Amplification using recurrent connectivity
This post has much the same content as this NeuroWiki page; you may wish to read and comment on it there.
I’ve only skimmed this interesting article, so beware that I may not correctly understand it.
This article proposes that recurrent excitation in cortex leads to amplification, and analyzes this using the mathematics of basic amplifiers (taught in introductory electrical engineering courses; i.e. open-loop gain and closed loop gain).
They construct a simulation based on this principal that agrees with some electrophysiological and pharmacological results from neurobiology experiments in layer IV of V1.
At the end, they conjecture that a sensory network could use this principal for noise reduction and possibly pattern recognition.
Douglas, Rodney J.; Koch, Christof; Mahowald, Misha; Martin, Kevan A. C.; Suarez, Humbert H. Recurrent Excitation in Neocortical Circuits. Science, Volume 269, Issue 5226, pp. 981-985.
September 9th, 2005 at 2:20 am
If you are interested in this type of thing, I highly recommend looking into Sebastian Seung’s neural network course lecture notes & videos (http://seunglab.mit.edu/courses/9.641/lectures/index.html). The first half of the course takes a very EE-inspired approach to analyzing neural network dynamics in terms of concepts such as amplification, attenuation, and integration. A short summary of these results can be found in: http://hebb.mit.edu/people/seung/papers/hbtnn.pdf
The theory of “permitted and forbidden sets” leads to more sophisticated analysis of recurrent networks. For example, one can use the eigenvalues of the synaptic weight matrix to show that certain sets of neurons in a network are “forbidden” from ever being activated together while others are “permitted” to be active together (at stable points of the network activity). This makes it simple to analytically relate synaptic structure to computational function (with relevant examples from associative memory and constraint satisfacation studied in the course notes). Also, these networks (provably) operate within a dynamical regime known as “hybrid computation” that exhibits both digital and analog qualities conjectured to be important properties of biological networks.