Optimality, robustness, and dynamics of decision making under norepinephrine modulation:
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Optimality, robustness, and dynamics of decision making under norepinephrine modulation: A spiking neuronal network model. Joint work with Philip Eckhoff and Phil Holmes. Sloan-Swartz Meeting 2008. Experimental results: Cellular level.

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Joint work with Philip Eckhoff and Phil Holmes

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Optimality, robustness, and dynamics of decision making under norepinephrine modulation:A spiking neuronal network model

Joint work with Philip Eckhoff and Phil Holmes

Sloan-Swartz Meeting 2008

Experimental results: Cellular level

  • Norepinephrine (NE) modulates EPSP, IPSP, cellular excitability

  • Locus coeruleus (LC) supplies NE throughout the brain

  • LC neurons exhibit tonic or phasic firing rate mode

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

| | | || | | | | || | | | || |

Tonic mode

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Phasic mode

Berridge and Abercrombie (1999)

Aston-Jones et. al (1999)

Aston-Jones and Cohen (2005)

Experimental results: Behavioral level

Inverted-U shape performance in behavioral tasks

Past 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).


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

  • 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 time

Choice 1 made



X.-J. Wang (2002)

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



… …

… …

n trial


n+1 trial


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

Assume linear LC [NE] gsyn

“1” denotes

standard set of parameters of Wang (2002)

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


Too high

Too low

Neural dynamics under tonic modulationof E and I cells





Firing rate



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

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 modulation

NE = 100 ms

Delay = 200 ms

[NE] = F(LC) for phasic?

dg / dt = G( [NE] ) ? Assume linear.

Phasic modulation can provide further improvement in performance…

… provided glutamatergic modulation dominates over that of GABAergic synapses


  • 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.


  • Barry Waterhouse, Drexel University College of Medicine

  • Jonathan Cohen, Princeton University

  • PHS grants MH58480 and MH62196

  • AFOSR grant FA9550-07-1-0537

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