Alan Murray

Integrated pulse stream neural networks: results, issues, and pointers

Results from working analog VLSI implementations of two different pulse stream neural network forms are reported. The circuits are rendered relatively invariant to processing variations, and the problem of cascadability of synapses to form large systems is addressed. A strategy for interchip communication of large numbers of neural states has been implemented in silicon and results are presented. The circuits demonstrated confront many of the issues that blight massively parallel analog systems, and offer solutions.

Pulse arithmetic in VLSI neural networks

This review explores the use of biological signaling methods to build silicon networks. Recent designs using a technique called pulse stream, which employs fully analog, dynamic weight storage, are described. The pulse-stream concept is explained, and a comparison is made with conventional analog neural networks. An analog synapse based on the pulse-stream approach is presented. Chip details and simulation results are given.<<ETX>>

Implementation of wireless power transfer and communications for an implantable ocular drug delivery system

A wireless power transfer and communication system based on near-field inductive coupling has been designed and implemented. The feasibility of using such a system to remotely control drug release from an implantable drug delivery system is addressed. The architecture of the wireless system is described and the signal attenuation over distance in both water and phosphate buffered saline is studied. Additionally, the health risk due to exposure to radio frequency (RF) radiation is examined using a biological model. The experimental results demonstrate that the system can trigger the release of drug within 5 s, and that such short exposure to RF radiation does not produce any significant (<or= 1 degrees C) heating in the biological model. The conclusion of the work is that this system could replace a chemical battery in an implantable system, eliminating the risks associated with battery failure and leakage and also allowing more compact designs for applications such as drug delivery.

Adaptation of barn owl localization system with spike timing dependent plasticity

To localize a seen object, the superior colliculus of the barn owl integrates the visual and auditory localization cues which are accessed from the sensory system of the brain. These cues are formed as visual and auditory maps, thus the alignment between visual and auditory maps is very important for accurate localization in prey behavior. Blindness or prism wearing may disturb this alignment. The juvenile barn owl could adapt its auditory map to this mismatch after several weeks training. Here we investigate this process by building a computational model of auditory and visual integration with map adjustment in the deep superior colliculus. The adaptation is based on activity dependent axon developing which is instructed by an inhibitory network. In the inhibitory network, the strength of the inhibition is adjusted by spike timing dependent plasticity(STDP). The simulation results are in line with the biological experiment and support the idea that the STDP is involved in the alignment of sensory maps. The system of the model provides a new mechanism capable of eliminating the disparity in visual and auditory map integration.

The adaptation of visual and auditory integration in the barn owl superior colliculus with Spike Timing Dependent Plasticity

To localize a seen object, the superior colliculus of the barn owl integrates the visual and auditory localization cues which are accessed from the sensory system of the brain. These cues are formed as visual and auditory maps. The alignment between visual and auditory maps is very important for accurate localization in prey behavior. Blindness or prism wearing may interfere this alignment. The juvenile barn owl could adapt its auditory map to this mismatch after several weeks training. Here we investigate this process by building a computational model of auditory and visual integration in deep Superior Colliculus (SC). The adaptation of the map alignment is based on activity dependent axon developing in Inferior Colliculus (IC). This axon growing process is instructed by an inhibitory network in SC while the strength of the inhibition is adjusted by Spike Timing Dependent Plasticity (STDP). The simulation results of this model are in line with the biological experiment and support the idea that STDP is involved in the alignment of sensory maps. This model also provides a new spiking neuron based mechanism capable of eliminating the disparity in visual and auditory map integration.

Adaptive Visual and Auditory Map Alignment in Barn Owl Superior Colliculus and Its Neuromorphic Implementation

Adaptation is one of the most important phenomena in biology. A young barn owl can adapt to imposed environmental changes, such as artificial visual distortion caused by wearing a prism. This adjustment process has been modeled mathematically and the model replicates the sensory map realignment of barn owl superior colliculus (SC) through axonogenesis and synaptogenesis. This allows the biological mechanism to be transferred to an artificial computing system and thereby imbue it with a new form of adaptability to the environment. The model is demonstrated in a real-time robot environment. Results of the experiments are compared with and without prism distortion of vision, and show improved adaptability for the robot. However, the computation speed of the embedded system in the robot is slow. A digital and analog mixed signal very-large-scale integration (VLSI) circuit has been fabricated to implement adaptive sensory pathway changes derived from the SC model at higher speed. VLSI experimental results are consistent with simulation results.

High-Accuracy Mixed-Signal VLSI for Weight Modification in Contrastive Divergence Learning

This paper presents an approach to on-chip, unsupervised learning. A circuit capable of changing a neurons synaptic weight with great accuracy is described and experimental results from its aVLSI implementation in a 0.6µm CMOS process are shown and discussed. We consider its use in the contrastive divergence learning scheme of the Product of Experts (PoE) architecture.

Silicon superior colliculus for the integration of visual and auditory information with adaptive axon connection

Visual and auditory map alignment in the superior colliculus (SC) of the barn owl is important for its accurate localization of prey. The visual map, and hence the alignment, may be purposefully disturbed in a juvenile barn owl by fitting it with ocular prisms, and it is found that it can adapt its auditory map to this mismatch after several weeks training. In our previous SC model, the axon growing process is instructed by an inhibitory network, the strength of which is adjusted using the neural structures involved in spatial localization. Based on this model, a mixed signal integrated circuit of the SC has been designed, and simulation results are consistent with those found by biological experiment. This new model makes possible artificial networks capable of eliminating the disparity between the visual and auditory maps.

Modeling visual and auditory integration of barn owl superior colliculus with STDP

The visual and auditory map alignment in the superior colliculus of barn owl is important for its accurate localization in prey behavior. This alignment may be disturbed by the blindness or prism wearing, the juvenile barn owl could adapt its auditory map to this mismatch after several weeks training. It is believed in literature that auditory map with the plasticity shifts in terms of the visual map change. In this paper, a model is built to explain this mechanism. The activity dependent axonogenesis during the auditory map shift is guided by the visual instructive spikes whereas the visual instructive spikes are modulated by an inhibitory network based on spike timing dependent plasticity(STDP). The simulation results are consistent with the biological experiment and would open a way towards artificial networks capable of eliminating the disparity in visual and auditory map integration.

Sensor-driven neuromorphic walking leg control

We present a simple neuromorphic central pattern generators (CPG) circuit module, which is essentially a pair of coupled oscillators, to actuate a joint on a leg. This novel, reconfigurable CPG module is able to generate different motor patterns of different frequencies or duty cycles, simply by changing a few of circuit parameters. Three CPG modules, corresponding to three joints, can make an arthropod leg of three degrees of freedom (DOFs). With appropriate circuit parameter settings, and thus suitable phase lags among joints, the leg is expected to walk on a complex terrain with adaptive steps. The adaptation is associated with the circuit parameters mediated by external commands or sensory signals. Simulation results for the circuitry, designed using a 0.35µm process, are reported.