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

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-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or 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

Synaptic noise due to intense network activity can have a significant impact on the electrophysiological properties of individual neurons. This is the case for the cerebral cortex, where ongoing activity leads to strong barrages of synaptic inputs, which act as the main source of synaptic noise affecting on neuronal dynamics. Here, we characterize the subthreshold behavior of neuronal models in which synaptic noise is represented by either additive or multiplicative noise, described by Ornstein-Uhlenbeck processes. We derive and solve the Fokker-Planck equation for this system, which describes the time evolution of the probability density function for the membrane potential. We obtain an analytic expression for the membrane potential distribution at steady state and compare this expression with the subthreshold activity obtained in Hodgkin-Huxley-type models with stochastic synaptic inputs. The differences between multiplicative and additive noise models suggest that multiplicative noise is adequate to describe the high-conductance states similar to in vivo conditions. Because the steady-state membrane potential distribution is easily obtained experimentally, this approach provides a possible method to estimate the mean and variance of synaptic conductances in real neurons.

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Abstract: Many biological neural network models face the problem of scalability because of the limited computational power of today’s computers. Thus, it is difficult to assess the efficiency of these models to solve complex problems such as image processing. Here, we describe how this problem can be tackled using event-driven computation. Only the neurons that emit a discharge are processed and, as long as the average spike discharge rate is low, millions of neurons and billions of connections can be modeled. We describe the underlying computation and implementation of such a mechanism in SpikeNET, our neural network simulation package. The type of model one can build is not only biologically compliant, it is also computationally efficient as 400 000 synaptic weights can be propagated per second on a standard desktop computer. In addition, for large networks, we can set very small time steps (less than 0.01 ms) without significantly increasing the computation time. As an example, this method is applied to solve complex cognitive tasks such as face recognition in natural images.

We present COMPUCELL3D, a software framework for three-dimensional simulation of morphogenesis in different organisms. COMPUCELL3D employs biologically relevant models for cell clustering, growth, and interaction with chemical fields. COMPUCELL3D uses design patterns for speed, efficient memory management, extensibility, and flexibility, permitting the simulation of various organisms. We verify COMPUCELL3D by building a model of growth and skeletal pattern formation in the avian (chicken) limb bud. Binaries and source code are available, along with documentation and input files for sample simulations, at http://www.nd.edu/lcls/compucell.

this article). Stochastic forcing also broadens the peak of the average subthreshold voltage versus input current curves as in Figure 5b (not shown). In view of this, we have set out to determine (1) whether a subtractive effect is also present with stochastic synaptic input and (2) whether stochastic input produces a divisive regime at lower firing frequencies, as in the compartmental model. For simplicity we considered only the voltage-independent case (# 0) of equation 4.1, since divisiveness was also seen for the S synapses in the compartmental simulations (see Figure 5a). There are a variety of ways in which a stochastic synaptic model with reversal potentials can be approximated by diffusion models (Lansk y & Sato, 1999). Here we let the conductance g in the LIF model be a stochastic quantity by setting g = +#(g)#(t) where g is the mean conductance and #(t) is a stochastic process of standard deviation #( g). To match the smoothness of the conductance fluctuations in the compartmental model, we model #(t) as an Ornstein-Uhlenbeck process (lowpass-filtered gaussian white noise) with correlation time # 75 ms; our results were not qualitatively sensitive to this correlation time. Equation 4.1 thus becomes a stochastic differential equation with multiplicative noise (since the noise term multiplies the state variable Vm ): #t + #( g)#(t) I (4.7 # ) ## #t =- # +#(4.7 ## ) where # is gaussian white noise with zero-mean and unit standard deviation. Numerical simulations produced sigmoidal curves of mean firing rate versus input current I (see Figure 7), as expected. Increases in inhibitory firing rate in the compartmental model were modeled here as increases in mean conductance g; these increases are linearly related, as discussed above...

The availability of efficient and reliable simulation tools is one of the mission-critical technologies in the fast-moving field of computational neuroscience. Research indicates that higher brain functions emerge from large and complex cortical networks and their interactions. The large number of elements (neurons) combined with the high connectivity (synapses) of the biological network and the specific type of interactions impose severe constraints on the explorable system size that previously have been hard to overcome. Here we present a collection of new techniques combined to a coherent simulation tool removing the fundamental obstacle in the computational study of biological neural networks: the enormous number of synaptic contacts per neuron. Distributing an individual simulation over multiple computers enables the investigation of networks orders of magnitude larger than previously possible. The

Abstract—Dual intracellular recordings in slices of adult rat neocortex and hippocampus investigated slow, putative GABA B receptor-mediated inhibitory postsynaptic potentials. In most pairs tested in which the interneuron elicited a fast inhibitory postsynaptic potential in the pyramid, this GABA A receptor mediated inhibitory postsynaptic potential was entirely blocked by bicuculline or picrotoxin (3:3 in neocortex, 6:8 in CA1, all CA1 basket cells), even when high-frequency presynaptic spike trains were elicited. However, in three of 85 neocortical paired recordings involving an interneuron, although no discernible response was elicited by single presynaptic interneuronal spikes, a long latency (�20 ms) inhibitory postsynaptic potential was elicited by a train of �3 spikes at frequencies �50–100 Hz. This slow inhibitory postsynaptic potential was insensitive to bicuculline (one pair tested). In neocortex, slow inhibitory postsynaptic potential duration reached a maximum of 200 ms even with prolonged presynaptic spike trains. In contrast, summing fast, GABA A inhibitory postsynaptic potentials, elicited by spike trains, lasted as long as the train. Between four and 10 presynaptic spikes, mean peak slow inhibitory postsynaptic potential amplitude increased sharply to 0.38, 2.6 and 2.9 mV, respectively, in the three neocortical pairs (membrane potential �60 to �65 mV). Thereafter increases in spike number had little additional effect on amplitude. In two of eight pairs in CA1, one involving a presynaptic basket cell and the other a putative

Neuronal function involves the interaction of electrical and chemical signals that are distributed in time and space. ...