Some structural determinants of Pavlovian conditioning in artificial neural networks.

Abstract:

:This paper investigates the possible role of neuroanatomical features in Pavlovian conditioning, via computer simulations with layered, feedforward artificial neural networks. The networks' structure and functioning are described by a strongly bottom-up model that takes into account the roles of hippocampal and dopaminergic systems in conditioning. Neuroanatomical features were simulated as generic structural or architectural features of neural networks. We focused on the number of units per hidden layer and connectivity. The effect of the number of units per hidden layer was investigated through simulations of resistance to extinction in fully connected networks. Large networks were more resistant to extinction than small networks, a stochastic effect of the asynchronous random procedure used in the simulator to update activations and weights. These networks did not simulate second-order conditioning because weight competition prevented conditioning to a stimulus after conditioning to another. Partially connected networks simulated second-order conditioning and devaluation of the second-order stimulus after extinction of a similar first-order stimulus. Similar stimuli were simulated as nonorthogonal input-vectors.

journal_name

Behav Processes

journal_title

Behavioural processes

authors

Sánchez JM,Galeazzi JM,Burgos JE

doi

10.1016/j.beproc.2010.01.018

subject

Has Abstract

pub_date

2010-05-01 00:00:00

pages

526-35

issue

1

eissn

0376-6357

issn

1872-8308

pii

S0376-6357(10)00023-9

journal_volume

84

pub_type

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