A series of psychoacoustic experiments was conducted in subjects implanted with a permanent intracochlear bipolar electrode. These experiments were designed to reveal the nature of the sensation evoked by direct sinusoidal electrical stimulation of the acoustic nerve. A series of single unit experiments in the inferior colliculus of cats was then conducted, using intracochlear stimulus electrodes identical to those implanted in human subjects in all respects except size, and using identical stimuli. These physiological experiments were designed to reveal how sounds evoked by intracochlear electrical stimulation in humans are generated and encoded in the auditory nervous system. Among the results were the following: 1) The sensation arises from direct electrical stimulation of the acoustic nerve. It is not “electrophonic” hearing arising from electro-mechanical excitation of hair cells. 2) While sounds are heard with electrical stimulation at frequencies from below 25 to above 10,000 Hz, the useful range of discriminative hearing is limited to frequencies below 400–600 Hz. 3) There is no “place” coding of electrical stimuli of different frequency. Tonal sensations generated by electrical stimulation must be encoded by the time order of discharge of auditory neurons. 4) The periods of sinusoidal electrical stimuli are encoded in discharges of inferior colliculus neurons at frequencies up to 400–600 Hz. 5) Both psychoacoustic and physiological evidence indicates that the low tone sensations evoked by electrical stimulation are akin to the sensations of “periodicity pitch” generated in the normal cochlea. 6) Most cochlear hair cells are lost with intracochlear implantation with this electrode. Most ganglion cells survive implantation. Implications of these experiments for further development of an acoustic prosthesis are discussed.
Bio-inspired vision sensors are particularly appropriate candidates for navigation of vehicles or mobile robots due to their computational simplicity, allowing compact hardware implementations with low power dissipation. The Lobula Giant Movement Detector (LGMD) is a wide-field visual neuron located in the Lobula layer of the Locust nervous system. The LGMD increases its firing rate in response to both the velocity of an approaching object and the proximity of this object. It has been found that it can respond to looming stimuli very quickly and trigger avoidance reactions. It has been successfully applied in visual collision avoidance systems for vehicles and robots. This paper introduces a modified neural model for LGMD that provides additional depth direction information for the movement. The proposed model retains the simplicity of the previous model by adding only a few new cells. It has been simplified and implemented on a Field Programmable Gate Array (FPGA), taking advantage of the inherent parallelism exhibited by the LGMD, and tested on real-time video streams. Experimental results demonstrate the effectiveness as a fast motion detector.
Abstract-The Lobula Giant Movement Detector (LGMD) is a wide-field visual neuron that is located in the Lobula layer of the Locust nervous system. The LGMD increases its firing rate in response to both the velocity of the approaching object and its proximity. It has been found that it can respond to looming stimuli very quickly and can trigger avoidance reactions whenever a rapidly approaching object is detected. It has been successfully applied in visual collision avoidance systems for vehicles and robots. This paper proposes a modified LGMD model that provides additional movement depth direction information. The proposed model retains the simplicity of the previous neural network model, adding only a few new cells. It has been tested on both simulated and recorded video data sets. The experimental results shows that the modified model can very efficiently provide stable information on the depth direction of movement.
The vector components of the winning node Wk with minimum distance Di; is then updated as follows where TJ is the learning rate. The topological ordering property is imposed by also updating weight vectors of nodes in the neighbourhood of the winning node. This can be achieved by the following learning rulewhere N j is a neighbourhood function (defining the region around Wk ) based on the topological displacement of neighbouring neuron from the winning neuron. The size of N j decreases as training progresses.In the vast majority of implementations, the SOM input data and neurons are represented by real numbers, making it difficult to implement on a hardware architecture like the Field Programmable Gate Array (FPGA). However, in many applications the data is either presented as a binary string, or may be conveniently recoded as such (a "binary signature"). For example, in image processing applications a bank of Haar filters produces a long binary signature. In this paper we present a new learning algorithm which takes binary inputs and maintains tri-state weights (neuron) in the SOM. We also present the FPGA implementation of this binary Self Organizing Map (bSOM). The bSOM is designed for efficient hardware implementation, having both greatly reduced circuit size compared to a real-valued SOM, and exceptionally fast execution and training times.In section II, we review previous implementations of SOM on hardware architectures. The novel bSOM algorithm is then presented in III, followed by its FPGA implementation in section IV. Section V, presents the experimental results in software and hardware, and we conclude in section VI.During training, the "nearest" neuron prototype vector to the input vector is identified -this is called the "winning" neuron -using a distance metric, D. The Euclidean distance is most frequently used as the metric.For a given network with M neurons and N-dimensional input vector x, the distance for neuron with weight vector Wj (j < M) is given by
A series of simple psychoacoustic studies were conducted on three deaf patients with indwelling scala tympani electrodes to determine better what they hear as a consequence of electrical stimulation. Physiological experiments on cats implanted with a similar electrode were conducted to determine how the sensation heard by these patients is generated and encoded in the auditory nervous system. Some preliminary results of these animal experiments are described. Additional improvements in the surgical implantation procedure are detailed. Results of these studies suggest the following: 1. long‐term intracochlear implantation is technically feasible without unusual complications; 2. mechanical stability of the implant prosthesis has been improved by fixing the implant to the temporal bone with methyl methacrylate cement; 3. with simple periodic electrical stimuli the implanted patients described tonal sensation for frequencies ranging from about 100 Hz to more than 10 kHz; 4. apparent pitch changes rapidly as a function of stimulus frequency at frequencies below about 500 Hz; 5. subjects are able to identify many common environmental sounds and a few words, but conventional speech discrimination is poor; 6. the sound sensation generated by electrical stimulation arises as a consequence of simultaneous excitation of a broad segment of the acoustic nerve; therefore, no “place” coding of the electrical stimuli occurs in the cochlea; 7. if no cochlear place coding occurs then tonal quality ascribed to sounds heard with electrical stimulation at low stimulus frequencies must be encoded by the temporal ordering of discharges in stimulated neurons.
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