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Spaun: A biologically realistic large-scale functional brain modelChris Eliasmith (Centre for Theoretical Neuroscience, University of Waterloo)

Trevor Bekolay, Xuan Choo, Terrence C. Stewart, Travis DeWolf, Yichuan Tang, Daniel Rasmussen, Jan Gosmann

The Semantic Pointer Architecture UnifiedNetwork (Spaun) is a network of 2.5 millioninterconnected artificial spiking neurons.

Spaun is composed of groups of neurons that perform functions nec-essary to complete cognitive tasks. It flexibly coordinates thosegroups depending on the cognitive task being performed.

Working memory

Visualinput

Informationencoding

Transformcalculation

Rewardevaluation

Informationdecoding

Motorprocessing

Motoroutput

Action selection

These functionally related groups of neurons are mapped onto brainareas consistent with our current understanding of functional neu-roanatomy. Spaun can be manipulated in order to test hypothesesin neuroscience.

PPCM1 SMA

PM

DLPFC

VLPFC

OFC

AIT

Thalamus

ITV4V2

V1

Str (D2)

GPe

STN Str (D1)

GPi/SNr

SNc/VTA

vStr

TheoryCNS

Systems

Maps

Networks

Neurons

Synapses

Molecules

1m

10cm

1cm

1mm

100μm

1μm

10nm

⇐ Nengo modelling software1

⇐ Semantic pointer architecture2

List=Pos1~Item1�Pos2~Item2 . . .

⇐ Neural Engineering Frameworkωij =αjdiej x̂=∑

ai(x)di

⇐ Single-cell modellingdVdt =− 1

RC (V(t)−J(t)R)⇐ Post-synaptic current curves

h(t)= e−t/τ if t> 0

Spaun performs tasks like humans

PFC

DLFPC1

PFC

SMA

stimulus

IT

Str

GPi

DLPFC2

DLFPC1

DLPFC2

arm

Time (s)

*

**

*

*

* * * *

**

*

*

* *+

+ +

+

+ +

+

A 7 [ 1 ] [ 1 1 ] [ 1 1 1 ] [ 4 ] [ 4 4 ] [ 4 4 4 ] [ 5 ] [ 5 5 ] ?

Chance Humans SpaunImage recognition 10% 98% 94%

Fluid reasoning 13% 89% 88%

Number of positions counted

Tim

e (s

)

1 2 3 4 50.5

0.75

1

1.25

1.5

1.75

2

2.25

2.5

Counting

1 2 3 4 5 6 70

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Item position

Accu

racy

Question answering

What position is X?What is in position Y?

Spaun makes mistakes like humans

0 1 5 8 7 3

stimulus

IT

Str

GPi

PFC

DLPFC

DLPFC

SMA

Arm

Time (s)0 1 2 3 4 5 6

A 3 [ 0 1 5 8 7 3 ] ?

1 2 3 4 5 6 70

0.1

0.5

0.6

0.7

0.8

0.9

1

4 items5 items6 items7 items

1 2 3 4 5 6 70

0.1

0.5

0.6

0.7

0.8

0.9

1

4 items5 items6 items7 items

Item position

Acc

ura

cy

List memory (Human data)

Acc

ura

cy

List memory (Spaun)

Item position

Spaun matches experimental data

0

2

4

6

8

Spik

es p

er s

econ

d

Delay Approach Reward Return

Experimental vStrSimulated vStr

5

15

25

35

Tria

l num

ber

0.0 0.1 0.2 0.3 0.4 0.5

Time (s)0

255075

100125150

Neu

ron

num

ber

Data Spaun

Data Spaun

Spaun can perform 8 cognitive tasks using only visual informationand without external intervention.

Copy drawing“Copy this 2.”

A 0 [ ] ? ⇒

Image recognition“Write a 2.”

A 1 [ 2 ] ? ⇒

Gambling“Try slot machine 0, 1 or 2.”

A 2 ? ⇒ ⇒ 0A 2 ? ⇒ ⇒ 1A 2 ? ⇒ ⇒ 1

List memory“Write the list 0 1 5 8 7 3.”

A 3 [ 0 1 5 8 7 3 ] ?⇒

Counting“Starting from 3, count 5.”

A 4 [ 3 ] [ 5 ] ? ⇒

Question answering“Write the 2nd number in 0 1 5 8 7 3.”

A 5 [ 0 1 5 8 7 3 ] [ P ] [ 2 ] ?⇒

Rapid variable creation“Complete the pattern:

0014→14, 0094→94, 0074→?”

A 6 [ 0 0 1 4 ] [ 1 4 ][ 0 0 9 4 ] [ 9 4 ][ 0 0 7 4 ] ?⇒

Fluid reasoning“Fill in the last cell.”

A 7 [ 1 ] [ 1 1 ] [ 1 1 1 ][ 4 ] [ 4 4 ] [ 4 4 4 ][ 5 ] [ 5 5 ] ?⇒

Recent developments

General instruction processingConductance

neuron model3

1.0

0.5

0.0

0.5

1.0

Input B (50 standard LIF)

0 1 2 3 4 5 6

1.0

0.5

0.0

0.5

1.0

0 1 2 3 4 5 6

Input B (50 compartmental)

Time (s) Time (s)

0 10 20 30 40 50 60 70 800

20

40

60

80

100

Percentage of Na+ Channels Blocked (%)

Dig

it R

ecog

nitio

n A

ccur

acy

(%

)

A large-scale model of the functioning brain. Science, 338:1202–1205.Chris Eliasmith, Terrence C. Stewart, Xuan Choo, Trevor Bekolay, Travis DeWolf,Yichuan Tang, and Daniel Rasmussen (2012).

1) Trevor Bekolay et al. (2013). Nengo: a Python tool for building large-scalefunctional brain models. Frontiers in Neuroinformatics 7.Available at http://github.com/nengo/nengo

2) Chris Eliasmith (2013). How to build a brain: A neural architecture for biologicalcognition. Oxford University Press.

3) Armin Bahl, Martin B. Stemmler, Andreas V.M. and Arnd Roth (2012). Automatedoptimization of a reduced layer 5 pyramidal cell model based on experimental data.Journal of Neuroscience Methods, 210:22–34.

View this poster online at http://compneuro.uwaterloo.ca/files/spaun-2015.pdf