Computational Neuroscience

Computational Neuroscience

If I cannot build it, I do not understand it - Richard Feynman, Nobel prize in physics.

The present essay describes computational neuroscience as it applies to psychology. Indeed, the biology of the brain and the nervous system in general, or neurobiology, is a fascinating field. So is the related and very similar discipline of neuroscience. Evidently, with technological advances, computers began to take place in the elucidation of these highly complex matters. Therefore, the aim of the present section is a brief overview of the derivative sub-discipline of computational neuroscience. Computational neuroscientists build artificial models that attempt to mimic brain functions. Sounds like artificial intelligence? Maybe, but, arguably, science is still only working towards that goal. Although, of course, it all depends on the definition one accords to intelligence. After all, today’s machines can read, write, speak, count and perform many other complicated tasks necessitating processes akin to cognition and other mental feats in human.

The biology of the brain and the nervous system in general, or neurobiology, is a fascinating field. So is the related and very similar discipline of neuroscience. Evidently, with technological advances, computers began to take place in the elucidation of these highly complex matters. Therefore, the aim of the present section is a brief overview of the derivative sub-discipline of computational neuroscience.

Interestingly, according to Granger (2001), the neuroscientific branch is about more than computers per se or their working models, but about constructing simulacra of cortical activity. Amazingly, the author suggests that in time these theories would even permit the generation of prosthetic brains! Robots – Artificially intelligent machines, with the unveiling of the first androids (human like robotic entities, unlike the above depiction) in Japan and South Korea, no longer the realm of science fiction alone.urther, the ideology relies on a set of principles such as that neural systems rely on stochastic differential equations (Carillo et al., 2011). Rolls (2011) places the historical beginning of the collection of theoretical constructs around the 1970s, with mathematician David Marr as one of the first contributors. Thus, the present paper briefly highlights some key factors descriptive of computational neuroscience.

Robots – Artificially intelligent machines, with the unveiling of the first androids (human like robotic entities, unlike the above depiction) in Japan and South Korea, no longer the realm of science fiction alone. Image: Copyright © Elena.

Computational neuroscientists build artificial models that attempt to mimic brain functions. Sounds like artificial intelligence? Maybe, but, arguably, science is still only working towards that goal. Although, of course, it all depends on the definition one accords to intelligence. After all, today’s machines can read, write, speak, count and perform many other complicated tasks necessitating processes akin to cognition and other mental feats in human.

Further, the ideology relies on a set of principles such as that neural systems rely on stochastic differential equations (Carillo et al., 2011). Rolls (2011) places the historical beginning of the collection of theoretical constructs around the 1970s, with mathematician David Marr as one of the first contributors.

The marvels of contemporary technology. Image: Copyright © Elena.

References:

• Carillo, J. A., Gonzalez, M. D. M., Gualdani, M. P. & Schonbek, M. E. (2011). Classical solutions for a nonlinear Fokker-Planck equation arising in computational neuroscience. ArXiv, September 6, 1-22.
• Granger, R. (2011). How brains are built: Principles of computational neuroscience. Cerebrum, January: 1-17.
• Rolls, E. T. (2011). David Marr’s vision: Floreat computational neuroscience. Brain: A Journal of Neurology, 134: 913-16.

Copyright © 2011 Megan Jorgensen (Elena). All rights reserved.