A Model for the Development of Neurons in Visual Cortex.
Abstract
The object of our research is to find principles of development and organization of neural networks that can account both for experimental data on the cellular level and, when applied to large numbers of neurons that receive sensory and/or interneuronal information, for various higher level systems properties. Networks of neurons already have been constructed that can organize themselves to display some cognitive properties. Although these are still primitive compared to what animals or even machines in some cases can presently do, it is significance that these networks are self-organizing, that the global cognitive properties are the result of local modifications of the network components -- learning (so to speak) on a cellular level. This learning comes about through the modification of synaptic junctions (connections) between neurons. One crucial hypothesis concerns the form of this synaptic modification by applying them to the development of selectivity and ocular dominance in cat visual cortex, where much experimental data has been obtained in the last twenty years. This leads to a theory of synaptic evolution based on sets of coupled non-linear stochastic differential equations. Analysis and computer simulations show this theory is in good agreement with classical experimental results. In addition we obtain some new predictions that can be tested.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 05, 1984
- Accession Number
- ADA142842
Entities
People
- L. N. Cooper
Organizations
- Brown University