In this talk decision-making and temporal tasks inspired by Boolean functions are analyzed, exploring connectivity patterns, dynamics, and biological constraints of recurrent neural networks (RNNs) after training. The focus of such models in Computational Neuroscience is brain regions such as the cortex and prefrontal cortex and their recurrent connections related with different cognitive tasks. Understanding the dynamics behind these models is crucial for building hypotheses about brain function and explaining experimental results.

Dynamics is analyzed through numerical simulations, and the results are classified and interpreted. The study sheds light on the multiplicity of solutions for the same tasks and the link between the spectra of linearized trained networks and the dynamics of their counterparts. The distribution of eigenvalues of the recurrent weight matrix was studied and related to the dynamics in each task. Approaches and methods based on trained networks are presented. The importance of having a software framework that facilitates testing different hypotheses and constraints is also emphasized.

Cecilia Jarne did her PhD in physics at the IFLP and the Physics Department of the National University of La Plata and a PostDoc at the Buenos Aires University Physics department (IFIBA). Her research experience is based on large data sets analysis, programming and modelling, first in high-energy cosmic ray physics and then during her postdoctoral research analyzing bird songs and dynamics.  Currently, she is an assistant researcher and Professor at Universidad Nacional de Quilmes and  CONICET working on Recurrent Neural Networks and Complex Systems since 2018. During 2023 she is doing a research stay at the CFIN in Aarhus, Denmark.