When my mother's mother was in her early twenties, a century ago, a suitor took her duck hunting in a rowboat on a lake near Austin, Texas, where she grew up. He steadied his shotgun by resting the barrel on her right shoulder--she was sitting in the bow--and when he fired he not only missed the duck but also permanently damaged her hearing, especially on that side. The loss became more severe as she got older, and by the time I was in college she was having serious trouble with telephones. Her deafness probably contributed to one of her many eccentricities: ending phone conversations by suddenly hanging up. I'm a grandparent myself now, and lots of people I know have hearing problems. A guy I played golf with last year came close to making a hole in one, then complained that no one in our foursome had complimented him on his shot--even though, a moment before, all three of us had complimented him on his shot. The man who cuts my wife's hair began wearing two hearing aids recently, to compensate for damage that he attributes to years of exposure to professional-quality blow-dryers. My sister has hearing aids, too. She traces her problem to repeatedly listening at maximum volume to Anne's Angry and Bitter Breakup Song Playlist, which she created while going through a divorce. My ears ring all the time--a condition called tinnitus.

The neural basis of speech recognition and, more generally, sound processing is not well understood. A simple subset of the task of speech recognition, learning to categorise vowel sounds, provides some insights into the more general problems. A simulated neural system that performs this task is described. The system is based on relatively accurate fatiguing leaky integrate and fire neurons, and learns to categorise three categories of vowel sounds. The input to the system is in the form of neural stimulation that relatively accurately reflects the response of biological neurons in the ear to auditory input. The system correctly categorises 91.71% of the vowel sounds using a five-fold test. The system is a sound model of the neuropsychological task of phoneme categorisation, all be it a far from perfect model. As such, it provides an entry into a better understanding of the neuro-psychological mechanisms behind sound processing.

The most prominent feature of this network is the pair of large Mauthner cells whose axons cross the midline and descend down the spinal cord to synapse on primary motoneurons. The Mauthner system also includes inhibitory neurons, the PHP cells, which have a unique and intense field effect inhibition at the spikeinitiating zone of the Mauthner cells (Faber and Korn, 1978). The Mauthner system is part of the full brainstem escape network which also includes two pairs of cells homologous to the Mauthner cell and other populations of reticulospinal neurons. With this network fish initiate escapes only from appropriate stimuli, turn away from the offending stimulus, and do so very rapidly with a latency around 15 msec in goldfish. The Mauthner cells play an important role in these functions.

The most prominent feature of this network is the pair of large Mauthner cells whose axons cross the midline and descend down the spinal cord to synapse on primary motoneurons. The Mauthner system also includes inhibitory neurons, the PHP cells, which have a unique and intense field effect inhibition at the spikeinitiating zone of the Mauthner cells (Faber and Korn, 1978). The Mauthner system is part of the full brainstem escape network which also includes two pairs of cells homologous to the Mauthner cell and other populations of reticulospinal neurons. With this network fish initiate escapes only from appropriate stimuli, turn away from the offending stimulus, and do so very rapidly with a latency around 15 msec in goldfish. The Mauthner cells play an important role in these functions.

Eaton E. P. O. Biology University of Colorado Boulder, Co. 80309 Abstract We provide a computational description of the function of the Mauthner system.This is the brainstem circuit which initiates faststart escapes in teleost fish in response to sounds. Our simulations, usingbackpropagation in a realistically constrained feedforward network, have generated hypotheses which are directly interpretable interms of the activity of the auditory nerve fibers, the principle cells of the system and their associated inhibitory neurons. 1 INTRODUCTION 1.1 THE M.AUTHNER SYSTEM Much is known about the brainstem system that controls fast-start escapes in teleost fish. The most prominent feature of this network is the pair of large Mauthner cells whose axons cross the midline and descend down the spinal cord to synapse on primary motoneurons. The Mauthner system also includes inhibitory neurons, the PHP cells, which have a unique and intense field effect inhibition at the spikeinitiating zoneof the Mauthner cells (Faber and Korn, 1978). The Mauthner system is part of the full brainstem escape network which also includes two pairs of cells homologous to the Mauthner cell and other populations of reticulospinal neurons. With this network fish initiate escapes only from appropriate stimuli, turn away from the offending stimulus, and do so very rapidly with a latency around 15 msec in goldfish.

This paper describes an ongoing effort which approaches neural net research in a program of close collaboration of neurosc i ent i sts and eng i neers. The effort is des i gned to elucidate associative learning in the marine snail Hermissenda crassicornist in which Pavlovian conditioning has been observed. Learning has been isolated in the four neuron network at the convergence of the v i sua 1 and vestibular pathways in this animal t and biophysical changes t specific to learning t have been observed in the membrane of the photoreceptor B cell. A basic charging capacitance model of a neuron is used and enhanced with biologically plausible mechanisms that are necessary to replicate the effect of learning at the cellular level. These mechanisms are nonlinear and are t primarilYt instances of second order control systems (e.g.

This paper describes an ongoing effort which approaches neural net research in a program of close collaboration of neurosc i ent i sts and eng i neers. The effort is des i gned to elucidate associative learning in the marine snail Hermissenda crassicornist in which Pavlovian conditioning has been observed. Learning has been isolated in the four neuron network at the convergence of the v i sua 1 and vestibular pathways in this animal t and biophysical changes t specific to learning t have been observed in the membrane of the photoreceptor B cell. A basic charging capacitance model of a neuron is used and enhanced with biologically plausible mechanisms that are necessary to replicate the effect of learning at the cellular level. These mechanisms are nonlinear and are t primarilYt instances of second order control systems (e.g.

The output of the model of the four-neuron network di sp 1ays changes in the temporal vari at i on of membrane potential similar to those observed in electrophysiological measurements.

DISTRIBUTED NEURAL INFORMATION PROCESSING IN THE VESTIBULO-OCULAR SYSTEM Clifford Lau Office of Naval Research Detach ment Pasadena, CA 91106 Vicente Honrubia* UCLA Division of Head and Neck Surgery Los Angeles, CA 90024 ABSTRACT A new distributed neural information-processing model is proposed to explain the response characteristics of the vestibulo-ocular system and to reflect more accurately the latest anatomical and neurophysiological data on the vestibular afferent fibers and vestibular nuclei. In this model, head motion is sensed topographically by hair cells in the semicircular canals. Hair cell signals are then processed by multiple synapses in the primary afferent neurons which exhibit a continuum of varying dynamics. The model is an application of the concept of "multilayered" neural networks to the description of findings in the bullfrog vestibular nerve, and allows us to formulate mathematically the behavior of an assembly of neurons whose physiological characteristics vary according to their anatomical properties. INTRODUCTION Traditionally the physiological properties of individual vestibular afferent neurons have been modeled as a linear time-invariant system based on Steinhausents description of cupular motion.

DISTRIBUTED NEURAL INFORMATION PROCESSING IN THE VESTIBULO-OCULAR SYSTEM Clifford Lau Office of Naval Research Detach ment Pasadena, CA 91106 Vicente Honrubia* UCLA Division of Head and Neck Surgery Los Angeles, CA 90024 ABSTRACT A new distributed neural information-processing model is proposed to explain the response characteristics of the vestibulo-ocular system and to reflect more accurately the latest anatomical and neurophysiological data on the vestibular afferent fibers and vestibular nuclei. In this model, head motion is sensed topographically by hair cells in the semicircular canals. Hair cell signals are then processed by multiple synapses in the primary afferent neurons which exhibit a continuum of varying dynamics. The model is an application of the concept of "multilayered" neural networks to the description of findings in the bullfrog vestibular nerve, and allows us to formulate mathematically the behavior of an assembly of neurons whose physiological characteristics vary according to their anatomical properties. INTRODUCTION Traditionally the physiological properties of individual vestibular afferent neurons have been modeled as a linear time-invariant system based on Steinhausents description of cupular motion.