Optimality, robustness, and dynamics of decision making under

norepinephrine modulation: A spiking neuronal network model

Optimality, robustness, and dynamics of decision making under

norepinephrine modulation: A spiking neuronal network model

Joint work with Philip Eckhoff and Phil HolmesJoint work with Philip Eckhoff and Phil Holmes

Sloan-Swartz Meeting 2008

Experimental results: Cellular levelExperimental results: Cellular level Norepinephrine (NE) modulates EPSP, IPSP, cellular

excitability Norepinephrine (NE) modulates EPSP, IPSP, cellular

excitability Locus coeruleus (Locus coeruleus (LCLC) supplies NE throughout the brain) supplies NE throughout the brain Locus coeruleus (Locus coeruleus (LCLC) supplies NE throughout the brain) supplies NE throughout the brain

LC neurons exhibit tonic or phasic firing rate mode LC neurons exhibit tonic or phasic firing rate mode

[NE] release approx linear to tonic firing rate of LC [NE] release approx linear to tonic firing rate of LC

| | | || | | | | || | | | || |Tonic mode

| | || ||||||| | | | | |Phasic mode

Berridge and Abercrombie (1999)

Aston-Jones et. al (1999)Aston-Jones and Cohen (2005)

Experimental results: Behavioral level

Experimental results: Behavioral level

Inverted-U shape performance in behavioral tasks

Past modeling workPast modeling work

(i) Connectionist modeling e.g. Usher et al (1999); Brown et al (2004); Brown et al (2005)

(ii) Normative (Bayesian) approach e.g. Yu and Dayan (2005); Dayan and Yu (2006)

(iii) Biophysical modeling work are more concerned with signal-to-noise ratio, e.g. Hasselmo (1997); Moxon et al (2007).

GoalGoal

To link cellular to behavioral level of LC-NE modulation, in the context of a decision-making reaction task task, and study the decision circuit’s performance (reward rate) using a spiking neuronal network model

A spiking neuronal network model for 2-alternative forced-choice decision-

making tasks

A spiking neuronal network model for 2-alternative forced-choice decision-

making tasks

X.-J. Wang (2002)

II11 II22

Neuronal model: Leaky integrate-and fire

Recurrent excitatory synapses: AMPA, NMDA

Inhibition: GABAA

External inputs (background, stimulus): AMPA

Task difficulty depends on:

(I1 - I2) /(I1 + I2)

Neuronal model: Leaky integrate-and fire

Recurrent excitatory synapses: AMPA, NMDA

Inhibition: GABAA

External inputs (background, stimulus): AMPA

Task difficulty depends on:

(I1 - I2) /(I1 + I2)

Decision timeChoice 1 made

Performance in a reaction time task: Rate of receiving reward

Performance in a reaction time task: Rate of receiving reward

Reward rate = (Total # of correct trials) / (Total time) Total time = Sum of Reaction time + Response-to-stimulus interval Reaction time = Decision time + non-decision latency

Reward rate = (Total # of correct trials) / (Total time) Total time = Sum of Reaction time + Response-to-stimulus interval Reaction time = Decision time + non-decision latency

Time

n trial n+1 trialRSI… … … …

RT RT

Tonic LC-NE modulation of both E and I cells provides robust decision performance

Tonic LC-NE modulation of both E and I cells provides robust decision performance

Robust performance for modulation of NMDA or AMPA, as long as E and I cells are modulated together

“1” denotesstandard set of parameters of Wang (2002)

Assume linear LC [NE] gsyn

Neural dynamics under tonic modulationof E and I cells

Neural dynamics under tonic modulationof E and I cells

Standard

Too low

Too high

IncreasingLC-NE

Unmotivated

Impulsive

Standard/Optimal

Firi

ng r

ate

Time

Differential tonic modulation between E and I cells

Differential tonic modulation between E and I cells

There exists a maximum robustness when synapses of E cells are modulated about half that of I cells

Single-cell evoked response under tonic modulation

Single-cell evoked response under tonic modulation

Condition of maximum robustness also results in an inverted-U shape for single-cell evoked response. Since we used linear modulation, inverted-U shape is a pure network effect.

Phasic LC-NE modulationPhasic LC-NE modulation

[NE] = F(LC) for phasic? dg / dt = G( [NE] ) ? Assume linear.

NE = 100 ms = 100 ms

Delay = 200 msDelay = 200 ms

Phasic modulation can provide further improvement in performance…

Phasic modulation can provide further improvement in performance…

… provided glutamatergic modulation dominates over that of GABAergic synapses

ConclusionConclusion Inverted-U shape in decision performance Tonic co-modulation of E and I cells provides

robust performance (more expt on I cells needed to confirm)

Lesser affinity of E to I cells to tonic modulation results in: (i) maximum robust performance; (ii) inverted-U shape of single-cell evoked response (can be a pure network effect)

[NE] = F(LC) for phasic LC mode? If F is linear, our work shows that phasic modulation can further improve over tonic when modulation of glutamatergic synapses dominate over GABAergic.

Inverted-U shape in decision performance Tonic co-modulation of E and I cells provides

robust performance (more expt on I cells needed to confirm)

Lesser affinity of E to I cells to tonic modulation results in: (i) maximum robust performance; (ii) inverted-U shape of single-cell evoked response (can be a pure network effect)

[NE] = F(LC) for phasic LC mode? If F is linear, our work shows that phasic modulation can further improve over tonic when modulation of glutamatergic synapses dominate over GABAergic.

AcknowledgementsAcknowledgements

Barry Waterhouse, Drexel University College of Medicine

Jonathan Cohen, Princeton University

PHS grants MH58480 and MH62196 AFOSR grant FA9550-07-1-0537

Barry Waterhouse, Drexel University College of Medicine

Jonathan Cohen, Princeton University

PHS grants MH58480 and MH62196 AFOSR grant FA9550-07-1-0537