On Bubbles and Drifts: Continuous attractor networks in brain models Thomas Trappenberg Dalhousie University, Canada.

On Bubbles and Drifts:Continuous attractor networks in brain models

Thomas TrappenbergThomas Trappenberg

Dalhousie University, Canada Dalhousie University, Canada

Once upon a time ... (my CANN shortlist)

Wilson & Cowan (1973) Grossberg (1973) Amari (1977) … Sampolinsky & Hansel (1996) Zhang (1997) … Stringer et al (2002)

It’s just a `Hopfield’ net …

I ext rout

w

w

x

Recurrent architecture Synaptic weights

In mathematical terms …

Updating network states (network dynamics)

Gain function

Weight kernel

Weights describe the effective interaction profile in Superior Colliculus

TT, Dorris, Klein & Munoz, J. Cog. Neuro. 13 (2001)

Network can form bubbles of persistent activity (in Oxford English: activity packets)

0 5 10 15 20

20

40

60

80

100

Time [t]

Nod

e in

dex

External stimulus

End states

Space is represented with activity packets in the hippocampal system

From Samsonovich & McNaughtonPath integration and cognitive mapping in a continuous attractor neural J. Neurosci. 17 (1997)

There are phase transitions in the weight-parameter space

CANNs work with spiking neurons

Xiao-Jing Wang, Trends in Neurosci. 24 (2001)

Shutting-off works also in rate model

Time

No

de

Various gain functions are used

End states

CANNs can be trained with Hebb

Hebb:

Training pattern:

Normalization is important to have convergent method

• Random initial states• Weight normalization

w(x,50)

Training timex

x y

w(x,y)

Gradient-decent learning is also possible (Kechen Zhang)

Gradient decent with regularization = Hebb + weight decay

CANNs have a continuum of point attractors

Point attractors and basin of attraction

Line of point attractors

Can be mixed: Rolls, Stringer, Trappenberg A unified model of spatial and episodic memoryProceedings B of the Royal Society 269:1087-1093 (2002)

Neuroscience applications of CANNs

Persistent activity (memory) and winner-takes-all (competition)

• Working memory (e.g. Compte, Wang, Brunel etc)

• Place and head direction cells (e.g. Zhang, Redish, Touretzky, Samsonovitch, McNaughton, Skaggs, Stringer et al.)

• Attention (e.g. Olshausen, Salinas & Abbot, etc)

• Population decoding (e.g. Wu et al, Pouget, Zhang, Deneve, etc )

• Oculomotor programming (e.g. Kopecz & Schoener, Trappenberg)

• etc

Superior colliculus intergrates exogenous and endogenous inputs

C N

S N p r

T h a l

S E F

F E F

L IP

S C

R F

Cerebellum

Superior Colliculus is a CANN

TT, Dorris, Klein & Munoz, J. Cog. Neuro. 13 (2001)

CANN with adaptive input strength explains express saccades

CANN are great for population decoding (fast pattern matching implementation)

CANN (integrators) are stiff

… and drift and jumpTT, ICONIP'98

Modified CANN solves path-integration

CANNs can learn dynamic motor primitives

Stringer, Rolls, TT, de Araujo, Neural Networks 16 (2003).

Drift is caused by asymmetries

NMDA stabilization

CANN can support multiple packets

Stringer, Rolls & TT,Neural Networks 17 (2004)

How many activity packets can be stable?

T.T., Neural Information Processing-Letters and Reviews, Vol. 1 (2003)

Stabilization can be too strong

TT & Standage, CNS’04

CANN can discover dimensionality

0

1 1

( )( )( ) ( )

hdhd vii ihd

ac hd acij jhd c hd ac

j

inh hdij j

j

c hd cij j

j

dh th t Iw w r t

dt C

w rw r rC C

r

t

t

C

: activity of node i

: firing rate

: synaptic efficacy matrix

: global inhibition

: visual input

: time constant

: scaling factor

: #connections per node

: slope

: threshold

viI

ih

0

inhw

ir

ijw

Continuous dynamic (leaky integrator):

The model equations:

NMDA-style stabilization:1

2

if 0.5( )

elsewherei

i

rr

2 ( ( )) 1(1 e )i ih rir

ij i jw kr r Hebbian learning:

c hd hd cij i jw kr r r ac hd hd acij i jw kr r r