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Critical behavior in networks of real neurons
Published on Apr 29, 20143426 Views
The patterns of joint activity in a population of retinal ganglion cells encode the complete information about the visual world, and thus place limits on what could be learned about the environment by
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Chapter list
Critical behavior in networks of real neurons00:00
Visualization of a neural code02:49
Questions03:37
Overview05:38
Retina as an encoding device - 106:09
Retina as an encoding device - 207:29
Wait, isn’t the retina just a camera? - 107:57
Wait, isn’t the retina just a camera? - 210:06
Studying complete neural populations10:22
Watching the retinal output - 112:12
Watching the retinal output - 212:50
Watching the retinal output - 313:03
Watching the retinal output - 413:18
Neural codebook, dictionary, and vocabulary - 113:56
Neural codebook, dictionary, and vocabulary - 214:10
Neural codebook, dictionary, and vocabulary - 314:54
Neural codebook, dictionary, and vocabulary - 415:19
Maxent models for the neural vocabulary, P({σi})15:43
What are maxent models (and what they are not)? - 120:13
What are maxent models (and what they are not)? - 220:22
Pairwise models for small networks20:23
Models for > 100 neurons: pairwise - models24:04
Models for > 100 neurons: pairwise - models are no longer sufficient26:14
I: Predicting higher-order statistics28:17
II: Energy landscape - 129:27
II: Energy landscape - 232:21
III: Scaling of entropy34:13
IV: Correlations and error correction35:45
Thermodynamic behavior of the vocabulary P(σ)39:10
Signatures of criticality: density of states41:22
Signatures of criticality: heat capacity - 144:59
Signatures of criticality: heat capacity - 246:51
Signatures of criticality: correlation scaling - 147:06
Signatures of criticality: correlation scaling - 247:15
Critical codes, why and what for?47:19
Conclusions51:38