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A model of naturalistic decision making in preference tests
John Ksander1,2, Donald B Katz1,2, Paul Miller1,3
1Volen National Center for Complex Systems, Brandeis University, Waltham, Massachusetts, United States of America.
Plos Computational Biology
|September 23, 2021
Summary
Neural circuit models explain how animals decide to switch behaviors, like foraging. These models show switching depends on stimulus value and neural activity, offering insights into optimal foraging strategies.
Area of Science:
- Neuroscience
- Computational Biology
- Animal Behavior
Background:
- Animals constantly make decisions about continuing or switching behaviors, crucial for survival, especially in foraging.
- Stimulus perception and behavioral choices dynamically influence each other.
Purpose of the Study:
- To present model neural circuits using spiking neurons to explain the state transitions underlying behavioral switching.
- To analyze two circuit classes: 'entice to stay' (loss of positive input) and 'repel to leave' (aversive input).
Main Methods:
- Developed spiking neuron models for decision-making circuits.
- Analyzed two distinct circuit architectures: 'entice to stay' and 'repel to leave' models.
- Investigated the impact of stimulus value and synaptic dynamics on state transitions.
Main Results:
- In both models, increased alternative stimulus value decreased sampling time, linked to depressing synapses.
- Found greater competitive interaction in 'entice to stay' models.
- Observed qualitative similarities to the marginal value theorem in 'entice to stay' networks.
Conclusions:
- The models provide a framework for understanding optimal foraging behavior through neural circuit dynamics.
- The study suggests testable predictions for electrophysiological and behavioral experiments to validate the models.
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