DYNAMIC LEARNING MODEL OF EYEBLINK CONDITIONED REFLEX: COMPUTATIONAL SIMULATION AND IMPLICATIONS

摘要

Cerebellar cortex is known to be involved in acquisition and expression of eyeblink conditioned reflex. These phenomena imply temporal intervals learning. Several cell scale and network scale mechanisms have been proposed to produce eyeblink conditioned reflex. In this paper we briefly review the main theories concerning temporal coding, and we propose an alternative way of producing and storing delays and signal sequences after supervised learning. A network of Leaky Integrate-and-Fire (LIF) neurons is built, taking into account several cerebellar features. This network is then trained to produce eyeblink delays using (ⅰ) the classical conditioning paradigm and (ⅱ) known data on cerebellar spike timing dependent plasticity (STDP). The resulting model behaves like a temporal filter. It associates and anticipates on events occurences to be learned (ie. delayed signals) with a given input stimulus.