Codes, functions, and causes: A critique of Brette's conceptual analysis of coding

Abstract

In a recent article [1], Brette argues that coding as a concept is inappropriate for explanations of neurocognitive phenomena. Here, we argue that Brette's conceptual analysis mischaracterizes the structure of causal claims in coding and other forms of analysis-by-decomposition. We argue that analyses of this form are permissible, conceptually coherent, and offer essential tools for building and developing models of neurocognitive systems like the brain.

Is coding a relevant metaphor for building AI?

“…Each of these components contributes to the models success, and yet none of them fundamentally depend on considerations from neural coding. This is not to say that we cannot usefully apply representational analyses to such agents post-hoc, regardless of whether the representations satisfy Brettes criteria for neural codes (Barack et al, 2019). Indeed, since the earliest days of connectionism researchers have been interested in the neural codes that emerge when a clearly-specified learning algorithm is applied to a well-understood model trained to execute a particular task.…”