RESUMEN
A toy model of a neural network in which both Hebbian learning and reinforcement learning occur is studied. The problem of 'path interference', which makes that the neural net quickly forgets previously learned input-output relations is tackled by adding a Hebbian term (proportional to the learning rate nu) to the reinforcement term (proportional to delta) in the learning rule. It is shown that the number of learning steps is reduced considerably if 1/4Asunto(s)
Retroalimentación Psicológica
, Red Nerviosa/fisiología
, Redes Neurales de la Computación
, Refuerzo en Psicología
, Animales
, Simulación por Computador
, Condicionamiento Psicológico
, Humanos
, Modelos Neurológicos
, Procesos Estocásticos