We have had great success in applying both the principles and the related methodology to constructing models of perceptual, motor, and cognitive systems. Some detailed examples of these models can be found on the examples page. Principles of Neural Representation Neural representations are weighted linear combinations of sets of decoding functions. The matrix can be determined using the decoding functions and their complimentary encoding functions.
Neural representations have limited precision.
Therefore, the weights in the neural representations have finite accuracy. Methodology Relying on these four principles for understanding the information processing characteristics of neurobiological systems has lead us to develop the following methodology for constructing simulations of neural systems. The main goal of the first step is to describe the neural system of interest in such a way that the principles outlined previously are directly applicable to it. This description should include at least interconnectivity between subsystems, neuron response functions, neuron tuning curves, subsystem functional relations, and overall system behavior. Next, we express the functional description in mathematical terms.