EVOLUTION OF ADAPTIVE NEURAL NETWORKS - THE ROLE OF VOLTAGE-DEPENDENTK+ CHANNELS

Citation
Dl. Alkon et al., EVOLUTION OF ADAPTIVE NEURAL NETWORKS - THE ROLE OF VOLTAGE-DEPENDENTK+ CHANNELS, Otolaryngology and head and neck surgery, 119(3), 1998, pp. 204-211
Citations number
22
Categorie Soggetti
Surgery,Otorhinolaryngology
ISSN journal
01945998
Volume
119
Issue
3
Year of publication
1998
Pages
204 - 211
Database
ISI
SICI code
0194-5998(1998)119:3<204:EOANN->2.0.ZU;2-C
Abstract
The vestibular pathway of the mollusk Hermissenda crassicornis mediate s a reflexive, unconditioned response to disorientation, clinging, tha t has been conserved during evolution even to the emergence of our own species. This response becomes associated with a visual stimulus (med iated by a precisely ordered visual-vestibular synaptic network) accor ding to principles of Pavlovian conditioning that are also followed in human learning. It is not entirely surprising therefore that molecula r and biophysical cascades responsible for this associative learning a ppear to function in both mollusks and mammals. In brief, combinationa l elevation of (Ca2+)(i), diacylglycerol, and arachidonic acid activat es protein kinase C to phosphorylate the Ca2+ and guanosine triphospha te-binding protein, cp20 (now called calexcitin (Nelson T, et al. Proc Natl Acad Sci USA 1996;93:13808-13)), which potently inactivates post synaptic voltage-dependent K+ currents and thereby increases synaptic weight. Longer term changes included rearrangement of synaptic termina ls and modified protein synthesis. This cascade has also been implicat ed in other associative-learning paradigms (e.g., spatial maze, olfact ory discrimination) and as a pathophysiologic target in early Alzheime r's disease. Recent molecular biologic experiments also demonstrate th e dependence of associative memory (but not longterm potentiation) on voltage-dependent K+ currents. Theoretic learning models based on thes e findings focus on dendritic spine clusters and yield computer implem entations with powerful pattern-recognition capabilities.