DeWeerth, Stephen P.
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ItemITR/SY: a distributed programming infrastructure for integrating smart sensors(Georgia Institute of Technology, 2009-11-30) Ramachandran, Umakishore ; DeWeerth, Stephen P. ; Mackenzie, Kenneth M. ; Starner, Thad ; Hutto, Phil ; Wolenetz, Matt ; Rehg, James M.
ItemAdvancing neural modeling methods and technology(Georgia Institute of Technology, 2008-05-31) Lee, Robert ; DeWeerth, Stephen P. ; Butera, Robert J.
ItemSensory feedback in a half-center oscillator model(Georgia Institute of Technology, 2007-02) Simoni, Mario R. ; DeWeerth, Stephen P.We 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.
ItemAn asynchronous architecture for modeling intersegmental neural communication(Georgia Institute of Technology, 2006-02) Patel, Girish N. ; Reid, Michael S. ; Schimmel, David E. ; DeWeerth, Stephen P.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.
ItemUsing a hybrid neural system to reveal regulation of neuronal network activity by an intrinsic current(Georgia Institute of Technology, 2004-06) Sorensen, Michael ; DeWeerth, Stephen P. ; Cymbalyuk, Gennady ; Calabrese, Ronald L.The 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.
ItemAn analog VLSI model of muscular contraction(Georgia Institute of Technology, 2003-07) Hudson, Tina A. ; Bragg, Julian A. ; Hasler, Jennifer ; DeWeerth, Stephen P.We have developed analog VLSI circuits to model the behavior demonstrated by biological sarcomeres, the force generating components of muscle tissue. The circuits are based upon the mathematical description of crossbridge populations developed by A. F. Huxley (1957). We have implemented the sarcomere circuit using a standard 1.2 μm process, and have demonstrated the nonlinear transient behaviors exhibited by biological muscle.
ItemModeling alternation to synchrony with inhibitory coupling: A neuromorphic VLSI approach(Georgia Institute of Technology, 2000) Cymbalyuk, Gennady S. ; Patel, Girish N. ; Calabrese, Ronald L. ; DeWeerth, Stephen P. ; Cohen, Avis H.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.