Extensive simulations of large recurrent networks of integrate-and-fir
e excitatory and inhibitory neurons in realistic cortical conditions (
before and after Hebbian unsupervised learning of uncorrelated stimuli
) exhibit a rich phenomenology of stochastic neural spike dynamics and
, in particular, coexistence between two types of stable states: spont
aneous activity upon stimulation by an unlearned stimulus, and 'workin
g memory' states strongly correlated with learned stimuli. Firing rate
s have very wide distributions, due to the variability in the connecti
vity from neuron to neuron. ISI histograms are exponential, except for
small intervals. Thus the spike emission processes are well approxima
ted by a Poisson process. The variability of the spike emission proces
s is effectively controlled by the magnitude of the post-spike reset p
otential relative to the mean depolarization of the cell. Cross-correl
ations (CC) exhibit a central peak near zero delay, flanked by damped
oscillations. The magnitude of the central peak in the CCs depends bot
h on the probability that a spike emitted by a neuron affects another
randomly chosen neuron and on firing rates. It increases when average
rates decrease. Individual CCs depend very weakly on the synaptic inte
ractions between the pairs of neurons. The dependence of individual CC
s on the rates of the pair of neurons is in agreement with experimenta
l data. The distribution of firing rates among neurons is in very good
agreement with a simple theory, indicating that correlations between
spike emission processes in the network are effectively small.