The callosal dilemma: explaining diaschisis in the context of hemispheric rivalry via a neural network model.
It is often suggested that a major factor in diaschisis is the loss of transcallosal excitation to the intact hemisphere from the lesioned one. However, there is long-standing disagreement in the broader experimental literature about whether transcallosal interhemispheric influences in the human brain are primarily excitatory or inhibitory. Some experimental data are apparently better explained by assuming inhibitory callosal influences. Past neural network models attempting to explore this issue have encountered the same dilemma: in intact models, inhibitory callosal influences best explain strong cerebral lateralization like that occurring with language, but in lesioned models, excitatory callosal influences best explain experimentally observed hemispheric activation patterns following brain damage. We have now developed a single neural network model that can account for both types of data, i.e., both diaschisis and strong hemisphere specialization in the normal brain, by combining excitatory callosal influences with subcortical cross-midline inhibitory interactions. The results suggest that subcortical competitive processes may be a more important factor in cerebral specialization than is generally recognized.
Reggia
J A
JA
Department of Computer Science, Institute of Advanced Computer Studies, A.V. Williams Bldg., University of Maryland, College Park, MD 20742, USA. reggia@cs.umd.edu
Goodall
S M
SM
Shkuro
Y
Y
Glezer
M
M
D001930:Brain Injuries / Q000378:metabolism / Q000503:physiopathology; D002540:Cerebral Cortex / Q000033:anatomy & histology / Q000378:metabolism*; D002560:Cerebrovascular Circulation / Q000502:physiology; D003337:Corpus Callosum / Q000502:physiology*; D013181:Cortical Spreading Depression / Q000502:physiology; D007839:Functional Laterality / Q000502:physiology*; D006801:Humans; D007858:Learning / Q000502:physiology; D008564:Membrane Potentials / Q000502:physiology; D009433:Neural Inhibition / Q000502:physiology*; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology; D014705:Verbal Behavior / Q000502:physiology