Conventional computers’ limitations are both at the architectural level through the Von Neumann bottleneck and at the computational level through the deterministic nature of Boolean logic. By the emulation of one’s most preponderant mechanisms for brain’s long-term memory, its implementation in vicinity of sensing arrays, neural probes or biochips shall greatly optimize computational costs and recognition required to classify high-dimensional patterns from complex environments.īrain-inspired computing has reached important milestones in the last decades, positioning it as a realistic alternative to conventional computers’ in the big data era 1.
Circuit-element analysis and impedance spectroscopy confirms their morphological control in temporal windows where growth kinetics is finely perturbed by the input frequency and duty cycle. Growths time-lapse image processing shows spatial features to be dynamically dependent, and expand distinctively before and after conductive bridging with two electro-generated dendrites. Systematic study of applied voltage-pulse parameters details on tuning independently morphological aspects of micrometric dendrites’: fractal number, branching degree, asymmetry, density or length. Inexistent in electronics, we emulate dendritic morphogenesis by electropolymerization in water, aiming in operando material modification for hardware learning. Although materials and processes are different from biological cells’, brain mimicries led to tremendous achievements in parallel information processing via neuromorphic engineering. Weisstein, E.W.: Second Fundamental Theorem of Calculus (2018). Sah, M.P., et al.: A generic model of memristors with parasitic components. Riaza, R., Tischendorf, C.: Semistate models of electrical circuits including memristors. Penfield, P.P.: Varactor Applications, p. In: Proceedings of the 2014 IEEE ISCAS, pp. Muthuswamy, B., et al.: Memristor modelling. Muthuswamy, B., Chua, L.O.: Simplest chaotic circuit. Marszalek, W.: On the action parameter and one-period loops of oscillatory memristive circuits. Lin, D., Hui, S.Y.R., Chua, L.O.: Gas discharge lamps are volatile memristors. Kennedy, M.P., Chua, L.O.: Hysteresis in electronic circuits: a circuit theorist’s perspective. Jeltsema, D., Scherpen, J.M.A.: Multidomain modeling of nonlinear networks and systems. Princeton University Press, Princeton (2003) Havil, J.: Gamma : Exploring Euler’s Constant.
Cambridge University Press, Cambridge (2013) Hamill, P.: A Student’s Guide to Lagrangians and Hamiltonians. Georgiou, P.S., et al.: On Memristor ideality and reciprocity.
Tata McGraw Hill, New York (1969)Įlwakil, A.S., Kennedy, M.P.: Chaotic oscillator configuration using a frequency dependent negative resistor. McGraw-Hill, New York (1987)Ĭorinto, F., Forti, M.: Memristor circuits: flux-charge analysis method. 31(2), 231–235 (1984)Ĭhua, L.O., Tseng, C.: A Memristive circuit model for pn junction diodes. IEEE 64(2), 209–223 (1976)Ĭhua, L.O., Szeto, E.W.: Synthesis of higher order nonlinear circuit elements. Accessed Ĭhua, L.O., Kang, S.M.: Memristive devices and systems. University of California, Berkeley (Fall 2008), pp. 27(11), 1059–1087 (1980)Ĭhua, L.O.: Nonlinear circuit foundations for nanodevices, part I: the four-element torus (invited paper). Circuit Theory 18(5), 507–519 (1971)Ĭhua, L.O.: Device modeling via basic nonlinear circuit elements. McGraw-Hill, New York (1969)Ĭhua, L.O.: Memristor - the missing circuit element. Princeton University Press/The Hebrew University of Jerusalem, Princeton/Jerusalem (2011)Ĭhua, L.O.: Introduction to Nonlinear Network Theory. In: Proceedings of the 2012 IEEE International Symposium on Circuits and Systems, pp. Ambelang, S., Muthuswamy, B.: From Van der Pol to Chua: an introduction to nonlinear dynamics and chaos for second year undergraduates.