S. Raghavachari

dblp:59/6804 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2001
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Emerging computing paradigms · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Emerging computing paradigms › neuromorphic computing
neural coding
0.012001
3 state neurons for contextual processing · NIPS 2001
Emerging computing paradigms
neuromorphic computing
0.012001
3 state neurons for contextual processing · NIPS 2001
Bioinformatics and computational biology
computational neuroscience
0.012001
3 state neurons for contextual processing · NIPS 2001

Methods — techniques the papers use, named apart from their topics

membrane potential modeling · 0.1bistability analysis · 0.1
YearPublicationVenuePosition
2001 3 state neurons for contextual processing
abstract
Neurons receive excitatory inputs via both fast AMPA and slow NMDA type receptors. We find that neurons receiving input via NMDA receptors can have two stable membrane states which are input dependent. Action potentials can only be initiated from the higher voltage state. Similar observations have been made in sev(cid:173) eral brain areas which might be explained by our model. The in(cid:173) teractions between the two kinds of inputs lead us to suggest that some neurons may operate in 3 states: disabled, enabled and fir(cid:173) ing. Such enabled, but non-firing modes can be used to introduce context-dependent processing in neural networks. We provide a simple example and discuss possible implications for neuronal pro(cid:173) cessing and response variability.
Ádám Kepecs, S. Raghavachari
NIPS2