My aim in this introductory post is to provide context for future contributions by sharing my thoughts on the role of computer scientists in the study of brain, particularly in the field of computational neuroscience. For a field with “computation” in the name, it seems that computer scientists are underrepresented. I believe there are significant open questions in neuroscience which are best addressed by those who study the theory of computation, learning, and algorithms, and the systems upon which they are premised.

In my opinion “computational neuroscience” has two definitions: the first, from Marr, is the study of the computational capabilities of the brain, their algorithmic details, and their implementation in neural circuits; the second, stemming from machine learning, is the design and application of computational algorithms, either to solve problems in a biologically-inspired manner, or to aid in the processing and interpretation of neural data. Though quite different, I believe these are complementary and arguably co-dependent endeavors. The forward hypothesis generation advocated by the former seems unlikely to get the details right without the aid of computational and statistical tools for extracting patterns from neural recordings, guiding hypothesis generation, and comparing the evidence for competing models. Likewise, attempts to infer the fundamentals of neural computation from the bottom-up without strong inductive biases appear doomed to wander the vastness of the hypothesis space. How then, can computer scientists contribute to both aspects of computational neuroscience? Continue reading “The “Computation” in Computational Neuroscience”