This proposal aims to integrate two streams of research on learning and memory in an attempt to strengthen the links between theory and experiment, build models that explain experimental observations and use model predictions to guide new experiments. The experimental stream will record neuronal population activity in inferior temporal, perirhinal and prefrontal cortices during performance of delayed matching tasks which require maintenance of visual information in short term memory, using visual stimuli with various degrees of familiarity (from entirely novel to highly familiar). The modeling stream will investigate learning and memory in network models that include learning rules inferred from data, using a combination of mean field analysis and simulation. Models will generate predictions on patterns of delay period activity that will be tested using experimental data. The goals of this combined experimental and theoretical project will be to answer the following questions: How do changes in synaptic connectivity induced by learning due to repeated presentation of a particular stimulus affect the distributions of visual responses of neurons? In other words, how do neuronal representations change in cortex as a novel stimulus becomes familiar? Can we infer the learning rule in cortical circuits from experimentally observed changes in distributions of neuronal responses as the stimuli become familiar? Do changes in synaptic connectivity induced by learning rules that are consistent with the statistics of visual responses lead to delay period activity in a task such as the OMS task? Is delay period activity already present upon the first presentation of a stimulus, or does it develop over time? If it is not present during the initial presentations, how is sample information maintained in memory during the delayed match to sample task? see attached continuation
See attached
| Pereira, Ulises; Brunel, Nicolas (2018) Attractor Dynamics in Networks with Learning Rules Inferred from In Vivo Data. Neuron 99:227-238.e4 |
