NETWORKS WITH LATERAL CONNECTIVITY .3. PLASTICITY AND REORGANIZATION OF SOMATOSENSORY CORTEX

1. Mechanisms underlying cortical reorganizations were studied using a three-layered neural network model with neuronal groups already forme d in the cortical layer. 2. Dynamic changes induced in cortex by behav ioral training or intracortical microstimulation (ICMS) were simulated . Both manipulations resulted in reassembly of neuronal groups and for mation of stimulus-dependent assemblies. Receptive fields of neurons a nd cortical representation of inputs also changed. Many neurons that h ad been weakly responsive or silent became active. 3. Several types of learning models were examined in simulating behavioral training, ICMS -induced dynamic changes, deafferentation, or cortical lesion. Each le arning model most accurately reproduced features of experimental data from different manipulations, suggesting that more than one plasticity mechanism might be able to induce dynamic changes in cortex. 4. After skin or cortical stimulation ceased, as spontaneous activity continue d, the stimulus-dependent assemblies gradually reverted into structure -dependent neuronal groups. However, relationships among individual ne urons and identities of many neurons did not return to their original states. Thus a different set of neurons would be recruited by the same training stimulus sequence on its next presentation. 5. We also repro duced several typical long-term reorganizations caused by pathological manipulations such as cortical lesions, input loss, and digit fusion. 6. In summary, with Hebbian plasticity rules on lateral connections, t he network model is capable of reproducing most characteristics of exp eriments on cortical reorganization. We propose that an important mech anism underlying cortical plastic changes is formation of temporary as semblies that are related to receipt of strongly synchronized localize d input. Such stimulus-dependent assemblies can be dissolved by sponta neous activity after removal of the stimuli.