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.
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Notes
- 1.
The results of this section are grounded on the repeated use of central limit theorem. See [1] for more detail.
References
Ferrari, U.: Learning maximum entropy models from finite-size data sets: a fast data-driven algorithm allows sampling from the posterior distribution. Phys. Rev. E 94, 023301 (2016)
Stevenson, I.H., Kording, K.P.: How advances in neural recording affect data analysis. Nat. Neurosci. 14(2), 139–142 (2011)
Schneidman, E., Berry, M., Segev, R., Bialek, W.: Weak pairwise correlations imply strongly correlated network states in a population. Nature 440, 1007 (2006)
Peyrache, A., Dehghani, N., Eskandar, E.N., Madsen, J.R., Anderson, W.S., Donoghue, J.A., Hochberg, L.R., Halgren, E., Cash, S.S., Destexhe, A.: Spatiotemporal dynamics of neocortical excitation and inhibition during human sleep. Proc. Nat. Acad. Sci. 109(5), 1731–1736 (2012)
Hamilton, L.S., Sohl-Dickstein, J., Huth, A.G., Carels, V.M., Deisseroth, K., Bao, S.: Optogenetic activation of an inhibitory network enhances feedforward functional connectivity in auditory cortex. Neuron 80, 1066–1076 (2013)
Tkacik, G., Marre, O., Amodei, D., Schneidman, E., Bialek, W., Berry, M.J.: Searching for collective behaviour in a network of real neurons. PloS Comput. Biol. 10(1), e1003408 (2014)
Dehghani, N., Peyrache, A., Telenczuk, B., Le Van Quyen, M., Halgren, E., Cash, S.S., Hatsopoulos, N.G., Destexhe, A.: Dynamic balance of excitation and inhibition in human and monkey neocortex. Sci. Rep. 6 (2016). Article no: 23176. doi:10.1038/srep23176
Gardella, C., Marre, O., Mora, T.: A tractable method for describing complex couplings between neurons and population rate. Eneuro 3(4), 0160 (2016)
Tavoni, G., Ferrari, U., Cocco, S., Battaglia, F.P., Monasson, R.: Functional coupling networks inferred from prefrontal cortex activity show experience-related effective plasticity. Netw. Neurosci. 0(0), 1–27 (2017). doi:10.1162/NETN_a_00014
Jaynes, E.T.: On The rationale of maximum-entropy method. Proc. IEEE 70, 939 (1982)
Ferrari, U., Obuchi, T., Mora, T.: Random versus maximum entropy models of neural population activity. Phys. Rev. E 95, 042321 (2017)
Ackley, D.H., Hinton, G.E., Sejnowski, T.J.: A learning algorithm for Boltzmann machines. Cogn. Sci. 9, 147–169 (1985)
Broderick, T., Dudik, M., Tkacik, G., Schapire, R.E., Bialek, W.: Faster solutions to the inverse pairwise Ising problem. arXiv:0712.2437 (2007)
Cocco, S., Monasson, R.: Adaptive cluster expansion for inferring Boltzmann machines with noisy data. Phys. Rev. Lett. 106, 090601 (2011)
Sohl-Dickstein, J., Battaglino, P.B., DeWeese, M.R.: New method for parameter estimation in probabilistic models: minimum probability flow. Phys. Rev. Lett. 107, 220601 (2011)
Renart, A., De La Rocha, J., Bartho, P., Hollender, L., Parga, N., Reyes, A., Harris, K.D.: The asynchronous state in cortical circuits. Science 327(5965), 587–590 (2010)
Amari, S.: Natural gradient works efficiently in learning. Neural Comput. 10, 251–276 (1998)
Amari, S., Douglas, S.C.: Why natural gradient? Proc. IEEE 2, 1213–1216 (1998)
Le Van Quyen, M., Muller, L.E., Telenczuk, B., Halgren, E., Cash, S., Hatsopoulos, N.G., Dehghani, N., Destexhe, A.: High-frequency oscillations in human and monkey neocortex during the wake-sleep cycle. Proc. Nat. Acad. Sci. 113(33), 9363–93680 (2016)
Acknowledgments
We thank B. Telenczuk, G. Tkacik and M. Di Volo for useful discussion. Research funded by European Community (Human Brain Project, H2020-720270), ANR TRAJECTORY, ANR OPTIMA, French State program Investissements dAvenir managed by the Agence Nationale de la Recherche [LIFESENSES: ANR-10-LABX-65] and NIH grant U01NS09050.
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Nghiem, TA., Marre, O., Destexhe, A., Ferrari, U. (2017). Pairwise Ising Model Analysis of Human Cortical Neuron Recordings. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2017. Lecture Notes in Computer Science(), vol 10589. Springer, Cham. https://doi.org/10.1007/978-3-319-68445-1_30
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