Neural Comput - Statistical mechanics of reward-modulated learning in decision-making networks.

Tópicos

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Resumo

The neural substrates of decision making have been intensively studied using experimental and computational approaches. Alternative-choice tasks accompanying reinforcement have often been employed in investigations into decision making. Choice behavior has been empirically found in many experiments to follow Herrnstein's matching law. A number of theoretical studies have been done on explaining the mechanisms responsible for matching behavior. Various learning rules have been proved in these studies to achieve matching behavior as a steady state of learning processes. The models in the studies have consisted of a few parameters. However, a large number of neurons and synapses are expected to participate in decision making in the brain. We investigated learning behavior in simple but large-scale decision-making networks. We considered the covariance learning rule, which has been demonstrated to achieve matching behavior as a steady state (Loewenstein & Seung, 2006 ). We analyzed model behavior in a thermodynamic limit where the number of plastic synapses went to infinity. By means of techniques of the statistical mechanics, we can derive deterministic differential equations in this limit for the order parameters, which allow an exact calculation of the evolution of choice behavior. As a result, we found that matching behavior cannot be a steady state of learning when the fluctuations in input from individual sensory neurons are so large that they affect the net input to value-encoding neurons. This situation naturally arises when the synaptic strength is sufficiently strong and the excitatory input and the inhibitory input to the value-encoding neurons are balanced. The deviation from matching behavior is caused by increasing variance in the input potential due to the diffusion of synaptic efficacies. This effect causes an undermatching phenomenon, which has been often observed in behavioral experiments.

Resumo Limpo

neural substrat decis make intens studi use experiment comput approach alternativechoic task accompani reinforc often employ investig decis make choic behavior empir found mani experi follow herrnstein match law number theoret studi done explain mechan respons match behavior various learn rule prove studi achiev match behavior steadi state learn process model studi consist paramet howev larg number neuron synaps expect particip decis make brain investig learn behavior simpl largescal decisionmak network consid covari learn rule demonstr achiev match behavior steadi state loewenstein seung analyz model behavior thermodynam limit number plastic synaps went infin mean techniqu statist mechan can deriv determinist differenti equat limit order paramet allow exact calcul evolut choic behavior result found match behavior steadi state learn fluctuat input individu sensori neuron larg affect net input valueencod neuron situat natur aris synapt strength suffici strong excitatori input inhibitori input valueencod neuron balanc deviat match behavior caus increas varianc input potenti due diffus synapt efficaci effect caus undermatch phenomenon often observ behavior experi

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