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Inferring and testing optimality in perception and neurons


Matthew Chalk | INSERM Paris, France
Wiktor Młynarski | IST Austria


Many influential theories of neural computation are based on the idea that the brain has evolved to perform certain computations near-optimally. Prominent examples of theoretical frameworks grounded in the notion of optimality include efficient encoding, decision making and reinforcement learning. Despite their conceptual importance, these theories are often difficult to test and/or falsify on real neural data. This is primarily because currently we lack statistical tools to rigorously define and quantify the degree of optimality of a given neural system. Further, it is unclear how such optimality theories can be applied to neural data when we don’t know a priori what computation is being performed by the system in question.

Recently, a number of approaches aimed at rigorously testing and inferring optimal computations in neural systems and behaviour has emerged. In this workshop we will bring together neural theorists and cognitive scientists to discuss these recent developments. We will examine the overlap and similarities between seemingly disparate domains by asking when and how it could be possible to infer signatures of optimality in animals and neurons. We will also ask how approaches based on notions of optimality could be complemented by traditional, bottom-up statistical models of neural coding and behaviour.