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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.