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.
Joint work with Philip Eckhoff and Phil Holmes
Sloan-Swartz Meeting 2008
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Berridge and Abercrombie (1999)
(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
(I1 - I2) /(I1 + I2)
Choice 1 made
X.-J. Wang (2002)
Assume linear LC [NE] gsyn
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 lowNeural dynamics under tonic modulationof E and I cells
There exists a maximum robustness when synapses of E cells are modulated about half that of I cells
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.
NE = 100 ms
Delay = 200 ms
[NE] = F(LC) for phasic?
dg / dt = G( [NE] ) ? Assume linear.
… provided glutamatergic modulation dominates over that of GABAergic synapses