-th neuron is given by a sum of nonlinear, usually "sigmoidal" functions g of the inputs as: f j = ∑ i g ( w j i ′ x i + b j ) =\sum _g\left(w_'x_+b_\right)} . this response is then fed as input into other neurons and so on. the goal is to optimize the weights of the neurons to output a desired response at the output layer respective to a set given inputs at the input layer. this optimization of the neuron weights is often performed using the backpropagation algorithm and an optimization method such as gradient descent or newton's method of optimization. backpropagation compares the output of the network with the expected output from the training data, then updates the weights of each neuron to minimize the contribution of that individual neuron to the total error of the network. genetic algorithms genetic algorithms are used to evolve neural (and sometimes body) properties in a model brain-body-environment system so as to exhibit some desired behavioral performance. the evolved agents can then be subjected to a detailed analysis to uncover their principles of operation. evolutionary approaches are particularly useful for exploring spaces of possible solutions to a given behavioral task because these approaches minimize a priori assumptions about how a given behavior ought to be instantiated. they can also be useful for exploring different ways to complete a computational neuroethology model when only partial neural circuitry is available for a biological system of interest. neuron the neuron software, developed at duke university, is a simulation environment for modeling individual neurons and networks of neurons. the neuron environment is a self-contained environment allowing interface through its gui or via scripting with hoc or python. the neuron simulation engine is based on a hodgkin–huxley type model using a borg–graham formulation. several examples of models written in neuron are available from the online database modeldb. embodiment in electronic hardware conductance-based silicon neurons nervous systems differ from the majority of silicon-based computing devices in that they resemble analog computers (not digital data processors) and massively parallel processors, not sequential processors. to model nervous systems accurately, in real-time, alternative hardware is required. the most realistic circuits to date make use of analog properties of existing digital electronics (operated under non-standard conditions) to realize hodgkin–huxley-type models in silico. retinomorphic chips