Neural Computation and the Computational Theory of Cognition

The paper by that title, co-authored with neuroscientist Sonya Bahar, is now available on “early view” in the journal Cognitive Science. I think that means it’s available to the authors but not to the public yet. If anyone wants a copy,  please let me know.

This is perhaps the most significant paper I’ve published.


We begin by distinguishing computationalism from a number of other theses that are sometimes
conflated with it. We also distinguish between several important kinds of computation: computation
in a generic sense, digital computation, and analog computation. Then, we defend a weak version of
computationalism—neural processes are computations in the generic sense. After that, we reject on
empirical grounds the common assimilation of neural computation to either analog or digital computation,
concluding that neural computation is sui generis. Analog computation requires continuous
signals; digital computation requires strings of digits. But current neuroscientific evidence indicates
that typical neural signals, such as spike trains, are graded like continuous signals but are constituted
by discrete functional elements (spikes); thus, typical neural signals are neither continuous signals
nor strings of digits. It follows that neural computation is sui generis. Finally, we highlight three
important consequences of a proper understanding of neural computation for the theory of cognition.
First, understanding neural computation requires a specially designed mathematical theory (or theories)
rather than the mathematical theories of analog or digital computation. Second, several popular
views about neural computation turn out to be incorrect. Third, computational theories of cognition
that rely on non-neural notions of computation ought to be replaced or reinterpreted in terms of neural


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