Girish N. Patel

Modeling Alternation to Synchrony with Inhibitory Coupling: A Neuromorphic VLSI Approach

We developed an analog very large-scale integrated system of two mutually inhibitory silicon neurons that display several different stable oscillations. For example, oscillations can be synchronous with weak inhibitory coupling and alternating with relatively strong inhibitory coupling. All oscillations observed experimentally were predicted by bifurcation analysis of a corresponding mathematical model. The synchronous oscillations do not require special synaptic properties and are apparently robust enough to survive the variability and constraints inherent in this physical system. In biological experiments with oscillatory neuronal networks, blockade of inhibitory synaptic coupling can sometimes lead to synchronous oscillations. An example of this phenomenon is the transition from alternating to synchronous bursting in the swimming central pattern generator of lamprey when synaptic inhibition is blocked by strychnine. Our results suggest a simple explanation for the observed oscillatory transitions in the lamprey central pattern generator network: that inhibitory connectivity alone is sufficient to produce the observed transition.

An asynchronous architecture for modeling intersegmental neural communication

This paper presents an asynchronous VLSI architecture for modeling the oscillatory patterns seen in segmented biological systems. The architecture emulates the intersegmental synaptic connectivity observed in these biological systems. The communications network uses address-event representation (AER), a common neuromorphic protocol for data transmission. The asynchronous circuits are synthesized using communicating hardware processes (CHP) procedures. The architecture is scalable, supports multichip communication, and operates independent of the type of silicon neuron (spiking or burst envelopes). A 16-segment prototype system was developed, tested, and implemented; data from this system are presented.

An analog VLSI loser-take-all circuit

An analog circuit that performs a loser-take-all computation is presented. This circuit takes an array of analog input current and produces an array of binary output voltages/currents. The circuit functions such that the output at the node with the minimum input current is a non-zero constant while all of the other outputs are equal to zero. This circuit can also be used to compute the global minimum of the input array. Theory of operation for this circuit is developed and compared with experimental results.

Analog VLSI model of the leech heartbeat elemental oscillator

Although significant complexity has been demonstrated in software-based neural system models, it is very difficult to simulate these models near real time. Such real time operation is critical both in the extraction of the maximal information from the models and in the utilization of these models in the creation of artificial systems. Analog very large-scale integrated (aVLSI) circuits have been shown to be a useful medium for implementing real-time neural system models1. Additionally, aVLSI circuits are compact and dissipate little power, facilitating the engineering of artificial systems based on biological principles. Much research has been performed in the aVLSI modeling of early sensory (e.g., visual and auditory) processing 2,3,4. Little application has been made, however, in the modeling of biological motor systems, even though the technology has significant potential in this area.

Establishing the foundations for engineering education in K-5

We have developed a volunteer program designed to enhance physical science education within elementary schools in the metropolitan Atlanta area (US). Elementary Science for Elementary Students (ES/sup 2/) brings demonstrations into the classroom that reach fundamental concepts in electricity, machines, structures, and sound. Each demonstration has the same basic format, beginning with a brief interactive lecture and visually based demonstration that emphasizes at most two fundamental concepts and ending with a hands on activity that allows students to experiment with these concepts. This format is a particularly effective teaching method, because it combines the three major learning techniques (visual, auditory, tactile) into a single presentation. In this way, each child is able to learn the concepts in their most natural mode of learning, reinforced by the two remaining modes. It is not only the students, but also the teachers that gain a better understanding of science through our demonstrations, improving their confidence in the material which enables them to teach more effectively in the future. In the first phase of ES/sup 2/, volunteers go into the classrooms and do physical science demonstrations themselves. Once we have gained familiarity and trust in the schools, however we seek to maximize our educational impact in elementary school curricula by providing demonstration materials to the individual schools and training teachers to do these demonstrations independently.

Bifurcation of synchronous oscillations into torus in a system of two reciprocally inhibitory silicon neurons: Experimental observation and modeling

Oscillatory activity in the central nervous system is associated with various functions, like motor control, memory formation, binding, and attention. Quasiperiodic oscillations are rarely discussed in the neurophysiological literature yet they may play a role in the nervous system both during normal function and disease. Here we use a physical system and a model to explore scenarios for how quasiperiodic oscillations might arise in neuronal networks. An oscillatory system of two mutually inhibitory neuronal units is a ubiquitous network module found in nervous systems and is called a half-center oscillator. Previously we created a half-center oscillator of two identical oscillatory silicon (analog Very Large Scale Integration) neurons and developed a mathematical model describing its dynamics. In the mathematical model, we have shown that an in-phase limit cycle becomes unstable through a subcritical torus bifurcation. However, the existence of this torus bifurcation in experimental silicon two-neuron system was not rigorously demonstrated or investigated. Here we demonstrate the torus predicted by the model for the silicon implementation of a half-center oscillator using complex time series analysis, including bifurcation diagrams, mapping techniques, correlation functions, amplitude spectra, and correlation dimensions, and we investigate how the properties of the quasiperiodic oscillations depend on the strengths of coupling between the silicon neurons. The potential advantages and disadvantages of quasiperiodic oscillations (torus) for biological neural systems and artificial neural networks are discussed.

A bifurcation of a synchronous oscillations into a torus in a system of two mutually inhibitory aVLSI neurons: experimental observation

Abstract We studied a system of two ‘identical’ oscillatory aVLSI neurons with mutually inhibitory connections. The system demonstrates different oscillatory behaviors depending on the strength of the inhibitory connections: anti-phasic, synchronous, phase-shifted, and quasiperiodic oscillations. We experimentally observed a bifurcation of synchronous oscillations into quasiperiodic oscillations with two independent frequencies. This bifurcation was confirmed by the analysis of the phase between neuronal outputs, the cross-correlation function, the amplitude spectrum, and the correlation dimension. The observation of this bifurcation in a physical system suggests that this scenario might also occur in living half-center oscillators, such as those found in central pattern generators.

Spatiotemporal dynamics of a stochastic VLSI array

In this paper we present an analog VLSI array of hysteretic elements that facilitates the exploitation of known properties of stochastic resonance. We present data from a 7/spl times/9 array of locally coupled Schmitt trigger elements implemented in a 2 /spl mu/m n-well CMOS process. In particular, we demonstrate stochastic resonance in a single element (uncoupled case) with an improvement in output signal to noise ratio of approximately 40 dB. In a spatially extended system (elements coupled via analog transmission gates), we observe an array enhanced effect by measuring the relative firing times between two cells in the array.