Analysis and modeling of sensory systems with Rate Distortion Theory

Alex Dimitrov

Center for Computational Biology
Montana State University

We present an analytical approach through which the relevant stimulus space and the corresponding neural symbols of a neuron or neural ensemble can be discovered simultaneously and quantitatively, making few assumptions about the nature of the code or relevant features. The basis for this approach is to conceptualize a neural coding scheme as a collection of stimulus-response classes akin to a dictionary or 'codebook', with each class corresponding to a spike pattern 'codeword' and its corresponding stimulus feature in the codebook. The neural codebook is derived by quantizing the neural responses into a small reproduction set, and optimizing the quantization to minimize an information-based distortion function. This approach uses tools from Rate Distortion Theory for the analysis of neural coding schemes. Its success prompted us to consider the general framework of signal quantization with minimal distortion as a model for the functioning of early sensory processing. Evidence from behavioural and neuroanatomical data suggested that symmetries in the sensory environment need to be taken into account as well. We suggest two approaches - implicit and explicit - which can incorporate the symmetries in the quantization model.