Optimality, robustness, and dynamics of decision making under norepinephrine modulation: A spiking...
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Transcript of Optimality, robustness, and dynamics of decision making under norepinephrine modulation: A spiking...
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