Programmable neuromorphic circuits for spike-based neural dynamics

Hardware implementations of spiking neural networks offer promising solutions for a wide set of tasks, ranging from autonomous robotics to brain machine interfaces. We propose a set of programmable hybrid analog/digital neuromorphic circuits than can be used to build compact low-power neural processing systems. In particular, we present both CMOS and hybrid memristor/CMOS synaptic circuits that have programmable synaptic weights and exhibit biologically plausible response properties. For the CMOS circuits, we present experimental results demonstrating that they operate correctly over a wide range input frequencies; for the hybrid memristor/CMOS circuits we present circuit simulation results validating their expected response properties.

[1]  Wulfram Gerstner,et al.  SPIKING NEURON MODELS Single Neurons , Populations , Plasticity , 2002 .

[2]  Terrence J Sejnowski,et al.  Communication in Neuronal Networks , 2003, Science.

[3]  G. Indiveri,et al.  An ultra low power current-mode filter for neuromorphic systems and biomedical signal processing , 2006, 2006 IEEE Biomedical Circuits and Systems Conference.

[4]  Giacomo Indiveri,et al.  A VLSI network of spiking neurons with an asynchronous static random access memory , 2011, 2011 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[5]  Christofer Toumazou,et al.  Two centuries of memristors. , 2012, Nature materials.

[6]  G. Bi,et al.  Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type , 1998, The Journal of Neuroscience.

[7]  Walter Senn,et al.  Learning Real-World Stimuli in a Neural Network with Spike-Driven Synaptic Dynamics , 2007, Neural Computation.

[8]  Ammar Belatreche,et al.  Advances in Design and Application of Spiking Neural Networks , 2006, Soft Comput..

[9]  N. Brunel,et al.  Calcium-based plasticity model explains sensitivity of synaptic changes to spike pattern, rate, and dendritic location , 2012, Proceedings of the National Academy of Sciences.

[10]  Tobi Delbrück,et al.  32-bit Configurable bias current generator with sub-off-current capability , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

[11]  C. Toumazou,et al.  A Versatile Memristor Model With Nonlinear Dopant Kinetics , 2011, IEEE Transactions on Electron Devices.

[12]  Carver A. Mead,et al.  Neuromorphic electronic systems , 1990, Proc. IEEE.

[13]  D. Stewart,et al.  The missing memristor found , 2008, Nature.

[14]  L. Abbott,et al.  Homeostasis and Learning through Spike-Timing Dependent Plasticity , 2003 .

[15]  Leon O. Chua Resistance switching memories are memristors , 2011 .

[16]  Mostafa Rahimi Azghadi,et al.  Efficient design of triplet based Spike-Timing Dependent Plasticity , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[17]  Giacomo Indiveri,et al.  Synthesis of log-domain integrators for silicon synapses with global parametric control , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

[18]  Giacomo Indiveri,et al.  Frontiers in Neuromorphic Engineering , 2011, Front. Neurosci..

[19]  Xiang Yang,et al.  Dynamic-Load-Enabled Ultra-low Power Multiple-State RRAM Devices , 2012, Scientific Reports.

[20]  Bernabé Linares-Barranco,et al.  On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex , 2011, Front. Neurosci..

[21]  Mostafa Rahimi Azghadi,et al.  Design and implementation of BCM rule based on spike-timing dependent plasticity , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[22]  R. Kempter,et al.  Hebbian learning and spiking neurons , 1999 .

[23]  Giacomo Indiveri,et al.  Real-Time Classification of Complex Patterns Using Spike-Based Learning in Neuromorphic VLSI , 2009, IEEE Transactions on Biomedical Circuits and Systems.

[24]  Gert Cauwenberghs,et al.  Neuromorphic Silicon Neuron Circuits , 2011, Front. Neurosci.

[25]  Phill Rowcliffe,et al.  Training Spiking Neuronal Networks With Applications in Engineering Tasks , 2008, IEEE Transactions on Neural Networks.

[26]  Chiara Bartolozzi,et al.  Synaptic Dynamics in Analog VLSI , 2007, Neural Computation.

[27]  Wei Yang Lu,et al.  Nanoscale memristor device as synapse in neuromorphic systems. , 2010, Nano letters.