Workshop 3 Description:

Workshop 3: Neural Coding
How is information about the external world and about animals's internal states represented within their nervous systems? Although a great deal is known about the relationships between the stimulus/response properties of nerve cells in a variety of systems, we are in many cases far from having a detailed understanding of the correspondence between neural activity patterns and the information represented by those patterns. We will not be able to understand the operation of any nervous system rigorously until we decipher the neural code, i.e., the system of symbols used to represent and convey information within that system. A sound, rigorous understanding of neural coding will also be essential from the standpoint of developing sophisticated models of nerve cells and systems. What aspects of neural ensemble activity patterns should be measured experimentally and incorporated into models?

There is probably no such thing as THE neural code, universal across all animals or even between different subsystems in a single animal, in the same sense as there exists a universal genetic code. However, general principles of neural encoding are starting to emerge. Much recent work in this area involves the application of sophisticated statistical approaches to the analysis of neural spike train data, and applied mathematicians have made substantial contributions to this area of research. Numerous approaches to the estimation of information-theoretic quantities from spike trains have been proposed and applied in a variety of systems. However, many of the approaches are based on very different sets of assumptions. Some significant differences have emerged in the interpretations of these information theoretic analyses, and it is unclear how much of these differences can be explained by differences in what is actually being measured, to the biases or hidden assumptions in the methodologies, or to real differences in the biological coding schemes. The whole field is ripe for a rigorous examination, comparison and normalization of the different approaches. Neuroscience would benefit greatly from an increased involvement of mathematicians and statisticians in extending the analytical framework, and from their direct involvement in designing and interpreting the experiments.

Three general aims of the workshop include the following:

• to inspire collaborative interactions between experimentalists, mathematicians and statisticians in the development of more powerful algorithms for the analysis of neural encoding, with a strong focus on refining current hypotheses for ensemble spike train coding;
• to establish a sound, rigorous basis for examining the differences in findings within and across preparations;
• to consider the very challenging problems associated with extending information-theoretic analysis to networks. Examples of organizing questions to be considered in this workshop are as follows:

1. What is a channel, in the Shannon sense, within the neural processing architecture? Are single nerve cells the elemental computational units, or some larger-scale neural ensembles? This may depend on the level of analysis (e.g., whether the system is being studied with respect to the operations being carried out within single-cells, all the way up to systems consisting of millions of neurons distributed between several brain areas.
2. What is the nature and quantity of information represented at each processing stage of a neural subsystem? What is being represented? (i.e., what is the relevant stimulus world for the system under study?)
3. What is the code with which that information is represented, transmitted and operated upon across those channels? A variety of encoding schemes have been proposed, ranging from simple linear rate codes to complex nonlinear ensemble codes. What are rigorous criteria for identifying linear and non-linear codes? For static vs. dynamic temporal codes? What algorithms should we develop and apply to identify these different schemes?
4. Are nerve cells and networks noisy or deterministic? What are the principle sources of noise, from the biophysical level of macromolecules and ion channels to the dynamics of large networks of synaptically interconnected cells? To what extent must stochastic behavior be incorporated into neural models, and at what phenomenological level, in order to insure validity of those models?