A Two-Dimensional Visual Tracking Array

The density and concurrency available in VLSI make it an excellent technology for implementing visual image-processing. By incorporating phototransistors and analog processing elements onto a single die, the large signal bandwidths required for real-time computations can be achieved. This paper describes a VLSI chip that computes the "center of intensity" of a two-dimensional visual field. One application for this network is the localization of a bright spot of light against a dark background. Theoretical and experimental results are presented to describe the operation of the system and its suitability as a input device for tracking servo systems

An Analog VLSI Model of Adaptation in the Vestibulo-Ocular Reflex

The vestibulo-ocular reflex (VOR) is the primary mechanism that controls the compensatory eye movements that stabilize retinal images during rapid head motion. The primary pathways of this system are feed-forward, with inputs from the semicircular canals and outputs to the oculomotor system. Since visual feedback is not used directly in the VOR computation, the system must exploit motor learning to perform correctly. Lisberger(1988) has proposed a model for adapting the VOR gain using image-slip information from the retina. We have designed and tested analog very large-scale integrated (VLSI) circuitry that implements a simplified version of Lisberger's adaptive VOR model

Analog VLSI Circuits for Sensorimotor Feedback

This thesis presents a design framework and circuit implementations for integrating sensory and motor processing onto very large-scale integrated (VLSI) chips. The designs consist of analog circuits that are composed of bipolar and subthreshold MOS transistors. The primary emphasis in this work is the transformation from the spatially-encoded representation found in sensory images to a scalar representation that is useful for controlling motor systems. The thesis begins with a discussion of the aggregation of sensory signals and the resulting extraction of high-level features from sensory images. An integrated circuit that computes the centroid of a visual image is presented. A theoretical analysis of the function of this circuit in stimulus localization and a detailed error analysis are also presented. Next, the control of motors using pulse trains is discussed. Pulse-generating circuits for use in bidirectional motor control and the implementation of traditional control schemes are presented. A method for analyzing the operation of these controllers is also discussed. Finally, a framework for the combination of sensory aggregation and pulse-encoded outputs is presented. The need for signal normalization and circuits to perform this task are discussed. Two complete sensorimotor feedback systems are presented

Implementing neural architectures using analog VLSI circuits

Analog very large-scale integrated (VLSI) technology can be used not only to study and simulate biological systems, but also to emulate them in designing artificial sensory systems. A methodology for building these systems in CMOS VLSI technology has been developed using analog micropower circuit elements that can be hierarchically combined. Using this methodology, experimental VLSI chips of visual and motor subsystems have been designed and fabricated. These chips exhibit behavior similar to that of biological systems, and perform computations useful for artificial sensory systems

A Prototype of a Neural, Powered, Transtibial Prosthesis for the Cat: Benchtop Characterization

We developed a prototype of a neural, powered, transtibial prosthesis for the use in a feline model of prosthetic gait. The prosthesis was designed for attachment to a percutaneous porous titanium implant integrated with bone, skin, and residual nerves and muscles. In the benchtop testing, the prosthesis was fixed in a testing rig and subjected to rhythmic vertical displacements and interactions with the ground at a cadence corresponding to cat walking. Several prosthesis functions were evaluated. They included sensing ground contact, control of transitions between the finite states of prosthesis loading, and a closed-loop modulation of the linear actuator gain in each loading cycle. The prosthetic design parameters (prosthesis length = 55 mm, mass = 63 g, peak extension moment = 1 Nm) corresponded closely to those of the cat foot-ankle with distal shank and the peak ankle extension moment during level walking. The linear actuator operated the prosthetic ankle joint using inputs emulating myoelectric activity of residual muscles. The linear actuator gain was modulated in each cycle to minimize the difference between the peak of ground reaction forces (GRF) recorded by a ground force sensor and a target force value. The benchtop test results demonstrated a close agreement between the GRF peaks and patterns produced by the prosthesis and by cats during level walking

Using a hybrid neural system to reveal regulation of neuronal network activity by an intrinsic current

©2004 Society for NeuroscienceThe electronic version of this article is the complete one and can be found online at: http://www.jneurosci.org/content/24/23/5427.abstractDOI: 10.1523/JNEUROSCI.4449-03.2004The generation of rhythmic patterns by neuronal networks is a complex phenomenon, relying on the interaction of numerous intrinsic and synaptic currents, as well as modulatory agents. To investigate the functional contribution of an individual ionic current to rhythmic pattern generation in a network, we constructed a hybrid system composed of a silicon model neuron and a heart interneuron from the heartbeat timing network of the medicinal leech. When the model neuron and a heart interneuron are connected by inhibitory synapses, they produce rhythmic activity similar to that observed in the heartbeat network. We focused our studies on investigating the functional role of the hyperpolarization-activated inward current (I[subscript h] ) on the rhythmic bursts produced by the network. By introducing changes in both the model and the heart interneuron, we showed that I[subscript h] determines both the period of rhythmic bursts and the balance of activity between the two sides of the network, because the amount and the activation/deactivation time constant of I[subscript h] determines the length of time that a neuron spends in the inhibited phase of its burst cycle. Moreover, we demonstrated that the model neuron is an effective replacement for a heart interneuron and that changes made in the model can accurately mimic similar changes made in the living system. Finally, we used a previously developed mathematical model (Hill et al. 2001) of two mutually inhibitory interneurons to corroborate these findings. Our results demonstrated that this hybrid system technique is advantageous for investigating neuronal properties that are inaccessible with traditional techniques

ITR/SY: a distributed programming infrastructure for integrating smart sensors

Issued as final reportNational Science Foundation (U.S.

Sensory feedback in a half-center oscillator model

©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TBME.2006.886868We hypothesize that one role of sensorimotor feedback for rhythmic movements in biological organisms is to synchronize the frequency of movements to the mechanical resonance of the body. Our hypothesis is based on recent studies that have shown the advantage of moving at mechanical resonance and how such synchronization may be possible in biology. We test our hypothesis by developing a physical system that consists of a silicon-neuron central pattern generator (CPG), which controls the motion of a beam, and position sensors that provide feedback information to the CPG. The silicon neurons that we use are integrated circuits that generate neural signals based on the Hodgkin- Huxley dynamics. We use this physical system to develop a model of the interaction between the sensory feedback and the complex dynamics of the neurons to create the closed-loop system behavior. This model is then used to describe the conditions under which our hypothesis is valid and the general effects of sensorimotor feedback on the rhythmic movements of this system