Literature DB >> 3755947

Simulation of the classically conditioned nictitating membrane response by a neuron-like adaptive element: response topography, neuronal firing, and interstimulus intervals.

J W Moore, J E Desmond, N E Berthier, D E Blazis, R S Sutton, A G Barto.   

Abstract

A neuron-like adaptive element with computational features suitable for classical conditioning, the Sutton-Barto (S-B) model, was extended to simulate real-time aspects of the conditioned nictitating membrane (NM) response. The aspects of concern were response topography, CR-related neuronal firing, and interstimulus interval (ISI) effects for forward-delay and trace conditioning paradigms. The topography of the NM CR has the following features: response latency after CS onset decreases over trials; response amplitude increases gradually within the ISI and attains its maximum coincidentally with the UR. A similar pattern characterizes the firing of some (but not all) neurons in brain regions demonstrated experimentally to be important for NM conditioning. The variant of the S-B model described in this paper consists of a set of parameters and implementation rules based on 10-ms computational time steps. It differs from the original S-B model in a number of ways. The main difference is the assumption that CS inputs to the adaptive element are not instantaneous but are instead shaped by unspecified coding processes so as to produce outputs that conform with the real-time properties of NM conditioning. The model successfully simulates the aforementioned features of NM response topography. It is also capable of simulating appropriate ISI functions, i.e. with maximum conditioning strength with ISIs of 250 ms, for forward-delay and trace paradigms. The original model's successful treatment of multiple-CS phenomena, such as blocking, conditioned inhibition, and higher-order conditioning, are retained by the present model.

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Year:  1986        PMID: 3755947     DOI: 10.1016/0166-4328(86)90092-6

Source DB:  PubMed          Journal:  Behav Brain Res        ISSN: 0166-4328            Impact factor:   3.332


  8 in total

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3.  Adaptively timed conditioned responses and the cerebellum: a neural network approach.

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4.  A plausible neural circuit for classical conditioning without synaptic plasticity.

Authors:  G Tesauro
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5.  Evaluating the TD model of classical conditioning.

Authors:  Elliot A Ludvig; Richard S Sutton; E James Kehoe
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6.  Adaptive timing in neural networks: the conditioned response.

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Journal:  Biol Cybern       Date:  1988       Impact factor: 2.086

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8.  Sensory prediction or motor control? Application of marr-albus type models of cerebellar function to classical conditioning.

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  8 in total

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