@article {46302, title = {Optimizing one-shot learning with binary synapses.}, journal = {Neural Computation}, volume = {20}, year = {2008}, month = {2008 Aug}, pages = {1928-50}, abstract = {

A network of excitatory synapses trained with a conservative version of Hebbian learning is used as a model for recognizing the familiarity of thousands of once-seen stimuli from those never seen before. Such networks were initially proposed for modeling memory retrieval (selective delay activity). We show that the same framework allows the incorporation of both familiarity recognition and memory retrieval, and estimate the network{\textquoteright}s capacity. In the case of binary neurons, we extend the analysis of Amit and Fusi (1994) to obtain capacity limits based on computations of signal-to-noise ratio of the field difference between selective and non-selective neurons of learned signals. We show that with fast learning (potentiation probability approximately 1), the most recently learned patterns can be retrieved in working memory (selective delay activity). A much higher number of once-seen learned patterns elicit a realistic familiarity signal in the presence of an external field. With potentiation probability much less than 1 (slow learning), memory retrieval disappears, whereas familiarity recognition capacity is maintained at a similarly high level. This analysis is corroborated in simulations. For analog neurons, where such analysis is more difficult, we simplify the capacity analysis by studying the excess number of potentiated synapses above the steady-state distribution. In this framework, we derive the optimal constraint between potentiation and depression probabilities that maximizes the capacity.

}, keywords = {Algorithms, Brain, Computer Simulation, Learning, Models, Statistical, Neural Networks (Computer), Neurons, Pattern Recognition, Automated, Synapses, Synaptic Transmission}, issn = {0899-7667}, doi = {10.1162/neco.2008.10-07-618}, author = {Romani, Sandro and Amit, Daniel J and Amit, Yali} } @article {46303, title = {Universal memory mechanism for familiarity recognition and identification.}, journal = {The Journal of Neuroscience : The Official Journal of the Society for Neuroscience}, volume = {28}, year = {2008}, month = {2008 Jan 2}, pages = {239-48}, abstract = {

Macaque monkeys were tested on a delayed-match-to-multiple-sample task, with either a limited set of well trained images (in randomized sequence) or with never-before-seen images. They performed much better with novel images. False positives were mostly limited to catch-trial image repetitions from the preceding trial. This result implies extremely effective one-shot learning, resembling Standing{\textquoteright}s finding that people detect familiarity for 10,000 once-seen pictures (with 80\% accuracy) (Standing, 1973). Familiarity memory may differ essentially from identification, which embeds and generates contextual information. When encountering another person, we can say immediately whether his or her face is familiar. However, it may be difficult for us to identify the same person. To accompany the psychophysical findings, we present a generic neural network model reproducing these behaviors, based on the same conservative Hebbian synaptic plasticity that generates delay activity identification memory. Familiarity becomes the first step toward establishing identification. Adding an inter-trial reset mechanism limits false positives for previous-trial images. The model, unlike previous proposals, relates repetition-recognition with enhanced neural activity, as recently observed experimentally in 92\% of differential cells in prefrontal cortex, an area directly involved in familiarity recognition. There may be an essential functional difference between enhanced responses to novel versus to familiar images: The maximal signal from temporal cortex is for novel stimuli, facilitating additional sensory processing of newly acquired stimuli. The maximal signal for familiar stimuli arising in prefrontal cortex facilitates the formation of selective delay activity, as well as additional consolidation of the memory of the image in an upstream cortical module.

}, keywords = {Animals, Behavior, Animal, Computer Simulation, Discrimination (Psychology), Haplorhini, Identification (Psychology), Models, Neurological, Neural Networks (Computer), Photic Stimulation, Recognition (Psychology), ROC Curve, Task Performance and Analysis}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.4799-07.2008}, author = {Yakovlev, Volodya and Amit, Daniel J and Romani, Sandro and Hochstein, Shaul} } @article {46304, title = {Search for fMRI BOLD signals in networks of spiking neurons.}, journal = {European Journal of Neuroscience}, volume = {25}, year = {2007}, month = {2007 Mar}, pages = {1882-92}, abstract = {

In a recent experiment, functional magnetic resonance imaging blood oxygen level-dependent (fMRI BOLD) signals were compared in different cortical areas (primary-visual and associative), when subjects were required covertly to name images in two protocols: sequences of images, with and without intervening delays. The amplitude of the BOLD signal in protocols with delay was found to be closer to that without delays in associative areas than in primary areas. The present study provides an exploratory proposal for the identification of the neural activity substrate of the BOLD signal in quasi-realistic networks of spiking neurons, in networks sustaining selective delay activity (associative) and in networks responsive to stimuli, but whose unique stationary state is one of spontaneous activity (primary). A variety of observables are {\textquoteright}recorded{\textquoteright} in the network simulations, applying the experimental stimulation protocol. The ratios of the candidate BOLD signals, in the two protocols, are compared in networks with and without delay activity. There are several options for recovering the experimental result in the model networks. One common conclusion is that the distinguishing factor is the presence of delay activity. The effect of NMDAr is marginal. The ultimate quantitative agreement with the experiment results depends on a distinction of the baseline signal level from its value in delay-period spontaneous activity. This may be attributable to the subjects{\textquoteright} attention. Modifying the baseline results in a quantitative agreement for the ratios, and provided a definite choice of the candidate signals. The proposed framework produces predictions for the BOLD signal in fMRI experiments, upon modification of the protocol presentation rate and the form of the response function.

}, keywords = {Action Potentials, Brain, Brain Mapping, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Models, Neurological, Nerve Net, Neurons, Oxygen, Synapses}, issn = {0953-816X}, doi = {10.1111/j.1460-9568.2007.05408.x}, author = {Amit, Daniel J and Romani, Sandro} } @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 = {

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.

}, 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 = {

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.

}, 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} }