Wayne Carl Westerman

Multi-Touch: A New Tactile 2-D Gesture Interface for Human-Computer Interaction

The naturalness and variety of a touch-based hand gesture interface offers new opportunities for human-computer interaction. Using a new type of capacitive sensor array, a Multi-Touch Surface (MTS) can be created that is not limited in size, that can be presented in many configurations, that is robust under a variety of environmental operating conditions, and that is very thin. Typing and gesture recognition built into the Multi-Touch Surface allow users to type and perform bilateral gestures on the same surface area and in a smaller footprint than is required by current keyboard and mouse technologies. The present approach interprets asynchronous touches on the surface as conventional single-finger typing, while motions initiated by chords are interpreted as pointing, clicking, gesture commands, or hand resting. This approach requires learning only a few new chords for graphical manipulation, rather than a vocabulary of new chords for typing the whole alphabet. Graphical manipulation seems a better use of chords in todays computing environment.

An analog memory circuit for spiking silicon neurons

A simple circuit is described that functions as an analog memory whose state and dynamics are directly controlled by pulsatile inputs. The circuit has been incorporated into a silicon neuron with a spatially extensive dendritic tree as a means of controlling the spike firing threshold of an integrate-and-fire soma. Spiking activity generated by the neuron itself and by other units in a network can thereby regulate the neurons excitability over time periods ranging from milliseconds to many minutes. Experimental results are presented showing applications to temporal edge sharpening, bistable behavior, and a network that learns in the manner of classical conditioning.

Neuromorphic synapses for artificial dendrites

We describe neuromorphic, variable-weight synapses onartificial dendrites that facilitate experimentation with correlativeadaptation rules. Attention is focused on those aspects of biologicalsynaptic function that could affect a neuromorphic networkscomputational power and adaptive capability. These include sublinearsummation, quantal synaptic noise, and independent adaptationof adjacent synapses. The diffusive nature of artificial dendritesadds considerable flexibility to the design of fast synapsesby allowing conductances to be enabled for short or for variablelengths of time. We present two complementary synapse designs:the shared conductance array and the self-timed synapse. Bothsynapse circuits behave as conductances to mimic biological synapsesand thus enable sublinear summation. The former achieves weightvariation by selecting different conductances from an on-chiparray, and the latter by modulating the length of time a constantconductance remains activated. Both work with our interchip communicationsystem, virtual wires. For the present purpose of comparing variousadaptation mechanisms in software, synaptic weights are storedoff chip. This simplifies the addition of quantal weight noiseand allows connections from different sources to the same dendriticcompartment to have independent weights.

Antidromic Spikes Drive Hebbian Learning in an Artificial Dendritic Tree

Hebbian learning using the timing between pre-synaptic and post-synaptic spiking allows a network of silicon neuromorphs to learn and playback complex spatiotemporal input patterns. Learning occurred dynamically and in a stimulus dependent manner by potentiating active synapses that contributed to post-synaptic spike production and depressing active synapses that were anti-causal. Active synapses that were neither causal nor anti-causal remained at their pre-activated efficacy. The network used to evaluate hebbian synaptic plasticity was fully connected with each neuromorph making a prescribed number of connections to the dendrites of all the other neuromorphs. To enable learning of spatiotemporal spiking activity, efferents from each neuromorph had to make connections along the entire length of their target dendrites so as to produce a temporally distributed response. Upon repetitive presentation of an input pattern those synapses that had appropriate causal timing were strengthened while those that were anti-causal were depressed.