MH093338/MH/NIMH NIH HHS/United States[Grants and Funding] - Search Results

Year	Number of Results
2011	1
2012	2
2013	4
2014	6
2015	1
2016	5
2017	1
2018	3
2023	1
2024	0

1

Building functional networks of spiking model neurons.

Abbott LF, DePasquale B, Memmesheimer RM. Abbott LF, et al. Nat Neurosci. 2016 Mar;19(3):350-5. doi: 10.1038/nn.4241. Nat Neurosci. 2016. PMID: 26906501 Free PMC article. Review.

2

From fixed points to chaos: three models of delayed discrimination.

Barak O, Sussillo D, Romo R, Tsodyks M, Abbott LF. Barak O, et al. Prog Neurobiol. 2013 Apr;103:214-22. doi: 10.1016/j.pneurobio.2013.02.002. Epub 2013 Feb 21. Prog Neurobiol. 2013. PMID: 23438479 Free PMC article. Review.

3

Balanced excitation and inhibition are required for high-capacity, noise-robust neuronal selectivity.

Rubin R, Abbott LF, Sompolinsky H. Rubin R, et al. Proc Natl Acad Sci U S A. 2017 Oct 31;114(44):E9366-E9375. doi: 10.1073/pnas.1705841114. Epub 2017 Oct 17. Proc Natl Acad Sci U S A. 2017. PMID: 29042519 Free PMC article.

4

Stability and Competition in Multi-spike Models of Spike-Timing Dependent Plasticity.

Babadi B, Abbott LF. Babadi B, et al. PLoS Comput Biol. 2016 Mar 3;12(3):e1004750. doi: 10.1371/journal.pcbi.1004750. eCollection 2016 Mar. PLoS Comput Biol. 2016. PMID: 26939080 Free PMC article.

5

A computational model of motor neuron degeneration.

Le Masson G, Przedborski S, Abbott LF. Le Masson G, et al. Neuron. 2014 Aug 20;83(4):975-88. doi: 10.1016/j.neuron.2014.07.001. Epub 2014 Jul 31. Neuron. 2014. PMID: 25088365 Free PMC article.

6

Pairwise analysis can account for network structures arising from spike-timing dependent plasticity.

Babadi B, Abbott LF. Babadi B, et al. PLoS Comput Biol. 2013;9(2):e1002906. doi: 10.1371/journal.pcbi.1002906. Epub 2013 Feb 21. PLoS Comput Biol. 2013. PMID: 23436986 Free PMC article.

7

Conceptual and technical advances define a key moment for theoretical neuroscience.

Churchland AK, Abbott LF. Churchland AK, et al. Nat Neurosci. 2016 Mar;19(3):348-9. doi: 10.1038/nn.4255. Nat Neurosci. 2016. PMID: 26906500 Free PMC article.

8

The centrality of population-level factors to network computation is demonstrated by a versatile approach for training spiking networks.

DePasquale B, Sussillo D, Abbott LF, Churchland MM. DePasquale B, et al. Neuron. 2023 Mar 1;111(5):631-649.e10. doi: 10.1016/j.neuron.2022.12.007. Epub 2023 Jan 10. Neuron. 2023. PMID: 36630961 Free PMC article.

9

Inferring single-trial neural population dynamics using sequential auto-encoders.

Pandarinath C, O'Shea DJ, Collins J, Jozefowicz R, Stavisky SD, Kao JC, Trautmann EM, Kaufman MT, Ryu SI, Hochberg LR, Henderson JM, Shenoy KV, Abbott LF, Sussillo D. Pandarinath C, et al. Nat Methods. 2018 Oct;15(10):805-815. doi: 10.1038/s41592-018-0109-9. Epub 2018 Sep 17. Nat Methods. 2018. PMID: 30224673 Free PMC article.

10

full-FORCE: A target-based method for training recurrent networks.

DePasquale B, Cueva CJ, Rajan K, Escola GS, Abbott LF. DePasquale B, et al. PLoS One. 2018 Feb 7;13(2):e0191527. doi: 10.1371/journal.pone.0191527. eCollection 2018. PLoS One. 2018. PMID: 29415041 Free PMC article.

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