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Empirical models of spiking in neural populations
Published on Jan 25, 20123645 Views
Neurons in the neocortex code and compute as part of a locally interconnected population. Large-scale multi-electrode recording makes it possible to access these population processes empirically by fi
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Chapter list
Empirical models of spiking in neural populations00:00
Characterizing neural function - 100:17
Characterizing neural function - 201:24
Models of population activity: direct couplings or latent variables? - 102:27
Models of population activity: direct couplings or latent variables? - 203:54
Statistical models offer insights into neural computation - 104:17
Statistical models offer insights into neural computation - 204:39
Statistical models offer insights into neural computation - 304:52
Data: Multi-electrode recordings from primate cortex05:19
Model class I: Generalized linear models (GLMs) - 105:48
Model class I: Generalized linear models (GLMs) - 206:30
Model class I: Generalized linear models (GLMs) - 306:39
Model class I: Generalized linear models (GLMs) - 406:52
Model class II: Latent dynamical system (DS) models - 107:11
Model class II: Latent dynamical system (DS) models - 207:38
Model class II: Latent dynamical system (DS) models - 307:41
Model class II: Latent dynamical system (DS) models - 408:21
Measuring performance: predicting neural firing from the population08:43
Cross-prediction: Dynamical systems outperform GLMs09:42
Cross-prediction: Poisson observations improve performance10:30
Matching statistics of the data I: temporal cross-correlations - 110:46
Matching statistics of the data I: temporal cross-correlations - 213:06
Matching statistics of the data II: population spike counts13:34
How much variance of the data can we explain using cross-prediction? - 114:30
How much variance of the data can we explain using cross-prediction? - 215:30
How much variance of the data can we explain using cross-prediction? - 315:51
Summary & conclusions - 116:39
Summary & conclusions - 217:04
Summary & conclusions - 317:54