VLDB 2026 Research / reviewers in the wild / expert
S. Raghavachari
dblp:59/6804
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Emerging computing paradigms › neuromorphic computing
neural coding |
0.0 | 1 | 2001 | 3 state neurons for contextual processing · NIPS 2001 |
Emerging computing paradigms
neuromorphic computing |
0.0 | 1 | 2001 | 3 state neurons for contextual processing · NIPS 2001 |
Bioinformatics and computational biology
computational neuroscience |
0.0 | 1 | 2001 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2001 | 3 state neurons for contextual processingabstractNeurons 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 |
NIPS | 2 |