卒業生とその進路

Neuromorphic systems performing early-sensory and cognitive processing with CMOS devices


トバー ジェシカ マリア

2010 年度 卒 /博士(情報科学)
文部科学省国費留学生

博士論文の概要

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

  1. 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).
  2. 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).
  3. 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).
  4. 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

  1. 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).
  2. 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).
  3. 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).

招待講演/セミナー

  1. Tovar G.M., "Brain-inspired electrical circuits: life and research in Japan," Special Lecture in School of Electronics Engineering, Jose Antonio Paez University, Valencia, Venezuela (Sep. 21, 2009).

国際会議

  1. 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).
  2. Tovar G.M., Asai T., and Amemiya Y., "Coupling-enhanced stochastic resonance in noisy neuromorphic devices," Proceedings of the 14th International Conference on Cognitive and Neural Systems, p. 87, Boston, U.S.A. (May 19-22, 2010).
  3. Tovar G.M., Asai T., and Amemiya Y., "Noise-tolerant analog circuits for sensory segmentation based on symmetric STDP learning," Proceedings of the 15th International Conference on Neural Information Processing of the Asia-Pacific Neural Network Assembly, pp. 199-200, Auckland, New Zealand (Nov. 25-28, 2008).
  4. Tovar G.M., Fujita D., Asai T., Hirose T., and Amemiya Y., "Neuromorphic MOS circuits implementing a temporal coding neural model," Proceedings of the 2008 RISP International Workshop on Nonlinear Circuits and Signal Processing, pp. 371-374, Gold Coast, Australia (Mar. 6-8, 2008).
  5. 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).
  6. 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).
  7. Tovar G.M., Asai T., Hirose T., and Amemiya Y., "Critical temperature sensor based on spiking neuron models: experimental results with discrete MOS circuits," Proceedings of the 2007 RISP International Workshop on Nonlinear Circuits and Signal Processing, pp. 599-602, Shanghai, China (Mar. 3-6, 2007).
  8. Tovar G.M., Hirose T., Asai T., and Amemiya Y., "Critical temperature sensor based on spiking neuron models," Proceedings of the 2006 International Symposium on Nonlinear Theory and its Applications (WIP session), pp. 84-88, Bologna, Italy (Sep. 11-14, 2006).
  9. Tovar G.M., Hirose T., Asai T., and Amemiya Y., "Precisely-timed synchronization among spiking neural circuits on analog VLSIs," Proceedings of the 2006 RISP International Workshop on Nonlinear Circuits and Signal Processing, pp. 62-65, Honolulu, U.S.A. (Mar. 3-5, 2006).

受賞

  1. Tovar G.M., "Critical temperature sensor based on spiking neuron models: experimental results with discrete MOS circuits," The Research Institute of Signal Processing - NSCP'07 Student Paper Award, Mar. 2007.

国内学会

  1. Tovar G.M., 浅井 哲也, 雨宮 好仁, "Neuromorphic CMOS analog circuit exhibiting array-enhanced stochastic resonance behavior with population heterogeneity," Neuro 2010, (神戸), 2010年9月.
  2. 藤田 大地, Tovar G.M., 浅井 哲也, 雨宮 好仁, "時系列信号の学習を行うニューラルハードウェアの記憶容量評価," 日本神経回路学会 第18回全国大会, P1-26, (茨城), 2008年9月.
  3. 藤田 大地, Tovar G.M., 浅井 哲也, 雨宮 好仁, "時系列コーディングを行う生体様CMOSアナログ回路," VDECデザイナーフォーラム2008, P-04, (東京), 2008年6月.
  4. 藤田 大地, Tovar G.M., 浅井 哲也, 廣瀬 哲也, 雨宮 好仁, "時系列コーディングを行う神経モデルのアナログCMOS回路化," 電子情報通信学会総合大会, (北九州), 2008年3月.
  5. Tovar G.M., 浅井 哲也, 廣瀬 哲也, 雨宮 好仁, "Neuromorphic LSI circuits for critical temperature detection," VDECデザイナーフォーラム2007(若手の会), (札幌), 2007年9月.
  6. 浅井 哲也, 廣瀬 哲也, Tovar G.M., 雨宮 好仁, "興奮系を用いた臨界温度センサ集積回路," 日本物理学会第62回年次大会, (千葉), 2006年9月.