to any of the stimuli learned have rates which gradually increase with the amplitude of synaptic potentiation. b. When the average LTP increases beyond a critical value, specific local attractors appear abruptly against the background of the global uniform spontaneous attractor. This happens with either gradual or discrete stochastic LTP. 4. The above findings predict that in the process of learning unfamiliar stimuli, there is a stage in which all neurons selective to any of the learned stimuli enhance their spontaneous activity relative to the rest. Then, abruptly, selective delay activity appear. Both facts could be observed in single unit recordings in delayed match to sample experiments. 5. Beyond this critical learning strength the local module has two types of collective activity. It either participates in the global spontaneous activity, or it maintains a stimulus selective elevated activity distribution. The particular mode of behavior depends on the stimulus: if it is unfa

this paper I present a network model of spiking neurons in which synapses are endowed with realistic gating kinetics, based on experimentally measured dynamical properties of cortical synapses. I will focus on how delay-period activity could be generated by neuronally plausible mechanisms; the issue of memory field formation will be addressed in a separate study. A main problem to be investigated is that of "rate control" for a persistent state: if a robust persistent activity necessitates strong recurrent excitatory connections, how can the network be prevented from runaway excitation in spite of the powerful positive feedback, so that neuronal firing rates are low and comparable to those of PFC cells (10 --50 Hz)? Moreover, a persistent state may be destabilized because of network dynamics. For example, fast recurrent excitation followed by a slower negative feedback may lead to network instability and a collapse of the persistent state. It is shown that persistent states at low firing rates are usually stable only in the presence of sufficiently slow excitatory synapses of the NMDA type. Functional implications of these results for the role of Received April 14, 1999; revised Aug. 12, 1999; accepted Aug. 12, 1999

Abstract not found

Cortical neurons are typically driven by thousands of synaptic inputs. The arrival of a spike from one input may or may not be correlated with the arrival of other spikes from different inputs. How does this interdependence alter the probability that the postsynaptic neuron will fire? We constructed a simple random walk model in which the membrane potential of a target neuron fluctuates stochastically, driven by excitatory and inhibitory spikes arriving at random times. An analytic expression was derived for the mean output firing rate as a function of the firing rates and pairwise correlations of the inputs. This stochastic model made three quantitative predictions. (1) Correlations between pairs of excitatory or inhibitory inputs increase the fluctuations in synaptic drive, whereas correlations between excitatory–inhibitory pairs decrease them. (2) When excitation and inhibition are fully balanced (the mean net synaptic drive is zero),

Although spike-frequency adaptation is a commonly observed property of neurons, its functional implications are still poorly understood. In this work, using a leaky integrate-and-fire neural model that includes a -activated K + current (I AHP ), we develop a quantitative theory of adaptation temporal dynamics and compare our results with recent in vivo intracellular recordings from pyramidal cells in the cat visual cortex. Experimentally testable relations between the degree and the time constant of spike-frequency adaptation are predicted. We also contrast the I AHP model with an alternative adaptation model based on a dynamical firing threshold. Possible roles of adaptation in temporal computation are explored, as a a time-delayed neuronal self-inhibition mechanism. Our results include the following: (1) given the same firing rate, the variability of interspike intervals (ISIs) is either reduced or enhanced by adaptation, depending on whether the I AHP dynamics is fast or slow compared with the mean ISI in the output spike train; (2) when the inputs are Poisson-distributed (uncorrelated), adaptation generates temporal anticorrelation between ISIs, we suggest that measurement of this negative correlation provides a probe to assess the strength of I AHP in vivo; (3) the forward masking effect produced by the slow dynamics of I AHP is nonlinear and effective at selecting the strongest input among competing sources of input signals.

Persistent neuronal activity is widespread in many areas of the cerebral cortex of monkeys performing cognitive tasks with a working memory component. Modeling studies have helped understanding of the conditions under which persistent activity can be sustained in cortical circuits. Here, we first review several basic models of persistent activity, including bistable models with excitation only and multistable models for working memory of a discrete set of pictures or objects with structured excitation and global inhibition. In many experiments, persistent activity has been shown to be subject to changes due to associative learning. In cortical network models, Hebbian learning shapes the synaptic structure and, in turn, the properties of persistent activity when pictures are associated together in the course of a task. It is shown how the theoretical models can reproduce basic experimental findings of neurophysiological recordings from inferior temporal and perirhinal cortices obtained using the following experimental protocols: (i) the pairassociate task; (ii) the pair-associate task with color switch; and (iii) the delay match to sample task with a fixed sequence of samples.

ng various types of collective dynamics. And systems in between, which mix more complex ionic, neuro-transmitter and neural structure with large scale features[7]. Clearly, any such simulation implies a model. One could "run" the simulation and observe the behaviour of the system under consideration. But this is more like an experimental situation than like a theoretical one. If the simulated system is complex enough, generic statements about its product dynamics would be almost as gratifying and surprising as about an experiment. Moreover, to monitor the system's progress one would have to define and sharpen tools, much like in the experimental situation, since the system generates an enormous amount of noisy data. Thus it appears that the simulation hangs somewhere between the theoretical and the experimental. The matter may be sharpened further: One may be given the design of a part of the brain, consisting of operational features of the elements: neurons, synapses etc., as well as<

Unreliability is a ubiquitous feature of synaptic transmission in the brain. The information conveyed in the discharges of an ensemble of cells (e.g., in the spike count or in the timing of synchronous events) may not be faithfully transmitted to the postsynaptic cell because a large fraction of the spikes fail to elicit a synaptic response. In addition, short-term depression increases the failure rate with the presynaptic activity. We use a simple neuron model with stochastic depressing synapses to understand the transformations undergone by the spatiotemporal patterns of incoming spikes as these are first converted into synaptic current and afterward into the cell response. We analyze the mean and SD of the current produced by different stimuli with spatiotemporal correlations. We find that the mean, which carries information only about the spike count, rapidly saturates as the input rate increases. In contrast, the current deviation carries information about the correlations. If the afferent action potentials are uncorrelated, it saturates monotonically, whereas if they are correlated it increases, reaches a maximum, and then decreases to the value produced by the uncorrelated stimulus. This means that, at high input rates, depression erases from the synaptic current any trace of the spatiotemporal structure of the input. The non-monotonic behavior of the deviation can be inherited by the response rate provided that the mean current saturates below the current threshold setting the cell in the fluctuation-driven regimen. Afferent correlations therefore enable the modulation of the response beyond the saturation of the mean current. Key words: synaptic integration; fluctuation-driven regimen; presynaptic spike correlations; synaptic short-term depression; vesicle depletion; neural coding

We present a new technique for efficiently simulating large-scale networks of synaptically interacting integrate-and-fire (IF) model neurons, based on a proposed event-driven strategy [7]. By means of the analytical evaluation of the statistical properties of single model neurons under random current injection, we show how it is possible to effectively implement the global background activity provided by a large external population, which constitutes the input to the network to be simulated.