On the design of neural networks in the brain by genetic evolution.

Hypotheses are presented of what could be specified by genes to enable the different functional architectures of the neural networks found in the brain to be built during ontogenesis. It is suggested that for each class of neuron (e.g., hippocampal CA3 pyramidal cells) a small number of genes specify the generic properties of that neuron class (e.g., the number of neurons in the class, and the firing threshold), while a larger number of genes specify the properties of the synapses onto that class of neuron from each of the other classes that makes synapses with it. These properties include not only which other neuron classes the synapses come from, but whether they are excitatory or inhibitory, the nature of the learning rule implemented at the synapse, and the initial strength of such synapses. In a demonstration of the feasibility of the hypotheses to specify the architecture of different types of neuronal network, a genetic algorithm is used to allow the evolution of genotypes which are capable of specifying neural networks that can learn to solve particular computational tasks, including pattern association, autoassociation, and competitive learning. This overall approach allows such hypotheses to be further tested, improved, and extended with the help of neuronal network simulations with genetically specified architectures in order to develop further our understanding of how the architecture and operation of different parts of brains are specified by genes, and how different parts of our brains have evolved to perform particular functions