A Neuromorphic aVLSI network chip with configurable plastic synapses

We describe and demonstrate the key features of a neuromorphic, analog VLSI chip (termed F-LANN) hosting 128 integrate-and-fire (IF) neurons with spike-frequency adaptation, and 16 384 plastic bistable synapses implementing a self-regulated form of Hebbian, spike-driven, stochastic plasticity. We were successfully able to test and verify the basic operation of the chip as well as its main new feature, namely the synaptic configurability. This configurability enables us to configure each individual synapse as either excitatory or inhibitory and to receive either recurrent input from an on-chip neuron or AER (address event representation)-based input from an off-chip neuron. It's also possible to set the initial state of each synapse as potentiated or depressed, and the state of each synapse can be read and stored on a computer. The main aim of this chip is to be able to efficiently perform associative learning experiments on a large number of synapses. In the future we would like to connect up multiple F-LANN chips together to be able to perform associative learning of natural stimulus sets.

[1]  Tobi Delbrück,et al.  A Multichip Pulse-Based Neuromorphic Infrastructure and Its Application to a Model of Orientation Selectivity , 2007, IEEE Transactions on Circuits and Systems I: Regular Papers.

[2]  M. V. Rossum,et al.  In Neural Computation , 2022 .

[3]  Davide Badoni,et al.  Spike-Driven Synaptic Plasticity: Theory, Simulation, VLSI Implementation , 2000, Neural Computation.

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

[5]  Andreas G. Andreou,et al.  Analog integrated circuits and signal processing , 1996 .

[6]  Vittorio Dante,et al.  A VLSI recurrent network of integrate-and-fire neurons connected by plastic synapses with long-term memory , 2003, IEEE Trans. Neural Networks.

[7]  Giacomo Indiveri,et al.  A Current-Mode Hysteretic Winner-take-all Network, with Excitatory and Inhibitory Coupling , 2001 .

[8]  Maurizio Mattia,et al.  Collective Behavior of Networks with Linear (VLSI) Integrate-and-Fire Neurons , 1999, Neural Computation.

[9]  Michael I. Jordan,et al.  Advances in Neural Information Processing Systems 30 , 1995 .

[10]  Stefano Fusi,et al.  Hebbian spike-driven synaptic plasticity for learning patterns of mean firing rates , 2002, Biological Cybernetics.

[11]  Bertram E. Shi,et al.  IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS — I : REGULAR PAPERS , VOL . ? ? , NO . ? ? , ? ? ? ? , 2007 .

[12]  P. D. Giudice,et al.  Modelling the formation of working memory with networks of integrate-and-fire neurons connected by plastic synapses , 2003, Journal of Physiology-Paris.

[13]  Daniel J. Amit,et al.  Spike-Driven Synaptic Dynamics Generating Working Memory States , 2003, Neural Computation.

[14]  H. Kalmus.  Biological Cybernetics , 1972, Nature.

[15]  Daniel J. Amit,et al.  Learning in Neural Networks with Material Synapses , 1994, Neural Computation.

[16]  Walter Senn,et al.  Learning Only When Necessary: Better Memories of Correlated Patterns in Networks with Bounded Synapses , 2005, Neural Computation.

[17]  Carver Mead,et al.  Analog VLSI and neural systems , 1989 .

[18]  Peter Tino,et al.  IEEE Transactions on Neural Networks , 2009 .

[19]  Hans-Hermann Bock,et al.  Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data , 2000 .