Imperial College London | UK
Brain networks are plastic, and plasticity is usually classified into two distinct categories: Hebbian or homeostatic. Hebbian is driven by correlation in the activity of neurons and is considered to be the basis of learning and memory, while homeostatic relies on a negative feedback signal to control neuronal activity. The exact way multiple types of plasticity interact in the brain, however, remains to be elucidated. In our work, simulations of spiking neural networks and mathematical tools were employed to show that there is more to homeostatic plasticity than just controlling network stability.More specifically, we showed that homeostatic plasticity has a Hebbian effect on the network level, leading to formation of cell assemblies upon stimulation. Using a model of classical conditioning task, we demonstrated that this rule can perform pattern completion, and that network response upon stimulation is gradual, reflecting the strength of the memory.
Furthermore, we showed that networks grown with homeostatic structural plasticity and a broad distribution of target rates exhibit non-random features similar to some of those found in cortical networks. It remains an open question, however, the extent to which homeostatic plasticity can be accounted for structural features found in the brain.
Full list of BCF seminars: here
DATES AND VENUE
Feb 16, 2021
from05:00 PM to05:30 PM
Zoom Meeting. You can contact Fiona Siegfried for meeting ID and password.