Computation in Networks of Neurons in the Brain I

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" summary

Argues that networks of spiking neurons can simulate any Turing machine and implement Boltzmann-like models to perform sampling-based inference, but only under idealized neuron/EPSP assumptions; points out pervasive trial-to-trial variability and unreliable synapses (~50% release) as core constraints. Presents simulations where STDP produces unsupervised pattern detectors, shows hippocampal and orbitofrontal recordings consistent with temporal sampling (~100 ms episodes), and highlights that spiking samplers can find good constraint-satisfaction solutions faster than Boltzmann machines, leaving open how assemblies, heterogeneity, and state retention are realized biologically.

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" chapters (17)

1. 0:00Introduction
2. 2:15Spiking neuron computation
3. 4:24Computational power
4. 7:29The impact of noise
5. 10:20Synaptic unreliability
6. 13:40Revisiting unreliability
7. 18:05Stochastic computation
8. 21:07Boltzmann machines
9. 25:19Spiking and Boltzmann
10. 29:45Probabilistic learning
11. 32:17Learning algorithms
12. 35:36Unsupervised learning
13. 37:53Constraint satisfaction
14. 43:35Experimental evidence
15. 50:20Efficiency of sampling
16. 53:25Future directions
17. 58:39Q&A and conclusion