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Updated: Jun 24, 2026
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An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
Published on: August 2, 2018
Optimality and robustness of a biophysical decision-making model under norepinephrine modulation.
Philip Eckhoff1, K F Wong-Lin, Philip Holmes
1Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, USA. peckhoff@princeton.edu
Summary
Norepinephrine (NE) from the locus ceruleus (LC) influences decision-making. Optimal performance in tasks occurs within a specific range of tonic NE levels, with phasic NE offering further improvements.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Systems Neuroscience
Background:
- The locus ceruleus (LC) releases norepinephrine (NE) modulating cortical activity and influencing behavior.
- LC-NE activity can be tonic or phasic, impacting cellular excitability and synaptic function.
Purpose of the Study:
- To investigate the effects of LC-NE modulation on decision-making performance in a computational model.
- To explore how different levels and patterns of NE influence reward rate and network functions.
Main Methods:
- Utilized a biophysical neural network model to simulate LC-NE modulation.
- Adjusted conductances within the model to represent varying NE levels and activity patterns.
- Measured performance in simulated two-alternative forced-choice tasks.
Main Results:
- Optimal decision-making performance was observed across a broad middle range of tonic NE levels.
- Low NE levels led to unmotivated behavior, while high levels resulted in impulsive, inaccurate choices.
- Phasic NE release enhanced performance, particularly when modulating only glutamatergic synapses.
Conclusions:
- LC-NE levels critically influence decision-making, with an optimal range for performance.
- Network functions like sensory accumulation and memory are modulated by tonic NE.
- Observed diverse neural responses may stem from network-level effects of NE.
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