Analysis of an Attractor Neural Network Model for Working Memory: A Control Theory Approach

Time: Wed 2019-07-03 10.00 - 11.00

Location: Seminar room C:728 (Harry Nyquist), Malvinas väg 10, Q-huset, floor 7, KTH Campus

Respondent: Gianluca Villani

Opponent: Abhishek Maji

Supervisor: Matin Jafarian

Examiner: Karl Henrik Johansson

Abstract: Working Memory (WM) is a general-purpose cognitive system responsible for temporary holding information in service of higher order cognition systems, e.g. decision making. In this thesis we focus on a non-spiking model belonging to a special family of biologically inspired recurrent Artificial Neural Network aiming to account for human experimental data on free recall. Considering its modular structure, this thesis gives a networked system representation of WM in order to analyze its stability and synchronization properties. Furthermore, with the tools provided by bifurcation analysis we investigate the role of the different parameters on the generated synchronized patterns. To the best of our knowledge, the proposed dynamical recurrent neural network has not been studied before from a control theory perspective.

Belongs to: Decision and Control Systems
Last changed: Jun 24, 2019