A Neural Network Model of Aversive Behavior

B. S. ZANUTTO, S. LEW

Anaheim, Calgary, Zürich

Congreso; International Conference on Neural Networks; 2000

IASTED International Conference on Neural Networks

Abstract  A neural network model of aversive behavior is proposed. It has one neuron for each possible response of the animal (R), and from neurobiological bases we assumed that there is a prediction of the unconditioned stimulus (US). It is computed by another neuron. The inputs of response neurons are: the prediction, the short-term memory of the conditioned stimuli (CSs) and of the US. Based on Hebbian or anti-Hebbian learning, depending on the prediction of the US, the synapticweights of the response neurons are calaculated. The shortterm memories of CSs, of the US and of the Rs are inputs of the neuron that computes prediction. The synaptic weights of this neuronare calculated based on the delta rule. Finally, animals executeany responde higher than a threshold. While the neurobiology underlying aversive behavior may be very complex, the dynamic process that governs aversive behavior is simple, responses are associated with the input stimuli by the Hebbian rule if the prediction of the unconditioned stimulus is lower than a threshold, and by the anti-Hebbian rule if it is higher. The simulations of the model and the experimental data are compared. This network can be applied to control autonomous systems