This article describes the concepts and strategies that have guided the design and implementation of this simulator, with emphasis on those features that are particularly relevant to its most efficient use. 1.1 The problem domain

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integrative properties of neocortical pyramidal neurons in vivo. J. Neurophysiol. 81: 1531–1547, 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

The origin of orientation selectivity in visual cortical responses is a central problem for understanding cerebral cortical circuitry. In cats, many experiments suggest that orientation selectivity arises from the arrangement of lateral geniculate nucleus (LGN) afferents to layer 4 simple cells. However, this explanation is not sufficient to account for the contrast invariance of orientation tuning. To understand contrast invariance, we first characterize the input to cat simple cells generated by the oriented arrangement of LGN afferents. We demonstrate that it has two components: a spatial-phase-specific component (i.e., one that depends on receptive field spatial phase), which is tuned for orientation, and a phase-nonspecific component, which is untuned. Both components grow with contrast. Second, we show that a correlation-based intracortical circuit,

Abstract. Networks of compartmental model neurons were used to investigate the biophysical basis of the synchronization observed between sparsely-connected neurons in neocortex. A model of a single column in layer 5 consisted of 100 model neurons: 80 pyramidal and 20 inhibitory. The pyramidal cells had conductances that caused intrinsic repetitive bursting at different frequencies when driven with the same input. When connected randomly with a connection density of lo%, a single model column displayed synchronous oscillatory action potentials in response to stationary, uncorrelated Poisson spike-train inputs. Synchrony required a high ratio of inhibitory to excitatory synaptic strength; the optimal ratio was 4: 1, within the range observed in cortex. The synchrony was insensitive to variation in amplitudes of postsynaptic potentials and synaptic delay times, even when the mean synaptic delay times were varied over the range 1 to 7 ms. Synchrony was found to be sensitive to the strength of reciprocal inhibition between the inhibitory neurons in one column: Too weak or too strong reciprocal inhibition degraded intra-columnar synchrony. The only parameter that affected the oscillation frequency of the network was the strength of the external driving input which could shift the frequency between 35 to 60 Hz. The same results were obtained using a model column of 1000 neurons with a connection density of 5%, except that the oscillation became more regular. Synchronization between cortical columns was studied in a model consisting of two columns with 100 model

: 274, Introduction: 676, Discussion: 2402 Acknowledgments. Thanks to Ken Miller, Allan Dobbins, Christof Koch, and the anonymous reviewers for many helpful comments on this work. This work was funded by the National Science Foundation and the Office of Naval Research, and by a Sloan Foundation Fellowship (D.R.). Abstract Hubel and Wiesel (1962) first distinguished "simple" from "complex" cells in visual cortex, and proposed a processing hierarchy in which rows of LGN cells are pooled to drive oriented simple cell subunits, which are pooled in turn to drive complex cells. Though parsimonious and highly influential, the pure hierarchical model has since been challenged by results indicating many complex cells receive excitatory monosynaptic input from LGN cells, or do not depend on simple cell input. Alternative accounts for complex cell orientation tuning remain scant, however, and the function of monosynaptic LGN contacts onto complex cell dendrites remains unknown. We have used a ...

Bal, and Alain Destexhe. A method to estimate synaptic conductances from membrane potential fluctuations. J Neurophysiol 91: 2884–2896, 2004; 10.1152/jn.01223.2003. In neocortical neurons, network activity can activate a large number of synaptic inputs, resulting in highly irregular subthreshold membrane potential (V m) fluctuations, commonly called “synaptic noise. ” This activity contains information about the underlying network dynamics, but it is not easy to extract network properties from such complex and irregular activity. Here, we propose a method to estimate properties of network activity from intracellular recordings and test this method using theoretical and experimental approaches. The method is based on the analytic expression of the subthreshold V m distribution at steady state in conductance-based models. Fitting this analytic expression to V m distributions obtained from intracellular recordings provides estimates of the mean and variance of excitatory and inhibitory conductances. We test the accuracy of these estimates against computational models of increasing complexity. We also test the method using dynamicclamp recordings of neocortical neurons in vitro. By using an on-line analysis procedure, we show that the measured conductances from spontaneous network activity can be used to re-create artificial states equivalent to real network activity. This approach should be applicable to intracellular recordings during different network states in vivo, providing a characterization of the global properties of synaptic conductances and possible insight into the underlying network mechanisms.

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),

During activated states, neocortical neurons receive intense synaptic background activity that induces large-amplitude membrane potential fluctuations and a strong conductance in the membrane. However, little is known about the integrative properties of neurons during such high-conductance states. Here we investigated the integrative properties of neocortical pyramidal neurons under in vivo conditions simulated by computational models. We show that the presence of high-conductance fluctuations induces a stochastic state in which active dendrites are fast conducting and have a different dynamics of initiation and forward-propagation of Na �-dependent spikes. Synaptic efficacy, quantified as the probability that a synaptic input specifically evokes a somatic spike, was approximately independent of the dendritic location of the synapse. Synaptic inputs evoked precisely timed responses (milliseconds), which also showed a reduced location dependence. This scheme was found to apply to a broad range of kinetics and density distributions of voltagedependent conductances, as well as to different dendritic morphologies. Synaptic efficacies were, however, modulable by the balance of excitation and inhibition in background activity, for all synapses at once. Thus, models predict that the intense synaptic activity in vivo can confer advantageous computational properties to neocortical neurons: they can be set to an integrative mode that is stochastic, fast conducting, and optimized to process synaptic inputs at high temporal resolution independently of their position in the dendrites. Some of these predictions can be tested experimentally. Key words: computational models; random synaptic inputs; noise; high-conductance state; synaptic integration; dendritic democracy

The width of the orientation tuning curves of the spike response of neurons in V1 is invariant to contrast. This property constrains the possible mechanisms underlying orientation selectivity. It has been suggested that noise circumvents the iceberg effect that would prevent contrast invariance in the purely feedforward mechanism. Here we investigate systematically how noise contributes to the contrast invariance of orientation tuning curves in V1. We study three models of increasing complexity: a simple threshold-linear firing rate model, a leaky integrateand-fire model, and a conductance-based model. We show that the noise transmutes the threshold nonlinearity of the input–output relationships into an approximate power law without a threshold within some firing rate range. This implies that, under certain conditions which are derived here, the tuning of the neuron output is approximately contrast invariant. In particular we show that this mechanism for contrast invariance requires that the neuron firing rate must not be too large and that increasing or lowering the contrast too much destroys this invariance. We also show that if this mechanism operates in V1, the spike response, R, and average voltage response V of the neurons in V1 should vary with the contrast, C, according to R(C) � V(C) �. The exponent � can be estimated from the amount by which the spike tuning curve is sharpened with respect to the voltage tuning curves of the neurons. This prediction does not depend on the specifics of the model and can be tested experimentally. Key words: orientation selectivity; primary visual cortex; V1; contrast invariance; noise; integrate-and-fire model; conductance-based model