Dynamics of perceptual bistability. Alternating perceptions of ambiguous scenes J Rinzel, NYU NIST, 2011 w/ N Rubin, A Shpiro, R Curtu, R Moreno, E Montbrio,

Dynamics of perceptual bistability. Alternating perceptions of ambiguous scenes J Rinzel, NYU NIST, 2011

w/ N Rubin, A Shpiro, R Curtu, R Moreno, E Montbrio, E Sussman

What do we perceive when confronted with ambiguous sensory stimuli?

Bradley et al, 1998

In binocular rivalry: present different images to each eye. Do we perceive an averaged image

or…?

Dynamics of Perceptual Bistability


• Visual modality: moving plaids; Auditory modality: repeating triplet pattern

• Neuronal-based models

• Oscillator models --• mutual inhibition + slow negative feedback (adaptation)• noise gives randomness to period

• Attractor models – • noise driven, no alternation w/o noise• double-well potential: phenomenological model

• Use stats to constrain models

• Levelt II revisited: perceptual exploratory strategy

• Auditory “objects” and alternations: ABA_ABA_… common principles.

Funding: Swartz Foundation, NIH

PLAID DEMOR Moreno, N Rubin

Transparent + coherent. Some parameter variations.

C:\Documents and Settings\rinzeladmin\My Documents\TALKS & POSTERS\Binoc Rivalry talks\Binoc Rivalry JR Talks\PlaidEx\plaid-freq-800x600.exe

C:\Documents and Settings\rinzeladmin\My Documents\TALKS & POSTERS\Binoc Rivalry talks\Binoc Rivalry JR Talks\PlaidEx\plaid-800x600.exe

Pressnitzer & Hupe, Current Biology, 2006

Integration

Segregation

“galloping”

Sound parameters: DF = frequency differencePR = presentation rate

Triplet pattern (Van Noorden, 1975) -- possible ambiguity in auditory streaming.

Mutual inhibition with slow adaptation

alternating dominance and suppression

IV:

Levelt, 1968

Oscillator Models for Directly Competing Populations

w/ N Rubin, A Shpiro, R Curtu

Two mutually inhibitory populations, corresponding to each percept.Firing rate model: r1(t), r2(t)

Slow negative feedback: adaptation or synaptic depression.

Wilson 2003; Laing and Chow 2003

r1

Slow adaptation, a1(t)

r1 r2r2r1

No recurrent excitation

…half-center oscillator

τ dr1/dt = -r1 + S(-βr2 - φ a1+ I1)τa da1/dt = -a1 + fa(r1)

τ dr2/dt = -r2 + S(-βr1 - φ a2+ I2)τa da2/dt = -a2 + fa(r2)

τa >> τ , S(u)=1/(1+exp[(θ-u)/k])

u

f

S, fa

Shpiro et al, J Neurophys 2007

Input, I1 I2

a2

Alternating firing rates Adaptation slowly grows/decays

IV fa(u)=γu

adaptation LC model

Excessive symmetry

Five Regimes of Behavior,

Common to Neuronal Competition Models

Incr

easi

ng S

timul

us

Shpiro et al, J Neurophys 2006Curtu et al, SIADS, 2008

depression LC model Wilson’s model

Math – adaptation model with adaptation dominant: φ > β / (1+τ). (I1=I2.)

Dynamic states and stability depend on steepness of S and inhibition strength, β.

Weak inhibition: SIM stable, for all I- no altern’ns

Strong enough: then Hopf bifur’cns (2 of them) are supercritical and lead to anti-phase oscill’n.

Very strong: multiple equilibria, pitchfork

and, if stable, WTA.

Five Regimes of Behavior,

Common to Neuronal Competition Models

Shpiro et al, J Neurophys 2006Curtu et al, SIADS, 2008

depression LC model

φ

φ

Fast-Slow dissection: r1 , r2 fast variables a1 , a2 slow variables

r1- nullcliner2- nullcline

r1 = S(- β r2 - φ a1+ I1)r2 = S(- β r1 - φ a2+ I2)

Fast/Slow Dynamics

a1, a2 frozen

r1

r2

Decision making models, XJ Wang…

r1-r2 phase plane, slowly drifting nullclines

a1

a2

Switching occurs when a1-a2

traj reaches a curve of SNs (knees)

At a switch: • saddle-node in fast dynamics.• dominant r is high while system ridesnear “threshold”of suppressed populn’s nullcline ESCAPE.

β =0.9, I1=I2=1.4

r1- nullcliner2- nullcline

r1

r2

input

f

Dominant, a ↑ I – φ a

θ

net input

Suppressed, a ↓ I – φ a - β

Small I, “release”

Switching due to adaptation:release or escape mechanism

θ

net input

Large I, “escape”

Recurrent excitation, secures “escape”

I + α a – φ a

rj = S(input to j) = S(- β rk - φ aj+ Ij)

S

Noise leads to random dominance durations and eliminates WTA behavior.

Added to stimulus I1,2

s.d., σ = 0.03, τn = 10

Model with synaptic depression

τ dri/dt = -ri+ S(-βrj - φ ai+ Ii + ni)τa dai/dt = -ai + fa(ri)

Noise-Driven Attractor Modelsw/ R Moreno, N Rubin

J Neurophys 2007

Noise-Driven Attractor Models w/ R Moreno, N Rubin J Neurophys 2007

No oscillations ifnoise is absent.

Kramers 1940

Noise-induced bursting. Sukbin Lim, NYU

LP-IV in an attractor model

1D model

2D model

IV

gA= gB

acti

vity

rA=rB

WTA

OSC

Compare dynamical skeletons: “oscillator” and attractor-based models

Dynamical properties of a network withspiking neurons. Simulation results.

Levelt II

distribution

100 LIF units/ popul’n

fMRI: Polonsky et al, 2000Electrophysiology: Leopold, Logothetis…

Sheinberg and Logothetis, 1997fMRI: Tong et al, 1998

Neuronal correlates of alternations

Rubin’s face-vase: early visual cortex. Parkkonen et al, PNAS, 2008

fMRI

MEG

From Sterzer et al, review, TICS, 2009

Noise-free

Observed variability and mean duration constrain the model.

1 sec < mean T < 10 sec

0.4 < CV < 0.6

With noise

Difficult to arrange high CV andhigh <T> in OSC regime.

With noise

Favored: noise-driven attractor with weak adaptation – but notfar from oscillator regime.

… LP-II in binocular rivalryParameter dependence of mean T and f:

contrast B

contrast A

Eye B

Eye Afrac

tion

B0.14

0.5

0

contrast B

contrast A

LP-II is not valid for all contrast values

TA

TB

contrast B

w/ Moreno, Shpiro, Rubin

???

Transparent + different frequencies.

Plaids with different wavelengths depth reversals

C:\Documents and Settings\rinzeladmin\My Documents\TALKS & POSTERS\Binoc Rivalry talks\Binoc Rivalry JR Talks\PlaidEx\plaid-freq_very_diff.exe

Percent dominance reflects brain’s estimate of probability of depth.

Levelt’s II generalized, Moreno et al, J Vision (2010), ‘Parameter manipulation of an ambiguous stimulus affects mostly the mean dominance duration of the stronger percept.’

also Brascamp et al 2006

Alternation: a perceptual exploration strategy.

#1

behi

nd #1

behi

nd

w/ Moreno, Shpiro, RubinJ of Vision, 2010

Maximum alternation rate at equidominance

log (1 ) log(1 )A A A AEntropy f f f f

Moreno-Bote et al, J of Vision, 2010.

depth reversals plaids

n=4

rivalry

0

AA

A B

gI

g g g

0

BB

A B

gI

g g g

Models: Input normalization and maximum alternation rate at equidominance

fraction of dominance

Alte

rnat

ion

rate

w/ Moreno, Shpiro, Rubin

Temporal dynamics of auditory and visual bistability – common principles of perceptual organization.

Pressnitzer & Hupe, 2006

Van Noorden, 1975

Visual


Integrated or segregated perceptsGrouped or split percepts

Exclusivity, randomness, and inevitability


Leopold & Logothetis, 1999

Visual




Bistability/alternation in auditory streaming – integration (galloping) or segregation.

Tone duration 120 ms, 3 STs, A=440 Hz, B=523 Hz3 STs 4 STs

Visual Auditory



Exclusivity, randomness, and inevitability


Leopold & Logothetis, 1999

w/ Montbrio

w/ Sussman, Montbrioascend descend

Direct demonstration of bistability for range of DF

Tone duration 120ms, 4 reps


• Visual & auditory modality

• Neuronal-based models

• Oscillator models --• mutual inhibition + slow negative feedback (adaptation)• noise gives randomness to period

• Attractor models – • noise driven, no alternation w/o noise• double-well potential: phenomenological model

• Use stats to constrain models

• Levelt II revisited: perceptual exploratory strategy

• Auditory “objects” and alternations: ABA_ABA_… common principles.

Funding: Swartz Foundation, NIH

SUMMARY

Other works: Dayan ‘98, Lehky ‘88, Grossberg ’87, Wilson ‘01

a1 adaptation

#1 dominant & adapting

#1 suppressed & recovering from adaptation

#1 goes “off”

#1 comes “on”

Range of a1 for bistability

DF = DF*, fixed

Some range of DFhas bistability

r1 f

irin

g ra

te

DF

a1

DF*

Range of DFfor bistability

Schematic of bistability and hysteresis