[Comp-neuro] How are sigmoid signals determined in spiking neurons? a role for AHP currents and ACh

Stephen Grossberg steve at cns.bu.edu
Thu Jul 28 12:08:18 CEST 2011

How is the shape of feedback and output signals from neurons determined? In 1973 and thereafter, I published a series of articles proving how feedback signals, and other design variations, in recurrent on-center off-surround networks of neurons whose cells obey the membrane, or shunting, equations of neurophysiology control how rate-based networks transform and store activity patterns in short-term memory. An early article that proved the key role of sigmoid signals, and also described how winner-take-all choices can occur, can be downloaded from my web page http://cns.bu.edu/~steve:

Grossberg, S. (1973). Contour enhancement, short-term memory, and constancies in reverberating neural networks. Studies in Applied Mathematics, 52, 213-257. 

It is easy to define a sigmoid signal function in a rate-based network. But how does one do this for spiking neurons?

The following article, which can be downloaded from the web page, proposes a key role for after-hyperpolarization currents and acetylcholine in controlling this transformation: 

Palma, J., Versace, M., and Grossberg, S. 
After-hyperpolarization currents and acetylcholine control sigmoid transfer functions in a spiking cortical model.
Journal of Computational Neuroscience, in press

Recurrent networks are ubiquitous in the brain, where they enable a diverse set of transformations during perception, cognition, emotion, and action. It has been known since the 1970’s how the choice of feedback signal function can control the transformation of input patterns to rate-based recurrent on-center off-surround networks into activity patterns that are stored in short term memory. A sigmoid signal function may, in particular, control a quenching threshold below which inputs are suppressed as noise and above which they may be contrast enhanced before the resulting activity pattern is stored.  The threshold and slope of the sigmoid signal function determine the degree of noise suppression and of contrast enhancement. This article analyses how sigmoid signal functions and their shape may be determined in biophysically realistic spiking neurons.Combinations of fast, medium, and slow after-hyperpolarization (AHP) currents, and their modulation by acetylcholine (ACh), can control sigmoid signal threshold and slope.  Instead of a simple gain in excitability that was previously attributed to ACh, cholinergic modulation may cause translation of the sigmoid threshold.  This property clarifies how activation of ACh by basal forebrain circuits, notably the nucleus basalis of Meynert, may alter the vigilance of category learning circuits, and thus their sensitivity to predictive mismatches, thereby controlling whether learned categories code concrete or abstract information, as predicted by Adaptive Resonance Theory. 
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