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AbstractöTo investigate the basis of the £uctuating activity present in neocortical neurons in vivo, we have combined computational models with whole-cell recordings using the dynamic-clamp technique. A simpli¢ed`point-conductance' model was used to represent the currents generated by thousands of stochastically releasing synapses. Synaptic activity was represented by two independent fast glutamatergic and GABAergic conductances described by stochastic randomwalk processes. An advantage of this approach is that all the model parameters can be determined from voltage-clamp experiments. We show that the point-conductance model captures the amplitude and spectral characteristics of the synaptic conductances during background activity. To determine if it can recreate in vivo-like activity, we injected this point-conductance model into a single-compartment model, or in rat prefrontal cortical neurons in vitro using dynamic clamp. This procedure successfully recreated several properties of neurons intracellularly recorded in vivo, such as a depolarized membrane potential, the presence of high-amplitude membrane potential £uctuations, a low-input resistance and irregular spontaneous ¢ring activity. In addition, the point-conductance model could simulate the enhancement of responsiveness due to background activity.We conclude that many of the characteristics of cortical neurons in vivo can be explained by fast glutamatergic and GABAergic conductances varying stochastically. ß 2001 IBRO. Published by Elsevier Science Ltd. All rights reserved.Key words: computational models, pyramidal neurons, dynamic clamp, synaptic bombardment, high-conductance states, CV.Synaptic background activity is invariably present in intracellular recordings of neocortical neurons in vivo, and modeling studies have suggested that it may have important consequences on the integrative properties of these neurons (Barrett, 1975;Holmes and Woody, 1989;Bernander et al., 1991;Destexhe and Parë, 1999). Background activity is maximal during the active states of the brain, when cortical neurons ¢re spontaneously at relatively high rates (5^20 Hz in awake animals; see Hubel, 1959;Evarts, 1964;Steriade, 1978;Matsumura et al., 1988;Steriade et al., 2001). This highly £uctuating activity was simulated in vitro by injecting noisy current waveforms (Mainen and Sejnowski, 1995;Stevens and Zador, 1998;Fellous et al., 2001). This approach, however, does not take into account the conductance due to background activity.Given that the neocortex is characterized by a very dense synaptic connectivity (5000^60 000 excitatory synapses per neuron; Cragg, 1967; DeFelipe and Farin ¬ as, 1992), these cells could potentially experience considerable amounts of synaptic conductances during periods of intense network activity. A recent estimation of the electrophysiological parameters of background activity in cat parietal cortex in vivo (Parë et al., 1998) provided evidence for a`high-conductance' state (see also Borg-Graham et al., 1998). By combining intracellular recording...

Destexhe, Alain and Denis Paré. Impact of network activity on the integrative properties of neocortical pyramidal neurons in vivo. J. Neurophysiol. 81: 1531Neurophysiol. 81: -1547Neurophysiol. 81: , 1999. During wakefulness, neocortical neurons are subjected to an intense synaptic bombardment. To assess the consequences of this background activity for the integrative properties of pyramidal neurons, we constrained biophysical models with in vivo intracellular data obtained in anesthetized cats during periods of intense network activity similar to that observed in the waking state. In pyramidal cells of the parietal cortex (area 5-7), synaptic activity was responsible for an approximately fivefold decrease in input resistance (R in ), a more depolarized membrane potential (V m ), and a marked increase in the amplitude of V m fluctuations, as determined by comparing the same cells before and after microperfusion of tetrodotoxin (TTX). The model was constrained by measurements of R in , by the average value and standard deviation of the V m measured from epochs of intense synaptic activity recorded with KAc or KClfilled pipettes as well as the values measured in the same cells after TTX. To reproduce all experimental results, the simulated synaptic activity had to be of relatively high frequency (1-5 Hz) at excitatory and inhibitory synapses. In addition, synaptic inputs had to be significantly correlated (correlation coefficient ϳ0.1) to reproduce the amplitude of V m fluctuations recorded experimentally. The presence of voltage-dependent K ϩ currents, estimated from current-voltage relations after TTX, affected these parameters by Ͻ10%. The model predicts that the conductance due to synaptic activity is 7-30 times larger than the somatic leak conductance to be consistent with the approximately fivefold change in R in . The impact of this massive increase in conductance on dendritic attenuation was investigated for passive neurons and neurons with voltage-dependent Na ϩ /K ϩ currents in soma and dendrites. In passive neurons, correlated synaptic bombardment had a major influence on dendritic attenuation. The electrotonic attenuation of simulated synaptic inputs was enhanced greatly in the presence of synaptic bombardment, with distal synapses having minimal effects at the soma. Similarly, in the presence of dendritic voltage-dependent currents, the convergence of hundreds of synaptic inputs was required to evoke action potentials reliably. In this case, however, dendritic voltage-dependent currents minimized the variability due to input location, with distal apical synapses being as effective as synapses on basal dendrites. In conclusion, this combination of intracellular and computational data suggests that, during low-amplitude fast electroencephalographic activity, neocortical neurons are bombarded continuously by correlated synaptic inputs at high frequency, which significantly affect their integrative properties. A series of predictions are suggested to test this model.

We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-andfire models, interacting with current-based or conductance-based synapses, using clock-driven or NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.

The electroencephalogram displays various oscillation patterns during wake and sleep states, but their spatiotemporal distribution is not completely known. Local field potentials (LFPs) and multiunits were recorded simultaneously in the cerebral cortex (areas 5-7) of naturally sleeping and awake cats. Slowwave sleep (SWS) was characterized by oscillations in the slow (Ͻ1 Hz) and delta (1-4 Hz) frequency range. The high-amplitude slow-wave complexes consisted in a positivity of depth LFP, associated with neuronal silence, followed by a sharp LFP negativity, correlated with an increase of firing. This pattern was of remarkable spatiotemporal coherence, because silences and increased firing occurred simultaneously in units recorded within a 7 mm distance in the cortex. During wake and rapideye-movement (REM) sleep, single units fired tonically, whereas LFPs displayed low-amplitude fast activities with increased power in fast frequencies (15-75 Hz). In contrast with the widespread synchronization during SWS, fast oscillations during REM and wake periods were synchronized only within neighboring electrodes and small time windows (100-500 msec). This local synchrony occurred in an apparent irregular manner, both spatially and temporally. Brief periods (Ͻ1 sec) of fast oscillations were also present during SWS in between slow-wave complexes. During these brief periods, the spatial and temporal coherence, as well as the relation between units and LFPs, was identical to that of fast oscillations of wake or REM sleep. These results show that natural SWS in cats is characterized by slow-wave complexes, synchronized over large cortical territories, interleaved with brief periods of fast oscillations, characterized by local synchrony, and of characteristics similar to that of the sustained fast oscillations of activated states.

1. A network model of thalamocortical (TC) and thalamic reticular (RE) neurons was developed based on electrophysiological measurements in ferret thalamic slices. Single-compartment TC and RE cells included voltage- and calcium-sensitive currents described by Hodgkin-Huxley type of kinetics. Synaptic currents were modeled by kinetic models of alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA), gamma-aminobutyric acid-A (GABAA) and GABAB receptors. 2. The model reproduced successfully the characteristics of spindle and slow bicuculline-induced oscillations observed in vitro. The characteristics of these two types of oscillations depended on both the intrinsic properties of TC and RE cells and their pattern of interconnectivity. 3. The oscillations were organized by the reciprocal recruitment between TC and RE cells, due to their manual connectivity and bursting properties. TC cells elicited AMPA-mediated excitatory postsynaptic potentials (EPSPs) in RE cells, whereas RE cells elicited a mixture of GABAA and GABAB inhibitory postsynaptic potentials (IPSPs) in TC cells. Because of the presence of a T current, sufficiently strong EPSPs could elicit a burst in RE cells, and TC cells could generate a rebound burst following GABAergic IPSPs. Under these conditions, interaction between the TC and RE cells produced sustained oscillations. 4. In the absence of spontaneous oscillation in any cell, the TC-RE network remained quiescent. Spindle oscillations with a frequency of 9-11 Hz could be initiated by stimulation of either TC or RE neurons. A few spontaneously oscillating TC neurons recruited the entire network model into a "waxing-and waning" oscillation. These "initiator" cells could be an extremely small proportion of TC cells. 5. In intracellular recordings, TC cells display a reduced ability for burst firing after a sequence of bursts. The "waning" phase of spindles was reproduced in the network model by assuming an activity-dependent upregulation of Ih operating via a calcium-binding protein in TC cells, as shown previously in a two-cell model. 6. Following the global suppression of GABAA inhibition, the disinhibited RE cells produced prolonged burst discharges that elicited strong GABAB-mediated currents in TC cells. The enhancement of slow IPSPs in TC cells was also due to cooperativity in the activation of GABAB-mediated current. These slow IPSPs recruited TC and RE cells into slower waxing-and-waning oscillations (3-4 HZ) that were even more highly synchronized. 7. Local axonal arborization of the TC to RE and RE to TC projections allowed oscillations to propagate through the network. An oscillation starting at a single focus induced a propagating wavefront as more cells were recruited progressively. The waning of the oscillation also propagated due to upregulation of Ih in TC cells, leading to waves of spindle activity as observed in experiments. 8. The spatiotemporal properties of propagating waves in the model were highly dependent on the intrinsic properties of TC cells. The spatial pattern of ...

Markov kinetic models were used to synthesize a complete description of synaptic transmission, including opening of voltage-dependent channels in the presynaptic terminal, release of neurotransmitter, gating of postsynaptic receptors, and activation of second-messenger systems. These kinetic schemes provide a more general framework for modeling ion channels than the Hodgkin-Huxley formalism, supporting a continuous spectrum of descriptions ranging from the very simple and computationally efficient to the highly complex and biophysically precise. Examples are given of simple kinetic schemes based on fits to experimental data that capture the essential properties of voltage-gated, synaptic and neuromodulatory currents. The Markov formalism allows the dynamics of ionic currents to be considered naturally in the larger context of biochemical signal transduction. This frame, work can facilitate the integration of a wide range of experimental data and promote consistent theoretical analysis of neural mechanisms from molecular interactions to network computations.

The frequency of spontaneous synaptic events in vitro is probably lower than in vivo because of the reduced synaptic connectivity present in cortical slices and the lower temperature used during in vitro experiments. Because this reduction in background synaptic activity could modify the integrative properties of cortical neurons, we compared the impact of spontaneous synaptic events on the resting properties of intracellularly recorded pyramidal neurons in vivo and in vitro by blocking synaptic transmission with tetrodotoxin (TTX). The amount of synaptic activity was much lower in brain slices (at 34 degrees C), as the standard deviation of the intracellular signal was 10-17 times lower in vitro than in vivo. Input resistances (Rins) measured in vivo during relatively quiescent epochs ("control Rins") could be reduced by up to 70% during periods of intense spontaneous activity. Further, the control Rins were increased by approximately 30-70% after TTX application in vivo, approaching in vitro values. In contrast, TTX produced negligible Rin changes in vitro (approximately 4%). These results indicate that, compared with the in vitro situation, the background synaptic activity present in intact networks dramatically reduces the electrical compactness of cortical neurons and modifies their integrative properties. The impact of the spontaneous synaptic bombardment should be taken into account when extrapolating in vitro findings to the intact brain.