A computational network model of the neural circuits subserving motivated behaviours

Authors and Affiliations: Suzanna Becker, PhD ¹*, Andrew Chan ¹, Paul Fletcher, PhD ² and Shitij Kapur, PhD ². ¹Psychology, McMaster University, Hamilton, Ontario, Canada and ²Research Section, CAMH, and Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.

Abstract Body:
Motivated behaviours arise from complex interactions between multiple brain
areas including limbic structures representing primary drives and incentives, cortical regions where more abstract cognitive representations are formed, and ascending neurotransmitter systems which modulate these representations. We are developing a neural network model to explain the of the role of ascending neuromodulators (esp. dopamine) acting on these brain structures in both the learning and expression of motivated behaviours. Previous computational models have focused either on the neuromodulation of behavioral expression over longer time-frames or on the neuromodulation of learning over millsecond time-frames. The current effort attempts to combine these two levels of analysis, thereby integrating several disparate views on the role of ascending neuromodulatory systems.

Methods:
The objective of this work is to develop and test a computational model that can help understand the neural mechanisms underlying motivated action and the effects of psychotropic drugs on these behaviours. Fundamental to our model are the following principles: Novel, appetitive and aversive events cause the release of dopamine, which has two consequences: 1) to prepares the system for learning about the consequences of these events, and 2) to prepare the system for taking action. We are developing computational models to test various components of this framework. Predictions generated by the computational modelling will then be tested in animal learning experiments.

Results
We have implemented a computational model encompassing several components of the above framework, in particular the role of the ventral tegmental
area and nucleus accumbens in gating the associations between conditioned
stimuli and motivated actions. We are now extending the model to include the the learning mechanisms underlying stimulus encoding in the limbic areas that support motivational/emotional memories.

Conclusions:
Our model should be able to simulate the effects of pharmacological agents including anti-psychotics and amphetamines on standard associative learning paradigms such as conditioned avoidance learning and latent inhibiton.