Simple neural networks for the amplification and utilization of small changes in neuron firing rates.
I describe physiologically plausible "voter-coincidence" neural networks such that secondary "coincidence" neurons fire on the simultaneous receipt of sufficiently large sets of input pulses from primary sets of neurons. The networks operate such that the firing rate of the secondary, output neurons increases (or decreases) sharply when the mean firing rate of primary neurons increases (or decreases) to a much smaller degree. In certain sensory systems, signals that are generally smaller than the noise levels of individual primary detectors, are manifest in very small increases in the firing rates of sets of afferent neurons. For such systems, this kind of network can act to generate relatively large changes in the firing rate of secondary "coincidence" neurons. These differential amplification systems can be cascaded to generate sharp, "yes-no" spike signals that can direct behavioral responses.
Adair
R K
RK
Department of Physics, Yale University, New Haven, CT 06520-8121, USA. adair@hepmail.physics.yale.edu
D000818:Animals; D008959:Models, Neurological; D009010:Monte Carlo Method; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D016012:Poisson Distribution; D011336:Probability; D012754:Sharks; D012860:Skates, Fish