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Neocortex plays key role in diverse brain functions. Understanding this role involves the study of collective neural activity patterns under different situations, and how these patterns relate to the structural and functional organization of neocortex. Here we study the effect of synaptic plasticity on neural spiking activity patterns in a neocortical network model. We measure changes in neural spiking patterns due to changes in the strengths of the synapses connecting neurons and relate them to changes in the functional connectivity of the network as disclosed by graph-theoretic measures. Our neocortical network model was composed of excitatory and inhibitory neurons in the proportion of four excitatory cells for each inhibitory cell. Neurons were described by the Izhikevich model [1]. The parameters of the model were adjusted so that excitatory neurons were of the regular spiking (RS) type and inhibitory neurons were all of either the fast spiking (FS) or the low-threshold spiking (LTS) type. Synapses were modeled as event-based, and two types of synaptic dynamics were considered: one without synaptic plasticity in which the synaptic weight received a fixed increment after the pre-synaptic event and decayed exponentially after that, and one with synaptic plasticity in which the synapse obeyed an asymmetric spike-timing dependent plasticity (STDP) rule described by [2]. Neurons were organized into four layers (2/3, 4, 5 and 6) with layer- and cell-specific statistical connectivity rules based on [3]. The total number of neurons in the model was about 4,000. Two experiments were done: one with all synapses described by the model without synaptic plasticity, and the other with synapses between excitatory neurons described by the STDP rule while the remaining synapses were described by the model without synaptic plasticity. In both cases, the model was stimulated by a current injection of random amplitude applied to neurons of layer 4 (L4), which is the main input layer of the cortex. The spiking activity of the network was evaluated by measures extracted from the raster plot of the spikes produced by the neurons, e.g. layer-specific and network mean and time-dependent firing rates. The structural and functional connectivities of the network were represented by the respective structural and functional adjacency matrices. The functional adjacency matrix was constructed by taking in consideration neuron pairs with strength of their synaptic coupling above a specific threshold. The topology of the adjacency matrices was characterized by graph-theoretic measures, e.g. clustering coefficient. We determined a set of parameters for which the spiking activity generated in L4 by the external input propagated to the entire network. This network-wide activity was oscillatory, and we found that its mean frequency was higher for the network version with synaptic plasticity than for the version without synaptic plasticity. We also found that the formation of clusters of synchronous neural activity was facilitated in the case with LTS cells as inhibitory neurons. Our results suggest that synaptic plasticity may induce changes in the functional connectivity of the neocortical network with impact on its global activity.

It is thought that modifications in the strength of synaptic connections onto cerebellar Purkinje cells (PCs) such as long-term depression (LTD) and long-term potentiation (LTP) contribute to learning. Repeated paired stimulations of parallel fibres (PFs) and climbing fibre (CF) evoke high-amplitude calcium signals that lead to LTD in PCs, while LTP is induced by low-amplitude calcium pulses as a result of PF input alone. Although the events following these stimulation protocols are becoming clearer, the precise interactions of the molecular machinery underlying LTD and LTP induction remain something of an enigma. Calcium-calmodulin dependent protein kinase II (CaMKII) is an important component of the signalling network that is responsible for cerebellar plasticity in PCs. The CaMKII holoenzyme contains isoforms such as αCaMKII and βCaMKII. Experiments with Camk2b knockout mice that lack the β isoform of CaMKII have demonstrated that βCaMKII regulates the direction of plasticity at PF-PC synapses [1]. Stimulation protocols that induce LTD in wild-type mice, which contain both α and βCaMKII, lead to LTP in knockout mice without βCaMKII, and vice versa. In previous work, we have developed a kinetic simulation of the phosphorylation and dephosphorylation of AMPA receptors by CaMKII and protein phosphatase 2B (PP2B) to investigate how βCaMKII controls bidirectional plasticity in cerebellar PCs [2]. This bidirectional plasticity model was based on our earlier model of CaMKII activation [3,4]. Because the βCaMKII, but not αCaMKII, isoforms can bind to filamentous actin (F-actin), we have included the binding of F-actin to CaMKII to simulate plasticity induction in wild-type mice, whereas the F-actin binding was omitted in the knockout mice that lack the β isoform. Our previous simulation results have demonstrated that the binding of F-actin to βCaMKII can contribute to the regulation of bidirectional plasticity at PF-PC synapses, as suggested by Van Woerden and collaborators [1]. However, we have investigated the induction of bidirectional plasticity only in the presence of calcium signals, i.e. during the early phase of LTD and LTP. But the experimental observations in [1] have demonstrated that the bidirectional plasticity is also observed after the offset of paired PF and CF inputs and PF input alone. To analyse the persistent AMPA receptor phosphorylation after calcium stimulation protocols that last 5 min, we modified our previous model [2] because it did not simulate the CaMKII dephosphorylation, and PP2B was rapidly inactivated with the interruption of calcium signals. Here, we allow the CaMKII subunits in the autonomous form, which are not bound to calcium-calmodulin but still retain some activity levels, to be dephosphorylated and switch to the inactive state of CaMKII. The kinase is therefore gradually inactivated after calcium stimulation stops. Moreover, calcium-calmodulin slowly dissociates from PP2B, leading to a gradual deactivation of PP2B. Results of computer simulations of our mathematical model suggest that the balance of CaMKII-mediated phosphorylation and PP2B-mediated dephosphorylation of AMPA receptors determines whether LTD or LTP occurs at the PF-PC synapse. PF activity alone evokes PP2B concentration levels higher than CaMKII, leading to LTP induction. Instead, LTD occurs when the CaMKII concentration levels surpass the PP2B concentration in response to the paired activation of PF and CF. Moreover, our recent simulation results replicate the experimental observations by Van Woerden et al. [1] not only in the early phase of LTD and LTP as shown in [2], but also in their intermediate and late phases after the termination of PF and CF stimulation and PF input alone. The mechanisms observed in our previous simulations with 5 min duration are also observed in simulations that last 100 min. The loss of F-actin binding in the knockout mice enhances the availability of active CaMKII when compared to the wild-type mice at low calcium concentrations. This favours the induction of LTD rather than LTP. In contrast, the reduction in the CaMKII concentration in the knockout mice leads to AMPA receptor dephosphorylation by PP2B for high calcium concentrations, leading to the induction of LTP instead of LTD. We again demonstrate that the binding of F-actin to βCaMKII can indeed contribute to the control of bidirectional plasticity at PF-PC synapses, as suggested in [1,2].

In the olfactory system, odors are detected by olfactory sensory neurons (OSN), which send their axons to glomeruli in the olfactory bulb (OB) where they make excitatory synapses with mitral and periglomerular (PG) cells [1]. The OB has a complex internal circuitry, which displays two levels of lateral inhibition. At the glomerular level, mitral and PG cells interact via dendrodendritic synapses in which mitral cells excite PG cells and PG cells inhibit mitral cells. Deeper at the external plexiform layer, mitral and granule cells also interact via dendrodendritic synapses, with mitral cells exciting granule cells and granule cells inhibiting mitral cells. Here we present a model of the OB constructed in NEURON [2] to study the effects of PG and granule cells on odor discrimination. The model contains 6 OSNs, 6 mitral cells, 30 PG cells and 240 granule cells. The OSNs are modeled by a 2-compartment adaptation of a previous model [3], the PG cells are modeled by a 6-compartment model recently developed by us [4], and the mitral and granule cell models were taken from [5]. The OB model is organized into 3 glomeruli, each one receiving inputs from a different pair of OSNs. Each glomerulus has 2 mitral cells and 10 PG cells and is associated with 80 granule cells. The cells were connected according to observed patterns of OB synaptic organization, with intra-glomerular synapses between mitral and PG/granule cells and inter-glomerular synapses mediated by PG cells. The model was stimulated by 5 different odors with different patterns of activation of the 3 glomeruli. Odors 1, 2 and 3 strongly activate distinct glomeruli but odors 4 and 5 activate the 3 glomeruli with overlapping patterns. The model responses to odors were measured by the average firing frequency of the two mitral cells in each glomerulus and the raster plots of the 6 mitral cells. The inhibitory effects of the PG and granule cells on the model response were evaluated by varying the maximum synaptic conductances of their synapses onto mitral cells between 0 and 0.01 microsiemens in steps of 0.001 microsiemens. For each pair of values of these maximum synaptic conductances 10 repetitions of each simulation were done to calculate averages. This allowed us to assess both the independent and collective effects of the PG and granule cells on the responses of the 3 glomeruli to the 5 odors. Our results show that PG cells alone can efficiently shape the responses of the 3 glomeruli so that they discriminate the 5 odors. With small increases in the maximum conductance of the PG cell inhibitory synapse, the glomeruli that already responded strongly to a single odor had their responses enhanced and the glomeruli that responded to the three odors had their response strength to one of them increased while the responses to the other two decreased. On the other hand, the inhibitory action of the granule cells alone was much less efficient in enhancing the responses of the first three glomeruli to odors 1, 2 and 3 and was not capable of improving the discriminatory capability of the latter two glomeruli to odors 4 and 5. On the contrary, the effect of increasing the maximum conductance of the granule cell synapse was to increase the response overlaps of the latter glomeruli to these odors.

The cerebral cortex exhibits spontaneous and structured collective activity patterns even in the absence of external stimuli [1]. A possible question that can be made given this evidence is: how does the topology of the cortical network together with the individual properties of the neuronal types that populate it affect this self-sustained neural activity? Anatomical evidence suggests that cortical architecture has modular and hierarchical design [2]. Based on their response patterns to intracellular current injection, cortical neurons may be classified into 5 main electrophysiological classes [3]: regular spiking (RS), intrinsically bursting (IB), chattering (CH), fast spiking (FS) and with low threshold spikes (LTS). Cells from the first 3 types are excitatory and cells from latter 2 are inhibitory [3]. In this work we used a hierarchical and modular network model composed of excitatory and inhibitory neurons to study the joint effect of (i) modularity level and (ii) combination of excitatory and inhibitory cell types on self-sustained network activity. Our hierarchical and modular network was constructed using a top-down method [4] starting with a random network of 1024 cells with connection probability of 0.01. The ratio of excitatory to inhibitory neurons was 4:1. Neurons were modeled using Izhikevich's neuron model [5] with parameters adjusted to reproduce the firing behaviors of the 5 cell types. Our model has 2 synaptic conductances (ge, gi), representing excitatory and inhibitory synapses. After a pre-synaptic event, these synaptic conductances are increased by constants Δge, Δgi. Otherwise, they decay according to first-order linear kinetics with characteristic times τe = 5 ms and τi = 6 ms. Using the modularization method of [4] (with rewiring probabilities Ri = 1 and Re = 0.9), we generated networks with 4 modularity levels (0-3). For each level we generated 6 networks given by the possible combinations of the 3 excitatory neuron types with the 2 inhibitory types. Our experimental protocol consisted in stimulating a network by applying white noise to all neurons for 200 ms. After the noise was switched off the simulation was allowed to run for an extra 4800 ms. We performed this experiment for 50 realizations of each network (to calculate averages) and for different combinations of the parameters (Δge, Δgi). From the resulting data we constructed, for each network configuration, a Δge-Δgi diagram plotting the time of last network spike. We defined a threshold for this time (~4500 ms) beyond which we assumed the network as having self-sustained activity. For each diagram, we measured the fraction of its total area for which self-sustained activity existed according to our criterion. This fraction, called FTA, was our measure of self-sustained network activity. Our results show that (1) FTA increases with the modularity level; (2) networks with RS excitatory cells have the highest FTA variability with modularity level; and (3) networks with CH excitatory neurons have the smallest FTA variability with modularity. For these latter networks, FTA reached maximum value for level-1 modularity. Our results show a strong dependency of self-sustained activity on both the modularity level and types of excitatory and inhibitory cells. They suggest that modular architecture favors self-sustained activity and that networks with most of excitatory cells of the RS class exhibit the widest range of self-sustained regimes.

The elevated plus-maze (EPM) is a widely used anxiety animal model [1]. It consists of a plus-shaped maze elevated 50 cm from the floor. The maze is composed of two open arms and two closed arms crossing at right angles at a central area. The usual anxiety measures are the percentages of open or closed arm entries and the percentages of time spent on open or closed arms [2]. Recently, we have proposed a graph representation of the EPM in which the maze floor is divided into 21 squares that are treated as nodes of a directed graph [3], i.e. transitions between connected nodes can occur in both directions. Using the directed graph representation we have shown that the displacements of the rat in the EPM during a 5 min trial can be well modeled by a first-order homogeneous Markov chain [4]. Moreover, we have shown that the estimated stochastic matrix, which gives the transition probabilities between nodes, is sensitive to the pharmacological condition of the animal, i.e. if it belongs to the control group or was treated with an anxiogenic or anxiolytic drug [4]. The stochastic matrices for the different drug treatment types are interesting to compare different groups of rats but they are not practical as succinct indexes of their anxiety level. Here we propose a new anxiety index for the rat behavior in the EPM based on measures from the stochastic matrix. The stochastic matrices were estimated from the recorded behaviors of 107 rats submitted to the EPM test under different pharmacological conditions. Twenty-five of the rats were control animals, 44 received anxiogenic drugs (pentylenetetrazol, 10, 20, and 30 mg/kg; semicarbazide, 20 mg/kg), and 38 received anxiolytic drugs (midazolam, 1 µg/kg; chlordiazepoxide, 5 mg/kg). From these matrices, the mean number of transitions until the rat reaches for the first time one of the nodes in the open or closed arms, starting from the central node at time 0, is calculated for all graph nodes. These mean values are called average absorption times of the nodes, which can be grouped in different ways. In this work, we considered the ratio of the average absorption time of a closed arm node to the average absorption time of the symmetrical node on the open arm. For high doses of anxiogenic drugs not all graph nodes are reached by the rat. These nodes were discarded from our calculations. Our results show that for each pharmacological condition the ratios determined as explained above are approximately the same for any distance from the center. This invariance suggests that only one of the average absorption time ratios can be used to characterize the pharmacological condition of the animal. Based on this finding we propose that the average absorption time ratio can be used as an additional index to characterize the anxiety-related behavior of the rat in the EPM.

Mitral cells in the olfactory bulb (OB) receive dendrodendritic inhibitory synaptic inputs at two different levels of OB: at the glomerular level, inhibitory input is provided by periglomerular (PG) cells and, at a deeper level (the external plexiform layer), inhibitory input is provided by granule (Gr) cells [1]. It is thought that both kinds of inhibition control mitral cell spiking in specific ways: PG early inhibition would regulate spike initiation while Gr inhibition would coordinate spike synchronization among mitral cells [1,2]. However, recent computational modeling studies have suggested that, although acting at separate locations, PG and Gr inhibitory synapses may have integrated effects on mitral cell spiking which may be beneficial for odor representation and processing [2]. In this work we constructed in NEURON [3] a model of a triad composed of reduced compartmental models of a mitral cell, a PG cell and a Gr cell to study the combined effect of the inhibitory dendrodendritic synapses of the PG and Gr cells on the firing frequency of the mitral cell. The mitral and Gr cell models were taken from [4] with all parameters and characteristics kept as in the original. The PG cell model was based on the five-compartment model used in [5]. The cells were coupled by models of their reciprocal dendrodendritic synapses. The main parameters investigated in our simulations were the conductances of the dendrodendritic synapses, the amplitudes, durations and initiation times of the excitatory inputs applied to the mitral and PG cells representing stimuli coming from olfactory sensory neurons, and the amplitude, duration and initiation time of the input applied to the Gr cell representing afferents from other cells in the olfactory bulb. We performed simulations in which these parameters were varied and studied their effects on the mitral cell’s spiking pattern. Our results show that the response pattern of the mitral cell is strongly dependent of the two inhibitory synapses, and that their interaction can regulate both the spike frequency and timing of the mitral cell. Figure ​Figure11 illustrates one of these results. Figure 1 Firing rate of mitral cell as a function of maximal conductances of inhibitory synapses made by PG (horizontal axis) and Gr (vertical axis) cells. The color-code bar on the right indicates frequency in Hz.

Reduced compartmental models of single neurons, i.e. models with few Hodgkin-Huxley (HH) type compartments, provide an interesting balance between highly detailed, morphologically realistic models, and abstract, point-like models [1]. Simoes-de-Souza and Roque [2] constructed a four-compartment model of the vertebrate olfactory receptor neuron (ORN), which was used in a large-scale simulation of the olfactory system [3]. Their model contains a biochemical compartment, which allows the study of the role of molecular pathways in odor representation. However, the use of this compartment slows down computer simulations of models with many receptor cells and makes the analysis of network behavior more complicated – because of the large number of variables and parameters. In this work, we present a simpler ORN model which does not include biochemical pathways and contains only three HH type compartments. Our model is different from [2] not only because it has one compartment less but also because its passive and active parameters have new values adjusted by a combination of “hand-fitting” with automatic fitting procedures so as to reproduce well experimental results for the rat [5]. The model was constructed in GENESIS [4]. Its three compartments kept the same names used in [2]: soma, dendrite and dendritic knob. The latter two are passive compartments while soma has four voltage-gated ionic currents: high-voltage-activated calcium current, fast voltage- and calcium-dependent potassium current, slow calcium-dependent potassium current, and delayed rectifier potassium current. These ionic currents were modeled according to the standard HH formalism with kinetic parameters adjusted as mentioned above. Input stimuli were constant current steps injected directly in dendritic knob to simulate experimental results [5]. Some simulation results are shown in Figure ​Figure1.1. Our reduced three-compartment model may allow faster and more efficient large-scale network simulations of the rat olfactory system. Figure 1 Some simulation results. A. Spike train generated by a 10 pA step current (to be compared with Figure 2B of [5]); B. Action potential (to be compared with the inset of Figure 2A of [5]).

Recent studies suggest the existence of electrical synapses (gap junctions) in the vertebrate retina, which would be present in at least three different circuits responsible for transmission of rod signals to ganglion cells. In this work we present a computer model of the receptor layer made of 900 biologically realistic rods coupled by gap junctions. The rod model has six types of ionic currents and the connectivity patterns within the receptor layer are based on experimental data available from the literature. We study the role of the gap junction coupling on the enhancement of the dynamic range of the photoreceptor layer beyond the dynamic range of a layer made of uncoupled rods. Simulation results show that the presence of gap junctions in early stages of signal processing could increase the dynamic range of the photoreceptor layer by preventing early saturation.

The vertebrates deal with visual stimuli with a broad range of intensity values. To enable these organisms to respond to this wide range of intensities there must be some mechanism capable of compressing the sensory signals at the retina [1]. It has been hypothesized that the wide dynamic range of the retina is due to electrical coupling of retinal ganglion cells (GCs) at the retina´s output layer [1-3]. The dynamic range of a network of neurons can be defined as the range of input stimuli values for which the network responds before saturation. In this work we constructed a biologically plausible model of the ganglion cell layer of the salamander retina and submitted it to simulated input stimuli. The GCs are connected via gap junctions and different values of the average connectivity were used to evaluate the effect of electrical coupling on the dynamic range of the network. We used the conductance-based model of the tiger salamander GC of Fohlmeister and Miller [4]. This is a single-compartment model containing the following voltage-dependent membrane conductances: sodium, calcium, A-type potassium, calcium activated potassium, and delayed rectifier potassium. A 20x20 square lattice was built with one GC in each node. Each GC in the network is randomly connected by electrical synapses with its nearest neighbors, with the average number of coupled neighbors being k = 0, 2 or 4. The network was stimulated by injecting in each cell an excitatory postsynaptic current with synaptic conductance modeled by an alpha function with decay time of 400 ms. The reversal potential of the synaptic current was set as 1.5 mV. The simulations were run using the NEURON simulator. The response of the network was taken as the total number of spikes during 1 sec of stimulation. Based on the F-I curves for the network we calculated the dynamic range (in decibels) for each average connectivity index k. The results (Figure ​(Figure1)1) show that electrical coupling enhances the dynamic range of the GC network. This suggests that GC coupling via electrical synapses may be involved in the mechanism that endows the vertebrate retina with its large dynamic range. Figure 1 Effect of electrical coupling on the dynamic range of the GC network for different values of the connectivity index k. Each column corresponds to the mean value of 300 neurons. * Significantly different from the control group with k = 0 (Dunn, P < ...

EEG oscillations are found in several structures of the olfactory system, like the olfactory epithelium (OE), olfactory bulb (OB) and pyriform cortex (PC). From the theoretical side a possible way to study mechanisms responsible for the origin of these oscillations is the construction of biologically detailed computational models, which can exhibit oscillatory neural activity and allow simulations of EEG measurements which can be compared with real data. In this work we present a large-scale biologically detailed model of the olfactory system consisting of models of OE, OB and PC and use it to study relationships between EEG oscillations in these three areas and the respiratory rhythm. The OE model contains 2500 olfactory sensory neuron models (OSNMs) distributed in a 50 × 50 square grid. The OB model has two cell layers, a 8 × 8 grid of mitral cell models and a 10 × 10 grid of granule cell models. The PC model has 96 pyramidal cell models distributed in a 16 × 6 grid and 225 interneuron models arranged in three layers with 75 model cells in each one of them. These three layers are called, respectively, multipolar cell layer, horizontal cell layer and globular cell layer. We developed a function that generates a receptor current in the OSNM based on a respiratory frequency and odor concentration. This receptor current is injected directly at the soma of an OSNM, simulating the effect of a stimulus. The responses of the OE, OB and PC were measured in terms of raster plots and simulated EEG records. In OE and OB, EEG records were made by single point electrodes placed at the centers of both the OE and OB models. In PC, the EEG was calculated as the average of the extracellular field potentials measured by a simulated grid of 8 × 5 point electrodes placed at the surface of the PC model. The results show that the slow components of the electrical oscillations produced in OB and PC are directly associated with the respiratory frequencies and odor concentrations at the receptor layer while the fast components are related with the intrinsic synaptic activity at each neural layer.

According to the classical view about cortical organization, primary sensory areas are exclusively dedicated to a single sensory modality. Recent studies have challenged this view by showing that primary sensory cortices may undertake cross-modal processing whenever necessary [1,2]. Despite the growing interest in cross-modal cortical processing, the neural basis of this phenomenon remains largely unknown. How to understand cross-modal cortical processing given the anatomical structure of the cortex and the dynamic features of cortical neurons? It is known that cortical architecture is modular, so that neurons within a module are more densely connected among themselves than with neurons in other modules [3], and that the specificities of cortical connections depend on their range, with excitatory connections reaching longer distances than inhibitory connections [4]. It is also known that cortical neurons belong to different electrophysiological classes [5]. In this work we used a modular network of spiking neurons to study cross-modular processing in terms of average spike rate and latency. The network was made of 1,000 neurons modeled according to the Izhikevich formalism [6] to simulate three electrophysiological classes: fast spiking (FS), regular spiking (RS) and bursting (BS) neurons. Synaptic connections were modeled by α-functions. To generate the modular network, we started with a random network with a 4:1 ratio of excitatory to inhibitory neurons and sparse connectivity. The network was randomly divided into four modules of equal size. Each existing inter-modular inhibitory connection was deleted and corresponding intra-modular inhibitory connections were randomly created in turn. A similar rewiring was made for the inter-modular excitatory connections but with probability p of deleting an excitatory inter-modular connection. Furthermore, two modules (let us call them modules 1 and 2) were randomly selected and all connections between them were deleted. Three experimental protocols were performed: (a) the entire network was stimulated by random noise for 1200 msec; (b) similar to (a) but module 1 also received a fixed input pattern from t = 200 msec to t = 250 msec; and (c) the only input given to the network was the fixed pattern to module 1 only for the same time interval as in (b). The raster plots of the four modules for protocol (a) show a similar frequency response for all of them but the spiking activity of module 2 cells is out of phase in comparison with activities in other modules. In the case of protocol (b), the presentation of the fixed input pattern to module 1 caused a slight and sustained increase in the average firing frequency of all modules. The start of this increase is delayed by 15 msec for modules 3 and 4, which are directly connected with module 1, and by 30 msec for module 2, which is only indirectly connected with module 1. These delays are not only due to synaptic delay (equal to 1 msec) but may also be due to intrinsic network properties. For protocol (c) the situation was similar to protocol (b) but with a lower average spike rate which decays toward zero after offset of the external stimulus. The simple network studied allow propagation of information between modules, with modules that do not receive direct external input responding stably (with delays) to an external and fixed input pattern presented to a single module. This suggests that more detailed analyses of modular networks may help understanding of cross-modal processing.

Newly born dentate gyrus (DG) granule cells (GCs) after Status Epilecticus induced by pilocarpine (PILO) exhibit morphological changes including narrower arborizations, greater number of branches and more endings in the inner molecular layer (IML) [1]. The increased concentration of dendrites in the IML where granule cell axons (mossy fibers) sprout in epileptic animals and make extensive recurrent excitatory synapses may contribute to enhance the DG hyperexcitability. A previous DG network model has shown that mossy fiber sprouting has a crucial role on hyperexcitability with only 10% sprouting being enough to generate spread of activity to all GCs in the network [2]. However, the additional effect of GC morphological changes on DG hyperexcitability is as yet unknown. Here we used the DG model [2] to evaluate the effect of different GC morphologies on the network activity. We replaced all reduced GC models of [2] with morphologically detailed models coming from tridimensional reconstructions of newly born doublecortin-positive DG GCs [1]. The scheme of the network connections is shown in Figure ​Figure1A.1A. Our sample of morphologically reconstructed GCs includes 20 from PILO-treated rats and 20 from control rats. Our cell models were constructed in NEURON and their ionic channels and maximum conductance distributions were the same as [2] taking into consideration the shorter arborizations of the PILO-treated cells. Our “control” network model is exactly the same as the topographic ring network of [2] with 10% sprouting available in ModelDB with the 500 original GC models replaced by morphologically reconstructed cells randomly chosen from our control sample. From our control network model we constructed two other models: one with 10% of the GCs (chosen randomly) replaced by cells chosen randomly from our PILO-treated sample and the other with the fraction of PILO-treated GC cells increased to 50%. The networks were submitted to focal perforant-path stimulations as in [2] and to obtain averages we ran 100 simulations for each network (connections between cells were created anew before each run). Our control network model produced a response similar to the one of the original model [2], showing that the insertion of GCs with realistic morphologies did not affect the 10% sprouting DG hyperexcitability (Figure 1 B, 100% C). In contrast, when a few amount of PILO-treated GCs were inserted the excitability of the network increased (Figure ​(Figure1B1B , 90% C – 10% P) and the increase was higher when the amount of PILO-treated GCs was larger (Figure 1 B, 50% C – 50% P). However, the effect of the insertion of PILO-treated GCs was only visible in combination with mossy fiber sprouting. Our results suggest that changes in GC morphology alone are not enough to affect the DG hyperexcitability but when these changes occur in the presence of other alterations such as mossy fiber sprouting they could enhance the DG hyperexcitability. Figure 1 Basic circuit diagram and effect of PILO-treated GC morphological changes on the average GCs response. A. Schematic view of the network (adapted from [1]). GC: granule cell; BC: basket cell; HC: HIPP cell; MC: mossy cell. DD: distal dendrites; MD: medial ...