  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). 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.
