Abstract Number:
3887
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Azar Ghahari (1)
Institutions:
(1) N/A, N/A
First Author:
Presenting Author:
Abstract Text:
Historically, neural decoding is based on spiking models for individual neurons, which require
a spike sorting preprocessing step. In the last decade, there have been major advances in
decoding from marked point process models that describe the joint activity of many neurons simultaneously, without the need for spike sorting. These methods provide substantial
improvement in decoding accuracy, but the reason has never been clearly identified. In this
study, we examine entropy-based metrics to analyze the information that is extracted from
each observed spike. We compared the entropy reduction between spike sorted and cluster�less models both for individual spikes observed in isolation and when the prior information
from all previously observed spikes is accounted for. We demonstrate the application of these
methods to the problem of decoding a rat's movement from hippocampal spiking activity.
Our analysis demonstrates that low amplitude spikes, which are difficult to cluster and often
left out of spike sorting, provide reduced information compared to sortable, high-amplitude
spikes when considered in isolation, but the two provide similar levels of information when
consi
Keywords:
Neural decoding|Marked point process models|Conditional entropy|Information measures| |
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