Abstract
Modeling in computational neuroscience generally falls into one of two categories. In one approach, differential equations describing the state of a neuron are developed by studying the electrical properties of a neuron under laboratory conditions. The equations are then used to model the behavior of a neuron in an idealized situation1,2. Mathematical models tend to be computationally expensive and difficult to scale up to large neural networks. An alternative approach is artificial neural networks (ANN). ANNs sacrifice features of real brain networks, such as, synaptic transmission, temporal properties and architecture for the ability to build large assemblies of neuronal elements3.
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