A VLSI recurrent network of integrate-and-fire neurons connected by plastic synapses with long-term memory

Chicca E, Badoni D, Dante V, D'Andreagiovanni M, Salina G, Carota L, Fusi S, Del Giudice P (2003)
IEEE Transactions on Neural Networks 14(5): 1297-1307.

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Electronic neuromorphic devices with on-chip, on-line learning should be able to modify quickly the synaptic couplings' to acquire information about new patterns to be stored (synaptic plasticity) and, at the same time, preserve this information on very long time scales (synaptic stability). Here, we illustrate the electronic implementation of a simple solution to this stability-plasticity problem, recently proposed and studied in various contexts. It is based on the observation that reducing the analog depth of the synapses to the extreme (bistable synapses) does not necessarily disrupt the performance of the device as an associative memory, provided that 1) the number of neurons is large enough; 2) the transitions between stable synaptic states are stochastic; and 3) learning is slow. The drastic reduction of the analog depth of the synaptic variable also makes this solution appealing from the point of view of electronic implementation and offers a simple methodological alternative to the technological solution based on floating gates. We describe the full custom analog very large-scale integration (VLSI) realization of a small network of integrate-and-fire neurons connected by bistable deterministic plastic synapses which can implement the idea of stochastic learning. In the absence of stimuli, the memory is preserved indefinitely. During the stimulation the synapse undergoes quick temporary changes through the activities of the pre- and postsynaptic neurons; those changes stochastically result in a long-term modification of the synaptic efficacy. The intentionally disordered pattern of connectivity allows the system to generate a randomness suited to drive the stochastic selection mechanism. We check by a suitable stimulation protocol that the stochastic synaptic plasticity produces the expected pattern of potentiation and depression in, the electronic network. The proposed implementation requires only 69 x 83 mum(2) for the neuron and 68 x 47 mum(2) for the synapse (using a 0.6 mum, three metals, CMOS technology) and, hence, it is particularly suitable for the integration, of a large number of plastic synapses on a single chip.
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Chicca E, Badoni D, Dante V, et al. A VLSI recurrent network of integrate-and-fire neurons connected by plastic synapses with long-term memory. IEEE Transactions on Neural Networks. 2003;14(5):1297-1307.
Chicca, E., Badoni, D., Dante, V., D'Andreagiovanni, M., Salina, G., Carota, L., Fusi, S., et al. (2003). A VLSI recurrent network of integrate-and-fire neurons connected by plastic synapses with long-term memory. IEEE Transactions on Neural Networks, 14(5), 1297-1307.
Chicca, E., Badoni, D., Dante, V., D'Andreagiovanni, M., Salina, G., Carota, L., Fusi, S., and Del Giudice, P. (2003). A VLSI recurrent network of integrate-and-fire neurons connected by plastic synapses with long-term memory. IEEE Transactions on Neural Networks 14, 1297-1307.
Chicca, E., et al., 2003. A VLSI recurrent network of integrate-and-fire neurons connected by plastic synapses with long-term memory. IEEE Transactions on Neural Networks, 14(5), p 1297-1307.
E. Chicca, et al., “A VLSI recurrent network of integrate-and-fire neurons connected by plastic synapses with long-term memory”, IEEE Transactions on Neural Networks, vol. 14, 2003, pp. 1297-1307.
Chicca, E., Badoni, D., Dante, V., D'Andreagiovanni, M., Salina, G., Carota, L., Fusi, S., Del Giudice, P.: A VLSI recurrent network of integrate-and-fire neurons connected by plastic synapses with long-term memory. IEEE Transactions on Neural Networks. 14, 1297-1307 (2003).
Chicca, Elisabetta, Badoni, D., Dante, V., D'Andreagiovanni, M., Salina, G., Carota, L., Fusi, S., and Del Giudice, P. “A VLSI recurrent network of integrate-and-fire neurons connected by plastic synapses with long-term memory”. IEEE Transactions on Neural Networks 14.5 (2003): 1297-1307.
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PMID: 26217173
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PMID: 26041985
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Cantley KD, Subramaniam A, Stiegler HJ, Chapman RA, Vogel EM., IEEE Trans Neural Netw Learn Syst 23(4), 2012
PMID: 24805040
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Hsieh HY, Tang KT., IEEE Trans Neural Netw Learn Syst 23(7), 2012
PMID: 24807133
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PMID: 24807998
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Neuromorphic silicon neuron circuits.
Indiveri G, Linares-Barranco B, Hamilton TJ, van Schaik A, Etienne-Cummings R, Delbruck T, Liu SC, Dudek P, Hafliger P, Renaud S, Schemmel J, Cauwenberghs G, Arthur J, Hynna K, Folowosele F, Saighi S, Serrano-Gotarredona T, Wijekoon J, Wang Y, Boahen K., Front Neurosci 5(), 2011
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Neural dynamics in reconfigurable silicon.
Basu A, Ramakrishnan S, Petre C, Koziol S, Brink S, Hasler PE., IEEE Trans Biomed Circuits Syst 4(5), 2010
PMID: 23853376
Memory capacities for synaptic and structural plasticity.
Knoblauch A, Palm G, Sommer FT., Neural Comput 22(2), 2010
PMID: 19925281
Real-Time Classification of Complex Patterns Using Spike-Based Learning in Neuromorphic VLSI.
Mitra S, Fusi S, Indiveri G., IEEE Trans Biomed Circuits Syst 3(1), 2009
PMID: 23853161
Transistor analogs of emergent iono-neuronal dynamics.
Rachmuth G, Poon CS., HFSP J 2(3), 2008
PMID: 19404469
Compact silicon neuron circuit with spiking and bursting behaviour.
Wijekoon JH, Dudek P., Neural Netw 21(2-3), 2008
PMID: 18262751
Synaptic dynamics in analog VLSI.
Bartolozzi C, Indiveri G., Neural Comput 19(10), 2007
PMID: 17716003
A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity.
Indiveri G, Chicca E, Douglas R., IEEE Trans Neural Netw 17(1), 2006
PMID: 16526488
A MOSFET-based model of a Class 2 nerve membrane.
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PMID: 15941002

18 References

Data provided by Europe PubMed Central.

Spike-driven synaptic plasticity: theory, simulation, VLSI implementation.
Fusi S, Annunziato M, Badoni D, Salamon A, Amit DJ., Neural Comput 12(10), 2000
PMID: 11032032

AUTHOR UNKNOWN, 0
Stochastic synaptic plasticity in deterministic aVLSI networks of spiking neurons
Chicca, Proc. World Congr. Neuroinformatics (), 2001
Floating-gate mos synapse transistors
Diorio, Neuromorphic Systems Engineering: Neural Networks in Silicon (), 0

AUTHOR UNKNOWN, 0

Amit, Modeling Brain Function (), 1989
Dynamic learning in neural networks with material synapses
Amit, Neural Comput. 6(), 1994

Mead, Analog VLSI and Neural Systems (), 1989
Emergent asynchronous, irregular firing in a deterministic analog vlsi recurrent network
D'Andreagiovanni, Proc. World Congr. Neuroinformatics (), 2001

Horowitz, The Art of Electronics (), 1989

AUTHOR UNKNOWN, 0
Robotic vision. Neuromorphic vision sensors.
Indiveri G, Douglas R., Science 288(5469), 2000
PMID: 10841740

Chicca, A VLSI neuromorphic device with 128 neurons and 3000 synapses: Area optimization and project (), 1999

AUTHOR UNKNOWN, 0
Communicating neuronal ensembles between neuromorphic chips
Boahen, Neuromorphic Systems Engineering (), 1998
The PCI-AER interface board
Dante, Proc. Workshop Neuromorphic Engineering (), 2001

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