due to temporal memory of the nodes. This approach is motivated by applications in social media, where long network loops are rare or absent. Our results suggest that under a weak behavioral noise, the nodes robustly split into several clusters, with partial synchronization of nodes within each cluster. We also study the randomly-weighted version of the models where the nodes are allowed to change their connection strength (this can model attention redistribution), and show that it does facilitate the self-sustained activity.

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Self-organization of day cycle and hierarchical associative memory in “live” neural network

Emelyanov-Yaroslavsky¹,

Potapov²

1992

Biol. Cybern.

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The "live" neural network model is proposed on the basis of "live" neuron model and optimal learning rule. By means of numerical simulation the initial stages of neural network self-organization have been shown: (1) the formation of two activity forms, which are identified with sleep and awaking, and (2) the self-organization of hierarchical associative memory when feeding a receptor excitation to the neural network. The energetic profit of self-organization is demonstrated. The formation of neural ensembles, playing the role of generalized neurons, is obtained.

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“Live” neuron and optimal learning rule

Cited by 4 publications

References 3 publications

Emergence of Leadership in Communication

Emergence of Leadership in Communication

Memory-induced mechanism for self-sustaining activity in networks

Self-organization of day cycle and hierarchical associative memory in “live” neural network

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