Computational explanation in neuroscience

Explanat Ion in Computat Ional Neurosc Ience : Causal and Non-causal

This paper examines three candidate cases of non-causal explanation in computational neuroscience. I argue that there are instances of efficient coding explanation which are strongly analogous to examples of non-causal explanation in physics and biology, as presented by Batterman (2002), Woodward (2003) and Lange (2013). By integrating Lange’s and Woodward’s accounts I offer a new way to elucid...

Minimal models and canonical neural computations: the distinctness of computational explanation in neuroscience

Editorial: Computational Neuroscience and Modeling of Diseases: Do We Need New Paradigms?

Foundations of computational neuroscience.

Most computational neuroscientists assume that nervous systems compute and process information. We discuss foundational issues such as what we mean by 'computation' and 'information processing' in nervous systems; whether computation and information processing are matters of objective fact or of conventional, observer-dependent description; and how computational descriptions and explanations ar...

Sparseness and Entropy in Semantic Change: Precedents from Early Vision

What is the difference between description and explanation in linguistics? We take explanation to be the reduction of description to independently motivated general principles. For language, a promising source of independently motivated general principles can be found in neuroscience, and especially computational neuroscience. As an example, we consider the notion of historical change in word m...