January 17, 2024, 16:00: Talk Prof. Martin Nawrot at the University of Tübingen, Germany
Title: Computation in spiking cortical attractor networks: action selection, reaction times and cortical variability
Short abstract: We investigate computation for sensory-motor transformation in spiking cortical attractor networks and compare our results to behavioral data and single-unit recordings from the monkey motor cortex. We find that a network topology with locally balanced excitatory-inhibitory clusters robustly shows metastability across a large range of network parameters. We use large scale spiking network simulations to perform a virtual delayed arm-reaching task. This allows for the reproduction of empirical reaction times, neural encoding of movement direction depending during movement preparation, and the task-related dynamics of cortical spike train variability as observed in the monkey in vivo data. I will discuss future steps of model extension.
Schmitt, F. J., Rostami, V., & Nawrot, M. P. (2023). Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN and NEST. Frontiers in Neuroinformatics, 17, 941696.
Rostami, V., Rost, T., Riehle, A., van Albada, S., & Nawrot, M. (2022). Spiking attractor model of motor cortex explains modulation of neural and behavioral variability by prior target information. Preprint, https://doi.org/10.21203/rs.3.rs-1337724/v1