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Updated: May 17, 2026
11:18
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
Neural computation and the computational theory of cognition
Gualtiero Piccinini1, Sonya Bahar
1Center for Neurodynamics, Department of Philosophy, University of Missouri - St. Louis St. Louis, MO 63121, USA. piccininig@umsl.edu.
Cognitive Science
|November 7, 2012
Summary
Neural processes are a unique form of computation, distinct from digital or analog models. Understanding this sui generis neural computation is crucial for cognitive theories.
Area of Science:
- Cognitive Science
- Neuroscience
- Philosophy of Mind
Background:
- Computationalism is often conflated with other theses.
- Distinctions between generic, digital, and analog computation are essential.
- Neural computation is frequently, but incorrectly, assimilated to digital or analog computation.
Purpose of the Study:
- To defend a weak version of computationalism: neural processes are computations in a generic sense.
- To empirically reject the assimilation of neural computation to analog or digital computation.
- To highlight consequences for cognitive theory based on the sui generis nature of neural computation.
Main Methods:
- Conceptual analysis to distinguish computationalism and types of computation.
- Empirical review of neuroscientific evidence regarding neural signals (e.g., spike trains).
- Argumentation based on the properties of neural signals versus requirements for analog and digital computation.
Main Results:
- Neural processes are defended as computations in a generic sense.
- Neural computation is empirically shown to be neither analog nor digital, thus sui generis.
- Typical neural signals exhibit characteristics of both continuous and discrete systems.
Conclusions:
- A new mathematical framework is needed for neural computation, distinct from analog or digital computation theories.
- Popular, existing views on neural computation require revision.
- Cognitive theories relying on non-neural computation concepts need reinterpretation or replacement with neural computation frameworks.
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