Rearrangement of receptive field topography after intracortical and peripheral stimulation: the role of plasticity in inhibitory pathways

Abstract

Intracortical microstimulation (ICMS) of a single site in the somatosensory cortex of rats and monkeys for 2-6 h increases the number of neurons responsive to the skin region corresponding to the ICMS-site receptive field (RF), with very little effect on the position and size of the ICMS-site RF, and the response evoked at the ICMS site by tactile stimulation. Large changes in RF topography are also observed following several weeks of repetitive stimulation of a restricted skin region during tactile frequency discrimination training in monkeys. It has been suggested that these changes in RF topography are caused by competitive learning in excitatory pathways. This paper analyses the possible role of lateral inhibitory synaptic plasticity in producing cortical plasticity after ICMS and peripheral conditioning in adult animals. The 'EXIN' (afferent excitatory and lateral inhibitory) synaptic plasticity rules are used to model RF changes after ICMS and peripheral stimulation. The EXIN model produces RF topographical changes similar to those observed experimentally. It is shown that lateral inhibitory pathway plasticity is sufficient to model RF changes and increase in position discrimination after peripheral stimulation. Several novel and testable predictions are made based on the EXIN model.

Excitation and Inhibition Jointly Regulate Cortical Reorganization in Adult Rats

“…Based on theoretical and experimental work, it has been suggested that the maintenance of cortical maps and RFs arises from a meticulous balance of both local and global mechanisms based on feedforward and recurrent inputs (Stratford et al, 1996;Miller et al, 2001;Kalarickal and Marshall, 2002), on alterations of both excitatory and inhibitory mechanisms (Heizmann, 1993;Jones, 2000;Kalarickal and Marshall, 2002;Rosselet et al, 2006), and on a continues protein synthesis (Kleim et al, 2003).…”

Sensorimotor Learning Biases Choice Behavior: A Learning Neural Field Model for Decision Making

“…We use two variants of the basic Hebbian rule that incorporate an implicit limit of weight growth, the ‘instar’ and ‘outstar’ learning rules in the formulation of Marshall [42]. These rules have successfully been used in topographical dynamic neural networks that are comparable to DNFs [43], [44]. We further adapted them to be used in a reward-dependent manner: As in the original rules, the weights between active regions are strengthened if the reward signal is positive, but in addition they are weakened if the reward signal is negative.…”