Arvind Kumar: Feedforward and feedback interactions in the striatum
| When |
Oct 26, 2022
from 12:15 PM to 01:00 PM |
|---|---|
| Where | Zoom Lecture. Meeting ID and password will be sent with e-mail invitation. You can also contact Fiona Siegfried for Meeting ID and password. |
| Contact Name | Fiona Siegfried |
| Add event to calendar |
 vCal iCal
|
Abstract
Striatum is the main input station of the basal ganglia. Therefore, striatum is involved in many motor and cognitive functions. At the network level striatum is unique in the sense that it is a purely inhibitory network of medium spiny neurons (MSNs) which spike at a very low rate. There are two populations of MSNs which more or less exclusively project to the so-called direct (dMSN) and indirect (iMSN) pathways. 5% neurons in the striatum constitute the fast spiking interneurons (FSIs), cholinergic interneurons and a few other types. All experimental evidence suggests high recurrent connectivity among the MSNs (though with weak synapses) and between MSNs and interneurons. Yet, the functional and dynamics role of recurrent connectivity among and between MSN populations and feedforward inhibition from FSIs is unclear.
In my talk I will show that feedforward (FSI) and feedback (between MSNs) inhibition have opposing roles in controlling synchrony in the striatum. While FSIs enhance synchrony, feedback inhibition tends to counter that. When we consider stimulus representation, we found that the FSIs which constitute only 1% of the striatal population have an even more unexpected role. On one hand FSIs increase the across trial variability.
On the other hand FSIs create a temporal winner-take-all (tWTA) dynamics. Together when the striatum is driven by correlated inputs, FSis decorrelate the striatal responses but when input is uncorrelated FSIs correlate the striatal responses. That is, given their small relative population size FSIs create a sort of correlation attractor. These results highlight how a small neural population can have a large role in shaping the stimulus response properties and affect the behavior. Finally I will discuss the implication of these fdinsing for the aberrant dynamics in Parkinson’s disease.
