title: Modeling fast stimulus-response association learning along the occipito-parieto-frontal pathway following rule instructions.
abstract: On the basis of instructions, humans are able to set up associations between sensory and motor areas of the brain separated by several neuronal relays, within a few seconds. This paper proposes a model of fast learning along the dorsal pathway, from primary visual areas to pre-motor cortex. A new synaptic learning rule is proposed where synaptic efficacies converge rapidly toward a specific value determined by the number of active inputs of a neuron, respecting a principle of resource limitation in terms of total synaptic input efficacy available to a neuron. The efficacies are stable with regards to repeated arrival of spikes in a spike train. This rule reproduces the inverse relationship between initial and final synaptic efficacy observed in long-term potentiation (LTP) experiments. Simulations of learning experiments are conducted in a multilayer network of leaky integrate-and-fire (LIF) spiking neuron models. It is proposed that cortical feedback connections convey a top-down learning-enabling signal that guides bottom-up learning in "hidden" neurons that are not directly exposed to input or output activity. Simulations of repeated presentation of the same stimulus-response pair, show that, under conditions of fast learning with probabilistic synaptic transmission, the networks tend to recruit a new sub-network at each presentation to represent the association, rather than re-using a previously trained one. This increasing allocation of neural resources results in progressively shorter execution times, in line with experimentally observed reduction in response time with practice. This article is part of a Special Issue entitled: Neural Coding.
authors: Centre for Robotic and Neural Systems, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK. gbugmann@plymouth.ac.uk
authors: Guido
authors: G
authors: Bugmann
mesh_terms: D001245:Association Learning / Q000502:physiology*; D005625:Frontal Lobe / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000502:physiology; D009778:Occipital Lobe / Q000502:physiology*; D010296:Parietal Lobe / Q000502:physiology*; D011930:Reaction Time / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology