The steady progress in data collection and empirical evidence on neural phenomena calls for a deeper theoretical understanding of the processes involved. Mathematics offers both a language and a general framework to achieve this understanding. The corresponding mathematical description must account for two very particular - and related - attributes: neural phenomena are the result of a complex architecture acting at multiple scales and observations exhibit a high degree of variability under similar experimental conditions. These features are a strong indication that stochastics must play a dominant role in the mathematical foundations of neuroscience. In addition, conceptual understanding and predictive power require that the mathematical models be contrasted and validated by experimental observations. Progress in theoretical neuroscience requires, therefore, a concerted collaborative effort of researchers in different areas of probability and statistics and experts in applied areas of neuroscience.
The purpose of the workshop is to bring together researchers in all these areas for a week of exchange of ideas, mutual learning, overview of past accomplishments and failures and, as a result, determination of future research directions and design of new collaborations. The program combines keynote talks with ample time for general and small group discussions.
The workshop will focus on the following main topics:
· Networks of neurons and interacting processes.
· Gibbsian vs process descriptions.
· Emergence of non-generic attributes.
· Random networks and neural phenomena.
· Simulations in neuroscience.
· Relation between structure and function in neural networks.