Digital Implementation of a Biological Astrocyte Model and Its Application

This paper presents a modified astrocyte model that allows a convenient digital implementation. This model is aimed at reproducing relevant biological astrocyte behaviors, which provide appropriate feedback control in regulating neuronal activities in the central nervous system. Accordingly, we investigate the feasibility of a digital implementation for a single astrocyte and a biological neuronal network model constructed by connecting two limit-cycle Hopf oscillators to an implementation of the proposed astrocyte model using oscillator-astrocyte interactions with weak coupling. Hardware synthesis, physical implementation on field-programmable gate array, and theoretical analysis confirm that the proposed astrocyte model, with considerably low hardware overhead, can mimic biological astrocyte model behaviors, resulting in desynchronization of the two coupled limit-cycle oscillators.

[1]  Tobi Delbrück,et al.  CAVIAR: A 45k Neuron, 5M Synapse, 12G Connects/s AER Hardware Sensory–Processing– Learning–Actuating System for High-Speed Visual Object Recognition and Tracking , 2009, IEEE Transactions on Neural Networks.

[2]  Luca Geretti,et al.  The Correspondence Between Deterministic and Stochastic Digital Neurons: Analysis and Methodology , 2008, IEEE Transactions on Neural Networks.

[3]  Gert Cauwenberghs,et al.  Dynamically Reconfigurable Silicon Array of Spiking Neurons With Conductance-Based Synapses , 2007, IEEE Transactions on Neural Networks.

[4]  Tetsuya Hishiki,et al.  A Novel Rotate-and-Fire Digital Spiking Neuron and its Neuron-Like Bifurcations and Responses , 2011, IEEE Transactions on Neural Networks.

[5]  Antonio M. Batista,et al.  Delayed feedback control of bursting synchronization in a scale-free neuronal network , 2010, Neural Networks.

[6]  Peter Jung,et al.  Astrocytes Optimize the Synaptic Transmission of Information , 2008, PLoS Comput. Biol..

[7]  Wenwu Yu,et al.  Identifying the Topology of a Coupled FitzHugh–Nagumo Neurobiological Network via a Pinning Mechanism , 2009, IEEE Transactions on Neural Networks.

[8]  P. Haydon Glia: listening and talking to the synapse , 2001, Nature Reviews Neuroscience.

[9]  S. Marra,et al.  Tanh-like Activation Function Implementation for High-performance Digital Neural Systems , 2006, 2006 Ph.D. Research in Microelectronics and Electronics.

[10]  Arash Ahmadi,et al.  Biologically Inspired Spiking Neurons: Piecewise Linear Models and Digital Implementation , 2012, IEEE Transactions on Circuits and Systems I: Regular Papers.

[11]  Bernabé Linares-Barranco,et al.  A Neuromorphic Cortical-Layer Microchip for Spike-Based Event Processing Vision Systems , 2006, IEEE Transactions on Circuits and Systems I: Regular Papers.

[12]  P. Olver Nonlinear Systems , 2013 .

[13]  D. V. Reddy,et al.  Dynamics of a limit cycle oscillator under time delayed linear and nonlinear feedbacks , 1999, chao-dyn/9908005.

[14]  R. Fields,et al.  New insights into neuron-glia communication. , 2002, Science.

[15]  D E Postnov,et al.  Dynamical patterns of calcium signaling in a functional model of neuron–astrocyte networks , 2009, Journal of biological physics.

[16]  Dmitry E. Postnov,et al.  Functional modeling of neural-glial interaction , 2007, Biosyst..

[17]  Suhita Nadkarni,et al.  Dressed neurons: modeling neural–glial interactions , 2004, Physical biology.

[18]  Sho Hashimoto,et al.  A Novel Hybrid Spiking Neuron: Bifurcations, Responses, and On-Chip Learning , 2010, IEEE Transactions on Circuits and Systems I: Regular Papers.

[19]  Giacomo Indiveri,et al.  A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity , 2006, IEEE Transactions on Neural Networks.

[20]  Steve Furber,et al.  High-performance computing for systems of spiking neurons , 2006 .

[21]  S. Goldman,et al.  New roles for astrocytes: Redefining the functional architecture of the brain , 2003, Trends in Neurosciences.

[22]  Mohammad Javad Yazdanpanah,et al.  Astrocyte-inspired controller design for desynchronization of two coupled limit-cycle oscillators , 2011, 2011 Third World Congress on Nature and Biologically Inspired Computing.

[23]  S. Nadkarni,et al.  Modeling synaptic transmission of the tripartite synapse , 2007, Physical biology.

[24]  Lawrence F. Gray,et al.  A Mathematician Looks at Wolfram''s New Kind of Science , 2003 .

[25]  John Rinzel,et al.  Synchrony measures for biological neural networks , 1995, Biological Cybernetics.

[26]  Jean Lorenceau,et al.  Neuron–glia interactions: from physiology to behavior , 2002, Journal of Physiology-Paris.

[27]  M. Sofroniew,et al.  Astrocytes: biology and pathology , 2009, Acta Neuropathologica.

[28]  Anthony G. Pipe,et al.  Implementing Spiking Neural Networks for Real-Time Signal-Processing and Control Applications: A Model-Validated FPGA Approach , 2007, IEEE Transactions on Neural Networks.