Biomedical subjects

Alan F Murray

Publications and source records attributed to Alan F Murray.

4 recordsLinked to original sources

PubMed2006

Continuous-valued probabilistic behavior in a VLSI generative model.

This paper presents the VLSI implementation of the continuous restricted Boltzmann machine (CRBM), a probabilistic generative model that is able to model continuous-valued data with a simple and hardware-amenable training algorithm. The full CRBM system consists of stochastic neurons whose continuous-valued probabilistic behavior is mediated by injected noise. Integrating on-chip training circuits, the full CRBM system provides a platform for exploring computation with continuous-valued probabilistic behavior in VLSI. The VLSI CRBM's ability both to model and to regenerate continuous-valued data distributions is examined and limitations on its performance are highlighted and discussed.

Algorithms↗

PubMed2006

An adaptive visual neuronal model implementing competitive, temporally asymmetric Hebbian learning.

A novel depth-from-motion vision model based on leaky integrate-and-fire (I&F) neurons incorporates the implications of recent neurophysiological findings into an algorithm for object discovery and depth analysis. Pulse-coupled I&F neurons capture the edges in an optical flow field and the associated time of travel of those edges is encoded as the neuron parameters, mainly the time constant of the membrane potential and synaptic weight. Correlations between spikes and their timing thus code depth in the visual field. Neurons have multiple output synapses connecting to neighbouring neurons with an initial Gaussian weight distribution. A temporally asymmetric learning rule is used to adapt the synaptic weights online, during which competitive behaviour emerges between the different input synapses of a neuron. It is shown that the competition mechanism can further improve the model performance. After training, the weights of synapses sourced from a neuron do not display a Gaussian distribution, having adapted to encode features of the scenes to which they have been exposed.

Algorithms↗

PubMed2004

Implementation of multichannel sensors for remote biomedical measurements in a microsystems format.

A novel microelectronic "pill" has been developed for in situ studies of the gastro-intestinal tract, combining microsensors and integrated circuits with system-level integration technology. The measurement parameters include real-time remote recording of temperature, pH, conductivity, and dissolved oxygen. The unit comprises an outer biocompatible capsule encasing four microsensors, a control chip, a discrete component radio transmitter, and two silver oxide cells (the latter providing an operating time of 40 h at the rated power consumption of 12.1 mW). The sensors were fabricated on two separate silicon chips located at the front end of the capsule. The robust nature of the pill makes it adaptable for use in a variety of environments related to biomedical and industrial applications.

Biomedical Engineering↗

PubMed2004

Synchrony detection and amplification by silicon neurons with STDP synapses.

Spike-timing dependent synaptic plasticity (STDP) is a form of plasticity driven by precise spike-timing differences between presynaptic and postsynaptic spikes. Thus, the learning rules underlying STDP are suitable for learning neuronal temporal phenomena such as spike-timing synchrony. It is well known that weight-independent STDP creates unstable learning processes resulting in balanced bimodal weight distributions. In this paper, we present a neuromorphic analog very large scale integration (VLSI) circuit that contains a feedforward network of silicon neurons with STDP synapses. The learning rule implemented can be tuned to have a moderate level of weight dependence. This helps stabilise the learning process and still generates binary weight distributions. From on-chip learning experiments we show that the chip can detect and amplify hierarchical spike-timing synchrony structures embedded in noisy spike trains. The weight distributions of the network emerging from learning are bimodal.

Action Potentials↗