Estimating a Separably Markov Random Field from Binary Observations

A fundamental problem in neuroscience is to characterize the dynamics of spiking from the neurons in a circuit that is involved in learning about a stimulus or a contingency. A key limitation of current methods to analyze neural spiking data is the need to collapse neural activity over time or trial...

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Veröffentlicht in:	Neural computation 2018-04, Vol.30 (4), p.1046-1079
Hauptverfasser:	Zhang, Yingzhuo, Malem-Shinitski, Noa, Allsop, Stephen A., M. Tye, Kay, Ba, Demba
Format:	Artikel
Sprache:	eng
Schlagworte:	Conditioning Contingency Data analysis Fields (mathematics) Learning Letters Markov processes Neural networks Neurons Parameter estimation Software Spiking State space models Two dimensional models
Online-Zugang:	Volltext
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