Adaptive timing in neural networks: The conditioned response

Abstract

A conditioned response not only reflects knowledge of an association between two events, a CS and a US, it also reflects knowledge about the timing of these events. A neural network and set of learning rules that generates appropriately timed conditioned response waveforms is presented. The model is capable of simulating some of the basic temporal properties of conditioned responses exhibited in biological systems, including (1) decreasing onset latency during acquisition training, (2) peak amplitude accurring at the temporal locus of the US, (3) inhibition of delay, and (4) trace conditioning. The model is also capable of simulating complex CR waveforms under certain conditions, and these simulations are compared with the results of behavioral experiments. The temporally adaptive responses are achieved by virtue of stimulus trace processes that are built into the network architecture.