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Items: 16

1.

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.

Nat Methods. 2018 Oct;15(10):805-815. doi: 10.1038/s41592-018-0109-9. Epub 2018 Sep 17.

2.

Computation through Cortical Dynamics.

Driscoll LN, Golub MD, Sussillo D.

Neuron. 2018 Jun 6;98(5):873-875. doi: 10.1016/j.neuron.2018.05.029.

3.

Corrigendum: Making brain-machine interfaces robust to future neural variability.

Sussillo D, Stavisky SD, Kao JC, Ryu SI, Shenoy KV.

Nat Commun. 2017 Jan 20;8:14490. doi: 10.1038/ncomms14490. No abstract available.

4.

Making brain-machine interfaces robust to future neural variability.

Sussillo D, Stavisky SD, Kao JC, Ryu SI, Shenoy KV.

Nat Commun. 2016 Dec 13;7:13749. doi: 10.1038/ncomms13749.

5.

The Largest Response Component in the Motor Cortex Reflects Movement Timing but Not Movement Type.

Kaufman MT, Seely JS, Sussillo D, Ryu SI, Shenoy KV, Churchland MM.

eNeuro. 2016 Aug 30;3(4). pii: ENEURO.0085-16.2016. eCollection 2016 Jul-Aug.

6.

A neural network that finds a naturalistic solution for the production of muscle activity.

Sussillo D, Churchland MM, Kaufman MT, Shenoy KV.

Nat Neurosci. 2015 Jul;18(7):1025-33. doi: 10.1038/nn.4042. Epub 2015 Jun 15.

7.

Neural circuits as computational dynamical systems.

Sussillo D.

Curr Opin Neurobiol. 2014 Apr;25:156-63. doi: 10.1016/j.conb.2014.01.008. Epub 2014 Feb 5. Review.

PMID:
24509098
8.

Context-dependent computation by recurrent dynamics in prefrontal cortex.

Mante V, Sussillo D, Shenoy KV, Newsome WT.

Nature. 2013 Nov 7;503(7474):78-84. doi: 10.1038/nature12742.

9.

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

Barak O, Sussillo D, Romo R, Tsodyks M, Abbott LF.

Prog Neurobiol. 2013 Apr;103:214-22. doi: 10.1016/j.pneurobio.2013.02.002. Epub 2013 Feb 21. Review.

10.

Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks.

Sussillo D, Barak O.

Neural Comput. 2013 Mar;25(3):626-49. doi: 10.1162/NECO_a_00409. Epub 2012 Dec 28.

PMID:
23272922
11.

Transferring learning from external to internal weights in echo-state networks with sparse connectivity.

Sussillo D, Abbott LF.

PLoS One. 2012;7(5):e37372. doi: 10.1371/journal.pone.0037372. Epub 2012 May 24.

12.

A recurrent neural network for closed-loop intracortical brain-machine interface decoders.

Sussillo D, Nuyujukian P, Fan JM, Kao JC, Stavisky SD, Ryu S, Shenoy K.

J Neural Eng. 2012 Apr;9(2):026027. doi: 10.1088/1741-2560/9/2/026027. Epub 2012 Mar 19.

13.

Generating coherent patterns of activity from chaotic neural networks.

Sussillo D, Abbott LF.

Neuron. 2009 Aug 27;63(4):544-57. doi: 10.1016/j.neuron.2009.07.018.

14.

Self-tuning of neural circuits through short-term synaptic plasticity.

Sussillo D, Toyoizumi T, Maass W.

J Neurophysiol. 2007 Jun;97(6):4079-95. Epub 2007 Apr 4.

15.

Feedforward inhibition contributes to the control of epileptiform propagation speed.

Trevelyan AJ, Sussillo D, Yuste R.

J Neurosci. 2007 Mar 28;27(13):3383-7.

16.

Modular propagation of epileptiform activity: evidence for an inhibitory veto in neocortex.

Trevelyan AJ, Sussillo D, Watson BO, Yuste R.

J Neurosci. 2006 Nov 29;26(48):12447-55.

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