Learning by stimulation avoidance: A principle to control spiking neural networks dynamics

Learning based on networks of real neurons, and learning based on biologically inspired models of neural networks, have yet to find general learning rules leading to widespread applications. In this paper, we argue for the existence of a principle allowing to steer the dynamics of a biologically ins...

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Veröffentlicht in:PloS one 2017-02, Vol.12 (2), p.e0170388-e0170388
Hauptverfasser: Sinapayen, Lana, Masumori, Atsushi, Ikegami, Takashi
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Masumori, Atsushi
Ikegami, Takashi
description Learning based on networks of real neurons, and learning based on biologically inspired models of neural networks, have yet to find general learning rules leading to widespread applications. In this paper, we argue for the existence of a principle allowing to steer the dynamics of a biologically inspired neural network. Using carefully timed external stimulation, the network can be driven towards a desired dynamical state. We term this principle "Learning by Stimulation Avoidance" (LSA). We demonstrate through simulation that the minimal sufficient conditions leading to LSA in artificial networks are also sufficient to reproduce learning results similar to those obtained in biological neurons by Shahaf and Marom, and in addition explains synaptic pruning. We examined the underlying mechanism by simulating a small network of 3 neurons, then scaled it up to a hundred neurons. We show that LSA has a higher explanatory power than existing hypotheses about the response of biological neural networks to external simulation, and can be used as a learning rule for an embodied application: learning of wall avoidance by a simulated robot. In other works, reinforcement learning with spiking networks can be obtained through global reward signals akin simulating the dopamine system; we believe that this is the first project demonstrating sensory-motor learning with random spiking networks through Hebbian learning relying on environmental conditions without a separate reward system.
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subjects Analysis
Animals
Avoidance
Avoidance learning
Avoidance Learning - physiology
Behavior
Biological effects
Biology and Life Sciences
Computer and Information Sciences
Computer simulation
Dopamine
Engineering and Technology
Environmental conditions
Experiments
Firing pattern
Humans
Laboratories
Machine learning
Medicine and Health Sciences
Models, Neurological
Motor skill learning
Neural circuitry
Neural networks
Neural Networks (Computer)
Neurons
Neurons - physiology
Neurosciences
Physical Sciences
Pruning
Reinforcement
Sensorimotor integration
Simulation
Social Sciences
Spiking
Stimulation
Synapses - physiology
title Learning by stimulation avoidance: A principle to control spiking neural networks dynamics
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