Learning new tasks and exposure to new environments lead to changes in the dynamics of brain circuits, as observed in various recent experiments. The ability to embed the statistics of the environment within brain circuits is essential for animals ability to thrive and survive in changing environments. However, the mechanisms by which circuits dynamics are implemented and learned are not well understood, and pose significant theoretical challenges. Recent work in both theoretical and experimental labs has highlighted the importance of circuit dynamics. Yet in most theoretical models the network connectivity is either not plastic, or obeys biologically implausible learning rules. Here we will develop a theory of how brain circuits can learn their dynamics from the statistics of the environment. We will anchor this work in a set of experiments, in order to make it biologically realistic and limited in scope.
In aim 1 we will try to understand how networks can learn stimulus-reward spatiotemporal statistics.
This aim will be based on circuit level experiments that show how neuronal dynamics change due to a stimulus followed by a delayed reward, and by cellular experiments that shed light on the mechanisms of reinforcement learning. This is a problem we know more about, and it is also inherently simpler than learning the statistics of the environment in an unsupervised manner.
In aim 2 we will concentrate on experiments on which cortical circuits learn the order, but not the timing, of a spatiotemporal sequence. In such networks the timing of the learned sequence are determined by intrinsic network dynamics; making this problem simpler than learning both the order and the timing of a sequence.
In aim 3 we develop networks and learning rules that can learn both the order and the timing of a spatiotemporal sequence. This effort will build on results in aim 2 in which the order of events is learned in an unsupervised manner, and of aim 1 in which the timing of events is learned using reinforcement learning.

Public Health Relevance

Learning new tasks and exposure to new environments lead to changes in the dynamics of brain circuits that are essential for animal survival. However, the mechanisms by which circuits dynamics are implemented and learned are not well understood, and pose significant theoretical challenges. We propose to develop a theoretical foundation for how brain circuits implement and learn their dynamics from the statistics of the environment, using both biophysically realistic models and advanced analytical techniques. The theory will be constrained by a set of specific experimental data that we will aim to reproduce.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
5R01EB022891-03
Application #
9545564
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Peng, Grace
Project Start
2016-09-26
Project End
2019-07-31
Budget Start
2018-08-01
Budget End
2019-07-31
Support Year
3
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of Texas Health Science Center Houston
Department
Neurosciences
Type
Schools of Medicine
DUNS #
800771594
City
Houston
State
TX
Country
United States
Zip Code
77030
Pereira, Ulises; Brunel, Nicolas (2018) Attractor Dynamics in Networks with Learning Rules Inferred from In Vivo Data. Neuron 99:227-238.e4