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What must a global theory of cortex explain?
1School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA.
Current Opinion in Neurobiology
|April 9, 2014
Summary
Computational neuroscience lacks a unifying theory for cognitive functions. Applying quantitative computational complexity principles from computer science is crucial for developing successful theories of cortical computation.
Area of Science:
- Neuroscience
- Computer Science
- Cognitive Science
Background:
- A universally accepted theory explaining cognitive phenomena from cortical computations is currently absent.
- There is no consensus on how to effectively refocus research efforts for a more fruitful pursuit of such a theory.
Purpose of the Study:
- To propose that computational neuroscience must incorporate quantitative concerns, specifically computational complexity, to advance.
- To argue that the lack of such quantitative considerations hinders the development of theories of cortical computation.
Main Methods:
- Observational analysis of research trends in computer science.
- Comparative analysis of computational challenges in computer science and their potential relevance to neuroscience.
Main Results:
- Computer science research demonstrates that significant computational phenomena necessitate overcoming inherent quantitative impediments like computational complexity.
- The absence of similar quantitative considerations in computational neuroscience suggests a potential reason for the lack of progress.
Conclusions:
- Computational neuroscience needs to adopt quantitative principles, akin to those in computer science, to develop plausible theories of cortical computation.
- The brain is unlikely to be an exception to the rule that complex computations require addressing quantitative challenges.
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