Reliable Computation in Noisy Backgrounds Using Real-Time Neuromorphic Hardware

Spike-time based coding of neural information, in contrast to rate coding, requires that neurons reliably and precisely fire spikes in response to repeated identical inputs, despite a high degree of noise from stochastic synaptic firing and extraneous background inputs. We investigated the degree of reliability and precision achievable in various noisy background conditions using real-time neuromorphic VLSI hardware which models integrate-and-fire spiking neurons and dynamic synapses. To do so, we varied two properties of the inputs to a single neuron, synaptic weight and synchrony magnitude (number of synchronously firing pre-synaptic neurons). Thanks to the realtime response properties of the VLSI system we could carry out extensive exploration of the parameter space, and measure the neurons firing rate and reliability in real-time. Reliability of output spiking was primarily influenced by the amount of synchronicity of synaptic input, rather than the synaptic weight of those synapses. These results highlight possible regimes in which real-time neuromorphic systems might be better able to reliably compute with spikes despite noisy input.

[1]  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.

[2]  Jorge V. José,et al.  Inhibitory synchrony as a mechanism for attentional gain modulation , 2004, Journal of Physiology-Paris.

[3]  B. Connors,et al.  Efficacy of Thalamocortical and Intracortical Synaptic Connections Quanta, Innervation, and Reliability , 1999, Neuron.

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

[5]  R. Desimone,et al.  Modulation of Oscillatory Neuronal Synchronization by Selective Visual Attention , 2001, Science.

[6]  R. Reid,et al.  Low Response Variability in Simultaneously Recorded Retinal, Thalamic, and Cortical Neurons , 2000, Neuron.

[7]  Michael J. Berry,et al.  Weak pairwise correlations imply strongly correlated network states in a neural population , 2005, Nature.

[8]  J. C. Anderson,et al.  Polyneuronal innervation of spiny stellate neurons in cat visual cortex , 1994, The Journal of comparative neurology.

[9]  B. Sakmann,et al.  Cortex Is Driven by Weak but Synchronously Active Thalamocortical Synapses , 2006, Science.

[10]  Moshe Abeles,et al.  On Embedding Synfire Chains in a Balanced Network , 2003, Neural Computation.

[11]  Paul H. E. Tiesinga,et al.  A New Correlation-Based Measure of Spike Timing Reliability , 2002, Neurocomputing.

[12]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[13]  A. Destexhe,et al.  Impact of network activity on the integrative properties of neocortical pyramidal neurons in vivo. , 1999, Journal of neurophysiology.

[14]  R. Reid,et al.  Temporal Coding of Visual Information in the Thalamus , 2000, The Journal of Neuroscience.

[15]  R. Reid,et al.  Paired-spike interactions and synaptic efficacy of retinal inputs to the thalamus , 1998, Nature.

[16]  R. Reid,et al.  Precisely correlated firing in cells of the lateral geniculate nucleus , 1996, Nature.

[17]  A. Peters,et al.  Numerical relationships between geniculocortical afferents and pyramidal cell modules in cat primary visual cortex. , 1993, Cerebral cortex.

[18]  Misha Anne Mahowald,et al.  VLSI analogs of neuronal visual processing: a synthesis of form and function , 1992 .

[19]  C. Koch,et al.  Encoding of visual information by LGN bursts. , 1999, Journal of neurophysiology.

[20]  T. Sejnowski,et al.  Reliability of spike timing in neocortical neurons. , 1995, Science.

[21]  R. Reid,et al.  Precise Firing Events Are Conserved across Neurons , 2002, The Journal of Neuroscience.

[22]  D. Ferster,et al.  Orientation selectivity of thalamic input to simple cells of cat visual cortex , 1996, Nature.