Computational Neuroscience - An Introduction

Wednesday, May 30, 2012

computational neuroscience

 Computational Neuroscience
Computational neuroscience reflects the possibilitv of generating theories of brain function in terms of the information-processing properties of structures that make up nervous systems. 
Computational neuroscience is the study of brain function in terms of the information processing properties of the structures that make up the nervous system. It is an interdisciplinary science that links the diverse fields of neuroscience, cognitive science and psychology with electrical engineering, computer science, mathematics and physics.

A key goal of many researchers in the field is to study what is going on  in the brain—the processing of neurons, axons and the whole connectome as well as the different areas of the brain— in order to learn from biology and apply knowledge directly to the creation of artificial intelligence systems.

Jeff Hawkins, for example has taken his learning from biological neuroscience to apply directly to the creation of software at Numenta.  According to Hawkins, in order to actually make an intelligent computer, we need to teach it to find and use patterns, not to attempt any specific tasks. Through this method, he thinks we can build intelligent machines, helping us do all sorts of useful tasks that current computers cannot achieve. He further argues that this memory-prediction system as implemented by the brain's cortex is the basis of human intelligence. 

areas of the brain

Much of this theory is based on the interpretation that the neural structures of brain, especially in the neocortex are a tabula rasa, or blank state at the time of birth.  The brains common building blocks can be transformed into the architecture of the connectome over a persons formative years and to some extent throughout a lifetime.  

Computational neuroscience is distinct from psychological connectionism and theories of learning from disciplines such as machine learning, neural networks and computational learning theory in that it emphasizes descriptions of functional and biologically realistic neurons (and neural systems) and their physiology and dynamics.

These models capture the essential features of the biological system at multiple space and time scales, from membrane currents, protein and chemical coupling to network oscillations, columnar and topographic architecture and learning and memory. These computational models are used to frame hypotheses that can be directly tested by current or future biological and/or psychological experiments.

The term "computational neuroscience" was introduced by Eric L. Schwartz, who organized a conference, held in 1985 in Carmel, California at the request of the Systems Development Foundation, to provide a summary of the current status of a field which until that point was referred to by a variety of names, such as neural modeling, brain theory and neural networks. The proceedings of this definitional meeting were later published as the book Computational Neuroscience.

In the excellent overview video below, MIT PHD researcher (and ultra-serious Star Wars geek) Evan Ehrenberg gives an overview of computational neuroscience from the biology to AI theory. It is worth noting that Ehrenberg graduated UC Berkeley in 2010 with a bachelor's of science in cognitive science with an emphasis in computational modeling at the age of 16.

Since the age of 14, Ehrenberg has wanted to create artificial intelligence, or Strong AI. Like Demis Hassabis, this prodigy is using his own extraordinary intelligence to forward the field of AI. Ehrenberg is also a member of the Sinha Lab for vision research. studying computational neuroscience for face perception.


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