This research aims at implementing “Neuromorphic Systems”, i.e., circuits inspired by the organizing principles of animal neural systems, implemented using standard Complementary Metal-Oxide Silicon (CMOS) LSI technology. These kinds of circuits are usually parallel, and they respond in real time. They operate mainly in the sub-threshold region, where the transistors have physi- cal properties that are useful for emulating neurons and neural systems, such as thresholding and exponentiation. Based on current knowledge of biological systems, this work aims at developing neural circuits and systems that emu- late basic functions of the sensory system. The sensory system is the part of the nervous system responsible for processing sensory information, it consists of sensory receptors, neural pathways, and other parts of the brain involved in sensory perception. Sense perception depends on sensory receptors that res- pond to various stimuli. When a stimulus triggers an impulse in a receptor, the stimulus is transformed into pulses or action potentials. The action potential travels through a pathway to the cerebral cortex, where they are processed and interpreted. To this end, this research starts with the implementation of some functions of the early-sensory processing like, detection and transformation of input stimuli, role synaptic connections in sensory information processing. This is done by implementing a number of models such as, a) a temperature sensor, (somatosensory system), inspired by the operation of neurons in sea slugs and snails, in order to mimic sensory receptors whose function is to transform phy- sical stimuli into a train of nerve impulses, b) this neuron model was extended for implementing a network for weak signal detection that exhibit tolerance to noises, to explore the ability of sensory systems to exploit noises inherit in their own elements (neurons) as well as noises from the environment (i.e. the input stimuli), and c) the circuit implementation of a depressing synapse model, whose dynamic effects possibly have a functional role in encoding information brought by sensory stimuli. In auditory pathway, depressing synapses may provide an effective way of detecting emergent synchrony in afferent activities. Then, the attention is shifted to the cognitive processing area with the introduction of two models. a) a neural network for sensory segmentation. To analyze and un- derstand natural scenes, i.e., images, sounds, etc. it is necessary to decompose the scene into coherent “segments”, where each segment corresponds to a dif- ferent component of the scene. This ability is known as sensory segmentation. The model consists of mutually coupled neural oscillators that exhibit synchro- nous (or asynchronous) activity. The basic idea is to strengthen (or weaken) the synaptic weights between synchronous (or asynchronous) neurons, which may result in phase-domain segmentation. Finally, this work concludes with b) the implementation of a neural model for the storage of temporal sequences. In or- der to study the brain ability to learn and recall information as the environment changes over time (i.e. information we perceive is time varying) which is of fun- damental importance in various sensory functions. The model consists of neural oscillators coupled to a common output cell. The basic idea is to learn input sequences, by superposition of rectangular periodic activity (oscillators) with different frequencies. To mimic the operation of these neurons and networks of neurons, we employed biological nonlinear oscillators. The mathematical model of these oscillators consist of two nonlinear differential equations whose main term is a sigmoid function. The stability of the model depends on the magnitude of its variables. In other words, the model can be excitatory or oscillatory de- pending on the value of its variables. The models were implemented with basic circuits such as differential pairs (which emulate a sigmoid-like operation) and current mirrors. The operations of the systems were investigated through theo- retical analysis, numerical simulations and circuit simulations. The implication of device fabrication mismatches and environmental noise were also studied.
学術論文
Tovar G.M., Asai T., Hirose T., and Amemiya Y., "Critical temperature sensor based on oscillatory neuron models," Journal of Signal Processing, vol. 12, no. 1, pp. 17-24 (2008).
Tovar G.M., Asai T., Fujita D., and Amemiya Y., "Analog MOS circuits implementing a temporal coding neural model," Journal of Signal Processing, vol. 12, no. 6, pp. 423-432 (2008).
Fukuda E.S., Tovar G.M., Asai T., Hirose T., and Amemiya Y., "Neuromorphic CMOS circuits implementing a novel neural segmentation model based on symmetric STDP learning," Journal of Signal Processing, vol. 11, no. 6, pp. 439-444 (2007).
Tovar G.M., Hirose T., Asai T., and Amemiya Y., "Neuromorphic MOS circuits exhibiting precisely-timed synchronization with silicon spiking neurons and depressing synapses," Journal of Signal Processing, vol. 10, no. 6, pp. 391-397 (2006).
書籍／チャプター
Tovar G.M., "Analog circuits implementing a critical temperature sensor based on excitable neuron models," Advances in Analog Circuits, Tlelo-Cuautle E., Ed., chapter 15, pp. 327-346, InTech, (2011).
Tovar G.M., Asai T., and Amemiya Y., "Noise-tolerant analog circuits for sensory segmentation based on symmetric STDP learning," Advances in Neuro-Information Processing, Koppen M., Kasabov N., and Coghill G, Eds., Lecture Notes in Computer Science, vol. 5507, pp. 851-858, Springer, Berlin / Heidelberg (2009).
Tovar G.M., Fukuda E.S., Asai T., Hirose T., and Amemiya Y., "Analog CMOS circuits implementing neural segmentation model based on symmetric STDP learning," Neural Information Processing, Ishikawa M., Doya K., Miyamoto H., and Yamakawa T., Eds., Lecture Notes in Computer Science, vol. 4985, pp. 117-126, Springer, Berlin / Heidelberg (2008).
Tovar G.M., Asai T., and Amemiya Y., "Array-enhanced stochastic resonance in a network of noisy neuromorhic circuits," Proceedings of the 17th International Conference on Neural Information Processing, pp. 188-196, Sydney, Australia (Nov. 22-25, 2010).
Tovar G.M., Fukuda E.S., Asai T., Hirose T., and Amemiya Y., "Analog CMOS circuits implementing neural segmentation model based on symmetric STDP learning," Proceedings of the 14th International Conference on Neural Information Processing, pp. 306-315, Kitakyushu, Japan (Nov. 13-16, 2007).
Tovar G.M., Fukuda E.S., Asai T., Hirose T., and Amemiya Y., "Neuromorphic CMOS circuits implementing a novel neural segmentation model based on symmetric STDP learning," Proceedings of the 2007 International Joint Conference on Neural Networks, pp. 897-901, Florida, U.S.A. (Aug. 12-17, 2007).
