@article {46305,
	title = {Mean-field analysis of selective persistent activity in presence of short-term synaptic depression.},
	journal = {Journal of Computational Neuroscience},
	volume = {20},
	year = {2006},
	month = {2006 Apr},
	pages = {201-17},
	abstract = {<p>Mean-Field theory is extended to recurrent networks of spiking neurons endowed with short-term depression (STD) of synaptic transmission. The extension involves the use of the distribution of interspike intervals of an integrate-and-fire neuron receiving a Gaussian current, with a given mean and variance, in input. This, in turn, is used to obtain an accurate estimate of the resulting postsynaptic current in presence of STD. The stationary states of the network are obtained requiring self-consistency for the currents-those driving the emission processes and those generated by the emitted spikes. The model network stores in the distribution of two-state efficacies of excitatory-to-excitatory synapses, a randomly composed set of external stimuli. The resulting synaptic structure allows the network to exhibit selective persistent activity for each stimulus in the set. Theory predicts the onset of selective persistent, or working memory (WM) activity upon varying the constitutive parameters (e.g. potentiated/depressed long-term efficacy ratio, parameters associated with STD), and provides the average emission rates in the various steady states. Theoretical estimates are in remarkably good agreement with data "recorded" in computer simulations of the microscopic model.</p>
},
	keywords = {Action Potentials, Algorithms, Animals, Cerebral Cortex, Excitatory Postsynaptic Potentials, Humans, Memory, Short-Term, Nerve Net, Neural Inhibition, Neural Networks (Computer), Neural Pathways, Neurons, Normal Distribution, Reaction Time, Synaptic Transmission, Time Factors},
	issn = {0929-5313},
	doi = {10.1007/s10827-006-6308-x},
	author = {Romani, Sandro and Amit, Daniel J and Mongillo, Gianluigi}
}
@article {46306,
	title = {Learning in realistic networks of spiking neurons and spike-driven plastic synapses.},
	journal = {European Journal of Neuroscience},
	volume = {21},
	year = {2005},
	month = {2005 Jun},
	pages = {3143-60},
	abstract = {<p>We have used simulations to study the learning dynamics of an autonomous, biologically realistic recurrent network of spiking neurons connected via plastic synapses, subjected to a stream of stimulus-delay trials, in which one of a set of stimuli is presented followed by a delay. Long-term plasticity, produced by the neural activity experienced during training, structures the network and endows it with active (working) memory, i.e. enhanced, selective delay activity for every stimulus in the training set. Short-term plasticity produces transient synaptic depression. Each stimulus used in training excites a selective subset of neurons in the network, and stimuli can share neurons (overlapping stimuli). Long-term plasticity dynamics are driven by presynaptic spikes and coincident postsynaptic depolarization; stability is ensured by a refresh mechanism. In the absence of stimulation, the acquired synaptic structure persists for a very long time. The dependence of long-term plasticity dynamics on the characteristics of the stimulus response (average emission rates, time course and synchronization), and on the single-cell emission statistics (coefficient of variation) is studied. The study clarifies the specific roles of short-term synaptic depression, NMDA receptors, stimulus representation overlaps, selective stimulation of inhibition, and spike asynchrony during stimulation. Patterns of network spiking activity before, during and after training reproduce most of the in vivo physiological observations in the literature.</p>
},
	keywords = {Action Potentials, Animals, Central Nervous System, Electric Stimulation, Humans, Memory, Short-Term, Nerve Net, Neural Inhibition, Neural Networks (Computer), Neural Pathways, Neuronal Plasticity, Neurons, Receptors, N-Methyl-D-Aspartate, Synapses, Synaptic Transmission},
	issn = {0953-816X},
	doi = {10.1111/j.1460-9568.2005.04087.x},
	author = {Mongillo, Gianluigi and Curti, Emanuele and Romani, Sandro and Amit, Daniel J}
}
