A P Shon | Search PubMed

PubMed2004

Motion detection and prediction through spike-timing dependent plasticity.

We describe a possible mechanism for the formation of direction- and velocity-selective cells in visual cortex through spike-timing dependent learning. We contrast the case where only feedforward excitation and inhibition signals are provided to visual neurons with the case where both feedforward and feedback signals are provided. In the feedforward-only case, neurons become selective for a broad range of velocities centered around the training velocity. However, we show that direction selectivity in this case is strongly dependent on delayed feedforward inhibition and in contrast to experimental results, becomes dramatically weaker when inhibition is reduced. When feedback connections are introduced, direction selectivity becomes much more robust due to predictive delays encoded in recurrent activity. Direction selectivity persists in the face of decreasing inhibition in a manner similar to experimental findings. The model predicts that direction-selective cells should exhibit anticipatory activity due to recurrent excitation and suggests a pivotal role for spike-timing dependent plasticity in shaping cortical circuits for visual motion detection and prediction.

Action Potentials↗

PubMed2002

Initial state randomness improves sequence learning in a model hippocampal network.

Randomness can be a useful component of computation. Using a computationally minimal, but still biologically based model of the hippocampus, we evaluate the effects of initial state randomization on learning a cognitive problem that requires this brain structure. Greater randomness of initial states leads to more robust performance in simulations of the cognitive task called transverse patterning, a context-dependent discrimination task that we code as a sequence prediction problem. At the conclusion of training, greater initial randomness during training trials also correlates with increased, repetitive firing of select individual neurons, previously named local context neurons. In essence, such repetitively firing neurons recognize subsequences, and previously their presence has been correlated with solving the transverse patterning problem. A more detailed analysis of the simulations across training trials reveals more about initial state randomization. The beneficial effects of initial state randomization derive from enhanced variation, across training trials, of the sequential states of a network. This greater variation is not uniformly present during training; it is largely restricted to the beginning of training and when novel sequences are introduced. Little such variation occurs after extensive or even moderate amounts of training. We explain why variation is high early in training, but not later. This automatic modulation of the initial-state-driven random variation through state space is reminiscent of simulated annealing where modulated randomization encourages a selectively broad search through state space. In contrast to an annealing schedule, the selective occurrence of such a random search here is an emergent property, and the critical randomization occurs during training rather than testing.

Animals↗