Frederic Henry and Emmanuel Dauce
SPIKE-TIMING DEPENDENT PLASTICITY AND REGIME TRANSITIONS IN RANDOM RECURRENT NEURAL NETWORKS
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In feedforward networks, STDP is found to reduce the latency of a neuron's response to a given input [4, 5].

     [4] L.F. Abbott Sen Song, Kenneth D. Miller. "Competitive hebbian learning through spike-timing dependent synaptic plasticity." Nature, 2000. => done

     [5] Rudy Guyonneau, Rudy VanRullen, and Simon Thorpe "Neurons tune to the earliest spikes through stdp." Neural Computation, 17:559.879, 2005. => done

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According to [6, 7], the dynamical regimes of random recurrent networks of spiking neurons can be classified in four categories (synchronous/asynchronous, regular/irregular), depending on the initial network parameters (balance between excitation and inhibition, gain of the cells responses...)

     [6] N. Brunel "Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons." Journal of Computational Neuroscience, 8:183.208, 2000. => done

     [7] E. Dauce, O. Moynot, O. Pinaud, and M. Samuelides. "Mean-field theory and synchronization in random recurrent neural networks." Neural Processing Letters, 14:115.126, 2001. => NG