MODELING THE ACQUISITION OF GOAL-DIRECTED BEHAVIORS BY POPULATIONS OFNEURONS

Recent neurophysiological studies have revealed the patterns of neuron al activity during the acquisition of goal-directed behaviors, both in single cells, and in large populations of neurons. We propose a model which helps three sets of experimental results in the monkey to be un derstood: (1) activity of single cells vary greatly and only populatio n activities are causally related to behavior. The model shows how a p opulation of stochastic neurons, whose behaviors vary widely, can lear n a skilled conditioned movement with only local activity-dependent sy naptic changes. (2) typical changes in neuronal activity occur when th e rules governing the behavior are changed, i.e. when the relationship between cues and actions to reach a goal changes over time. There are two types of neuronal patterns during changes in reward contingency: a monotonic increasing pattern and a non-monotonic pattern which follo ws the change in the way the reward is obtained. Units in the model di splay these two types of change, which correspond to synaptic modifica tions related to the encoding of the behavioral significance of sensor y and motor events. (3) These two patterns of neuronal activity define two populations whose anatomical distributions in the frontal lobe ov erlap with a gradient organized in the rostro-caudal direction. The mo del consists of two artificial neural networks, defined by the same se t of equations, but which differ in the values of two parameters (P an d Q). P defines the adaptive properties of processing units and Q desc ribes the coding of information. The model suggests that a balance in the relative strengths of these parameters distributed along a rostro- caudal gradient can explain the distribution of neuronal types in the frontal lobe of the monkey.