Post on 15-Jul-2020
Emergent auditory feature tuning in a real-timeneuromorphic VLSI system
Elisabetta Chicca
Cognitive Interaction Technology Center of ExcellenceUniversity of Bielefeld
STINT Workshop 2012
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Neuromorphic Engineering
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Neural computation→ neurotechnologies
The term neuromorphic was coined by Carver Mead, in the late 1980s to describeVery-Large-Scale Integration (VLSI) systems containing sub-threshold analog circuitsthat mimic neuro-biological architectures present in the nervous system.
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Neuromorphic circuits
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Building blocksThe biological neuron
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Integrate-and-Fire neuron
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Neuro
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0 1 2Time (s)
0 1 20
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Neuro
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Time (s)
G. Indiveri, et al. “A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity”, IEEE Transactionson Neural Networks, 2006
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Integrate-and-Fire neuron
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Ne
uro
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Time (s)0.5 0.6 0.7 0.8
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−Vgs
(V)
<f>
(H
z)
Vrfr
G. Indiveri, et al. “A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity”, IEEE Transactionson Neural Networks, 2006
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Synaptic dynamicsImpulse response and short-term depression
C. Bartolozzi and G. Indiveri, “Synaptic dynamics in analog VLSI”, Neural Computation, 2007.
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Synaptic dynamicsLearning
VL
θ
VH
wV
mem
0 0.1 0.2 0.3 0.4
pre
Time(s)
VL
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VH
wV
mem
0 0.1 0.2 0.3 0.4
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Time(s)
Mitra et al., “Real-time classification of complex patterns using spike-based learning in neuromorphic VLSI”, IEEE Transaction on BiomedicalCircuits and Systems, 2009.Giulioni et al.,“Classification of correlated patterns with a configurable analog VLSI neural network of spiking neurons and self-regulating plasticsynapses.”, Neural Computation, 2009.
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Neuromorphic systems
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Address Event Representation (AER)
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2
1
Inputs
Encode Decode
Address Event Bus
Source
Chip
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1
Outputs
3 2 1 2 1 32
Destination
Chip
Action Potential
Address-Event
representation of
action potential
R. S. Deiss, R. J. Douglas, and A. M. Whatley “A Pulse–Coded Communications Infrastructure for NeuromorphicSystems” in Pulsed Neural Networs, 1998.
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Multi-chip setup
PC workstation(A)
AER mapper
AER eventsare transmittedon a serial bus
CHIP-1 (B1)
SATA USB
CHIP-3 (C)
CHIP-2 (B2)
Sheik et al. “Emergent auditory feature tuning in a real-time neuromorphic VLSI system” Frontiers in Neuromorphic Engineering, 2012.
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Emergent auditory feature tuning
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Emergent auditory feature tuningFull neural network diagram
Martin Coath et al. “Emergent Feature Sensitivity in a Model of the Auditory Thalamocortical System” Advances in ExperimentalMedicine and Biology, 2011.
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Emergent auditory feature tuningHardware neural network diagram
A
B2B1
= Excitatory = Excitatory STDP = Inhibitory
C
A
B2B1
C
A
B2B1
C
Sheik et al. “Emergent auditory feature tuning in a real-time neuromorphic VLSI system” Frontiers in Neuromorphic Engineering, 2012.
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Implementing delays exploiting device mismatch
Sheik et al. “Exploiting Device Mismatch in Neuromorphic VLSI Systems to Implement Axonal Delays” International Joint Conference on NeuralNetworks, 2012.
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Emergent auditory feature tuningInput stimuli
A
B2B1
= Excitatory = Excitatory STDP = Inhibitory
C
A
B2B1
C
A
B2B1
C
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Emergent auditory feature tuningSynaptic Matrix after learning
A
B2B1
= Excitatory = Excitatory STDP = Inhibitory
C
A
B2B1
C
A
B2B1
C
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Emergent auditory feature tuningSynaptic Matrix after learning
A
B2B1
= Excitatory = Excitatory STDP = Inhibitory
C
A
B2B1
C
A
B2B1
C
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Emergent auditory feature tuningTuning curve
A
B2B1
= Excitatory = Excitatory STDP = Inhibitory
C
A
B2B1
C
A
B2B1
C
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Emergent auditory feature tuningTuning curve
A
B2B1
= Excitatory = Excitatory STDP = Inhibitory
C
A
B2B1
C
A
B2B1
C
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Conclusions
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Animal noises and argo floats
Lars Kindermann, “Strange Sounds of theSouthern Ocean”
Ocean Acoustics Lab, Alfred Wegener Institute,
DE
http://www.whoi.edu/
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Acknowledgments
Sadique Sheik§, Martin Coath�,Giacomo Indiveri§, Susan Denham�, Thomas Wennekers◦
Chiara Bartolozzi\, Emre Neftci§, Daniel Fasnacht§, Fabio Stefanini§, andRodney Douglas§
Funding: ICT-231168-SCANDLE “acoustic SCene ANalysis for DetectingLiving Entities” EU grant, DAISY (FP6-2005-015803) EU grant, and Cluster ofExcellence 277 (CITEC, Bielefeld University).
§ Institute of Neuroinformatics, University and ETH Zurich, CH� School of Psychology, University of Plymouth, UK◦ School of Computing and Mathematics, University of Plymouth, UK\ Italian Institute of Technology (IIT), IT
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Van Rossum distanceTuning curve
A
B2B1
= Excitatory = Excitatory STDP = Inhibitory
C
A
B2B1
C
A
B2B1
C
Given two spike trains s1 and s2, the Van Rossum distance is defined as:
D(s1,s2) =
√∫∞
−∞
[g ∗ s1−g ∗ s2]2dt
where g = g(t;τc) is a smoothing function (e.g., a decaying exponential) withtime constant τc .
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The VLSI AER transceiver chip (IFSLWTA)
Technology: AMS 0.35µmTotal area: 3.94mm×2.54mmCore area: 2.6mm×1.9mmNeurons: 128 (124 exc. + 4 inh.)Synapses: 32×128Dendritic tree multiplexer: 32x128 | 64x64 | ... | 4096x1|
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The 2D VLSI AER transceiver chip (2DIFWTA)
Technology: AMS 0.35µmArea: 5.14mm×2.94mmNeurons: 2048 (32×64)AER Synapses: 2048×3Local Synapses: 2048×11
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