Ls have been all of either FS or LTS kind. A random network because the one described above constitutesHere, we define the quantities and measures that characterize the spiking properties of single neurons and from the complete network. The spike train of a neuron i is represented as (Gabbiani and Koch, 1998; Dayan and A competitive Inhibitors products Abbott, 2001), xi (t) =f ti(t – ti ),f(four)Frontiers in Computational Neurosciencewww.frontiersin.orgSeptember 2014 | Volume eight | Post 103 |Tomov et al.Sustained activity in cortical modelsFIGURE 2 | Examples of connection matrices for hierarchical and modular networks at H = 0, . . . , 3 constructed with rebating probabilities given in text. Each dot represents a connection from a presynaptic neuron to a postsynaptic one particular.exactly where ti would be the set of times at which a neuron i fires. The firing price of this neuron over a time TFV-DP Biological Activity interval T is the number ni of spikes which it fires during the interval, divided by T: fi = ni 1 = T T xi (t )dt .Tf(five)Similarly, the mean firing price of N neurons within the network over a time interval T is: f = 1 NN i=1 Txi (t )dt .T(6)Equation (7) gives the variation from the number of active neurons within the network inside the interval t whilst Equation (8) offers the variation with the proportion of active neurons within t. Due to the fact t in each expressions will probably be fixed at 1 ms all through this study, below we denote the time-dependent activity and firing price of your network merely by A(t) and f (t). Irregularity of network firing was characterized by two distributions: the distribution of interspike intervals (ISI) of all neurons in the network, and the distribution of the coefficients of variation (CV) in the ISIs of every neuron. The ISI distribution was formed by the set ISIi , i = 1, . . . , N for all neurons. To obtain the distribution with the CVs, we calculated for just about every neuron i the common deviation ISIi of its ISIi distribution normalized by the imply ISIi for this neuron (Gabbiani and Koch, 1998): CVi = ISIi , ISIi (9)The time-dependent activity of the network A(t; t) was defined as the total quantity of spikes fired by its neurons inside a time interval t about t:NA(t; t) =i=1 tt+ txi (t )dt .(7)Dividing it by the number of neurons, we obtain the timedependent firing price of your network: f (t; t) = 1 NN i=1 t t+ tand took the set of CVi for all network neurons. Basing on the values of those activity measures extracted from the raster plots of your simulations, we delineated the regions exactly where SSA was observed on the plane of excitatory and inhibitory conductances gex , gin .3. RESULTS3.1. PARAMETER DEPENDENCE OF SSAxi (t )dt .(8)Beneath, “architecture on the network” denotes the topology from the network, i.e., hierarchical level H, plus its composition, i.e., theFrontiers in Computational Neurosciencewww.frontiersin.orgSeptember 2014 | Volume 8 | Post 103 |Tomov et al.Sustained activity in cortical modelstypes and proportions of participating neurons. A provided network realization is then a network with fixed architecture, produced randomly by the algorithm from the preceding section. We activated the network by injecting external present of amplitude Istim into a proportion Pstim in the neurons for the time interval Tstim . Soon after stimulus termination, the network was left to evolve freely until the finish of simulation time Tsim . While this activation may perhaps appear adequate sufficient from a physiological point of view, inside the dynamical sense it plays only the role of setting initial circumstances. Inside the course of stimulation, the.
