Ls were all of either FS or LTS variety. A random network as the a single described above constitutesHere, we define the quantities and measures that characterize the spiking properties of single neurons and with the complete network. The spike train of a neuron i is represented as (Gabbiani and Koch, 1998; Dayan and Abbott, 2001), xi (t) =f ti(t – ti ),f(four)Frontiers in Computational Neurosciencewww.frontiersin.orgSeptember 2014 | Volume 8 | Post 103 |Tomov et al.Sustained D-Fructose-6-phosphate (disodium) salt Data Sheet activity in cortical modelsFIGURE two | Examples of connection matrices for hierarchical and modular networks at H = 0, . . . , three constructed with rebating probabilities provided in text. Every dot represents a connection from a presynaptic neuron to a postsynaptic one particular.exactly where ti may be the set of occasions at which a neuron i fires. The firing price of this neuron over a time interval T is definitely the quantity ni of spikes which it fires in the course of the interval, divided by T: fi = ni 1 = T T xi (t )dt .Tf(5)Similarly, the mean firing price of N neurons in the network over a time interval T is: f = 1 NN i=1 Txi (t )dt .T(6)Equation (7) delivers the variation from the number of active neurons within the network within the interval t although Equation (8) offers the variation in the proportion of active neurons inside t. Considering that t in each expressions will probably be fixed at 1 ms all through this study, beneath we denote the time-dependent activity and firing rate on the network basically by A(t) and f (t). Irregularity of network firing was characterized by two distributions: the distribution of interspike intervals (ISI) of all neurons within the network, as well as the distribution from the coefficients of variation (CV) of your ISIs of each and every neuron. The ISI distribution was formed by the set ISIi , i = 1, . . . , N for all neurons. To obtain the distribution in the CVs, we calculated for every neuron i the normal deviation ISIi of its ISIi distribution normalized by the mean ISIi for this neuron (Gabbiani and Koch, 1998): CVi = ISIi , ISIi (9)The time-dependent activity from the network A(t; t) was defined because the total quantity of spikes fired by its neurons within a time interval t around t:NA(t; t) =i=1 tt+ txi (t )dt .(7)Dividing it by the amount of neurons, we get the timedependent firing rate in the network: f (t; t) = 1 NN i=1 t t+ tand took the set of CVi for all network neurons. Basing around the values of these activity measures extracted from the raster plots from the simulations, we delineated the regions where SSA was observed around the plane of excitatory and inhibitory conductances gex , gin .three. RESULTS3.1. PARAMETER DEPENDENCE OF SSAxi (t )dt .(8)Below, “architecture in 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 eight | Article 103 |Tomov et al.Sustained activity in cortical modelstypes and proportions of participating neurons. A offered network realization is then a network with fixed architecture, developed randomly by the algorithm from the preceding section. We activated the network by injecting external current of amplitude Istim into a proportion Pstim of your neurons for the time interval Tstim . Soon after stimulus termination, the network was left to evolve freely till the finish of simulation time Tsim . When this activation may well appear adequate sufficient from a physiological point of view, in the dynamical sense it plays only the function of setting initial circumstances. Within the course of stimulation, the.
