Download "A Biophysically Based Neural Model of Matching Law Behavior: Melioration by Stochastic Synapses"

In experiments designed to uncover the neural basis of adaptive decision making in a foraging environment, neuroscientists have
reported single-cell activities inthe lateral intraparietal cortex (LIP)that are correlated with choice options andtheir subjective values. To
investigate the underlying synaptic mechanism, we considered a spiking neuron model of decision making endowed with synaptic
plasticity that follows a reward-dependent stochastic Hebbian learning rule. This general model is tested in a matching task in which
rewards on two targets are scheduled randomly with different rates. Our main results are threefold. First, we show that plastic synapses
provide a natural way to integrate past rewards and estimate the local (in time) “return” of a choice. Second, our model reproduces the
matching behavior (i.e., the proportional allocation of choices matches the relative reinforcement obtained on those choices, which is
achieved through melioration in individual trials). Our model also explains the observed “undermatching” phenomenon and points to
biophysical constraints (such as finite learning rate and stochastic neuronal firing) that set the limits to matching behavior. Third,
although our decision model is an attractor network exhibiting winner-take-all competition, it captures graded neural spiking activities
observed in LIP, when the latter were sorted according to the choices and the difference in the returns for the two targets. These results
suggest that neurons in LIP are involved in selecting the oculomotor responses, whereas rewards are integrated and stored elsewhere,
possibly by plastic synapses and in the form of the return rather than income of choice options

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