2cm Emergent auditory feature tuning in a real-time neuromorphic VLSI …/file/Elisabetta... ·...

Emergent auditory feature tuning in a real-timeneuromorphic VLSI system

Elisabetta Chicca

Cognitive Interaction Technology Center of ExcellenceUniversity of Bielefeld

STINT Workshop 2012

E. Chicca(CITEC) Neuromorphic auditory features STINT Workshop 1

Neuromorphic Engineering


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.


Neuromorphic circuits


Building blocksThe biological neuron


Integrate-and-Fire neuron

0

10

20

30

Neuro

n

0 1 2Time (s)

0 1 20

10

20

30

Neuro

n

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


Integrate-and-Fire neuron

0

10

20

30

Ne

uro

n

0 1 2Time (s)

0 1 20

10

20

30

Ne

uro

n

Time (s)0.5 0.6 0.7 0.8

100

101

102

−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


Synaptic dynamicsImpulse response and short-term depression

C. Bartolozzi and G. Indiveri, “Synaptic dynamics in analog VLSI”, Neural Computation, 2007.


Synaptic dynamicsLearning

VL

θ

VH

wV

mem

0 0.1 0.2 0.3 0.4

pre

Time(s)

VL

θ

VH

wV

mem

0 0.1 0.2 0.3 0.4

pre

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.


Neuromorphic systems


Address Event Representation (AER)

3

2

1

Inputs

Encode Decode

Address Event Bus

Source

Chip

3

2

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.


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.


Emergent auditory feature tuning


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.


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.


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.


Emergent auditory feature tuningInput stimuli

A

B2B1


C

A

B2B1

C

A

B2B1

C


Emergent auditory feature tuningSynaptic Matrix after learning

A

B2B1


C

A

B2B1

C

A

B2B1

C


Emergent auditory feature tuningTuning curve

A

B2B1


C

A

B2B1

C

A

B2B1

C


Conclusions


Animal noises and argo floats

Lars Kindermann, “Strange Sounds of theSouthern Ocean”

Ocean Acoustics Lab, Alfred Wegener Institute,

DE

http://www.whoi.edu/


http://www.whoi.edu/

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


Van Rossum distanceTuning curve

A

B2B1


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 .


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|


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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