Name
Abstract | ||
|---|---|---|
During wakefulness and deep sleep brain states, cortical neural networks show a different behavior, with the second characterized by transients of high network activity. To investigate their impact on neuronal behavior, we apply a pairwise Ising model analysis by inferring the maximum entropy model that reproduces single and pairwise moments of the neuron's spiking activity. In this work we first review the inference algorithm introduced in Ferrari, Phys. Rev. E (2016) [1]. We then succeed in applying the algorithm to infer the model from a large ensemble of neurons recorded by multi-electrode array in human temporal cortex. We compare the Ising model performance in capturing the statistical properties of the network activity during wakefulness and deep sleep. For the latter, the pairwise model misses relevant transients of high network activity, suggesting that additional constraints are necessary to accurately model the data. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-68445-1_30 | GEOMETRIC SCIENCE OF INFORMATION, GSI 2017 |
Keywords | Field | DocType |
Ising model, Maximum entropy principle, Natural gradient, Human temporal cortex, Multielectrode array recording, Brain states | Pairwise comparison,Inference,Wakefulness,Ising model,Artificial intelligence,Principle of maximum entropy,Neuron,Artificial neural network,Slow-wave sleep,Mathematics,Machine learning | Conference |
Volume | ISSN | Citations |
10589 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Trang-Anh Nghiem | 1 | 0 | 0.34 |
| Olivier Marre | 2 | 27 | 5.03 |
| Alain Destexhe | 3 | 754 | 109.26 |
| Ulisse Ferrari | 4 | 0 | 1.35 |