A recurrent network of 21 linear integrate-and-fire (LIF) neurons (14 excitatory; 7 inhibitory) connected by 60 spike-driven, excitatory, plastic synapses and 35 inhibitory synapses are implemented in analog VLSI. The connectivity pattern is random and at a level of 30%. The synaptic efficacies have two stable values as long-term memory. Each neuron receives also an external afferent current. We present “neuro-physiological” recordings of the collective characteristics of the network at frozen synaptic efficacies. Examining spike rasters, we show that in absence of synaptic couplings and for constant external currents, the neuron spike in a regular fashion. Keeping the excitatory part of the network isolated, as the strength of the synapses rises, the neuronal spiking becomes increasingly irregular, as expressed in coefficient of variability (CV) of inter-spike intervals (ISI). The rates are high, in absence of inhibition and are well described by mean-field theory. Inhibition is then turned on, the rates decrease; variability remains and population activity fluctuations appear, as predicted by mean-field theory. We conclude that the collective behavior of the pilot network produces distributed noise expressed in the ISI distribution, as would be required to control slow stochastic learning, and that the random connectivity acts to make the dynamics of the network noisy even in the absence of noise in the external afferents.