Pattern recognition computation using action potential timing for stimulus representation

A computational model is described in which the sizes of variables are represented by the explicit times at which action potentials occur, rather than by the more usual 'firing rate' of neurons. The comparison of patterns over sets of analogue variables is done by a network using different delays for different information paths. This mode of computation explains how one scheme of neuroarchitecture can be used for very different sensory modalities and seemingly different computations. The oscillations and anatomy of the mammalian olfactory systems have a simple interpretation in terms of this representation, and relate to processing in the auditory system. Single-electrode recording would not detect such neural computing. Recognition 'units' in this style respond more like radial basis function units than elementary sigmoid units.

[1]  G. Shepherd.  The Synaptic Organization of the Brain , 1979 .

[2]  Walter Heiligenberg,et al.  Neural Nets in Electric Fish , 1991 .

[3]  L. Haberly.  Neuronal circuitry in olfactory cortex: anatomy and functional implications , 1985 .

[4]  R. Palmer,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[5]  G. Shepherd,et al.  Computer simulation of a dendrodendritic synaptic circuit for self- and lateral-inhibition in the olfactory bulb , 1979, Brain Research.

[6]  N. Suga,et al.  Delay lines and amplitude selectivity are created in subthalamic auditory nuclei: the brachium of the inferior colliculus of the mustached bat. , 1993, Journal of neurophysiology.

[7]  M. Konishi,et al.  A circuit for detection of interaural time differences in the brain stem of the barn owl , 1990, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[8]  G. Neuweiler,et al.  Interaural time and intensity coding in superior olivary complex and inferior colliculus of the echolocating bat Molossus ater. , 1985, Journal of neurophysiology.

[9]  M. Konishi,et al.  Axonal delay lines for time measurement in the owl's brainstem. , 1988, Proceedings of the National Academy of Sciences of the United States of America.

[10]  G. Laurent,et al.  Odorant-induced oscillations in the mushroom bodies of the locust , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[11]  D. Kleinfeld,et al.  Waves and stimulus-modulated dynamics in an oscillating olfactory network. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[12]  M. Serby,et al.  Science of Olfaction , 1992, Springer New York.

[13]  B. Connors,et al.  Intrinsic oscillations of neocortex generated by layer 5 pyramidal neurons. , 1991, Science.

[14]  J J Hopfield,et al.  Neural computation by concentrating information in time. , 1987, Proceedings of the National Academy of Sciences of the United States of America.

[15]  D Kleinfeld,et al.  Odors, oscillations, and waves: does it all compute? , 1994, Science.

[16]  Gilles Sicard,et al.  New studies on odour discrimination in the frog's olfactory receptor cells. I. Experimental results , 1982 .

[17]  W. Singer,et al.  Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex. , 1989, Proceedings of the National Academy of Sciences of the United States of America.

[18]  B J Richmond,et al.  Concurrent processing and complexity of temporally encoded neuronal messages in visual perception. , 1991, Science.

[19]  M. T. Shipley,et al.  Neurophysiology of the Olfactory Bulb , 1992 .

[20]  John J. Hopfield,et al.  Connected-digit speaker-dependent speech recognition using a neural network with time-delayed connections , 1991, IEEE Trans. Signal Process..

[21]  E. Adrian.  Olfactory reactions in the brain of the hedgehog , 1942, The Journal of physiology.

[22]  W. Reichardt.  Autokorrelations-Auswertung als Funktionsprinzip des Zentralnervensystems , 1957 .

[23]  T. Bullock.  Integrative systems research on the brain: resurgence and new opportunities. , 1993, Annual review of neuroscience.

[24]  Moustafa M. Fahmy,et al.  Probabilistic Winner-Take-All Learning Algorithm for Radial-Basis-Function Neural Classifiers , 1994, Neural Computation.

[25]  R. Llinás,et al.  Subthreshold Na+-dependent theta-like rhythmicity in stellate cells of entorhinal cortex layer II , 1989, Nature.

[26]  William Bialek,et al.  Reliability and information transmission in spiking neurons , 1992, Trends in Neurosciences.

[27]  John S. Kauer,et al.  Contributions of topography and parallel processing to odor coding in the vertebrate olfactory pathway , 1991, Trends in Neurosciences.