1,721,182 research outputs found
Generation of the PC output of the network [Dataset]
No full text(i) Raster plots of PCs in blue (and BCs in orange for completeness). The convolution of each PC spike with either a 1 ms Gaussian kernel (ii) or a 5 ms decaying exponential kernel (iii) generates a proxy for the instantaneous output firing rate (in arbitrary units). A CFC analysis of the firing rate (iv, v) provides insight into what could be detected in the dendrites of a downstream layer which is not dependent on the kernel. This analysis was conducted for PC spikes in a θ-ING motif (a) and a θ-PING motif (b). To ensure sufficient spikes for the CFC analysis, both networks were scaled up by a factor of 4—achieved by increasing the number of neurons and projections while reducing the number of synaptic weights. (tiff)Peer reviewe
S1 Table: The role of feedforward and feedback inhibition in modulating theta-gamma cross-frequency interactions in neural circuits
No full textSynaptic weights for Fig 1. The synaptic weight depicted for PC → BC is for the θ-PING, otherwise it is zero. Similarly, the synaptic weight depicted for θ → BC is for the θ-ING, otherwise it is zero. The difference in the order of magnitude between PC → BC and θ → BC is due to differences in the number of presynaptic neurons (80 vs 500) and their activity (0.49Hz vs 8Hz).Peer reviewe
Synaptic parameters of the model [Dataset]
No full textNMDA has an additional scaling factor due to the magnesium block , where [Mg] = 1mM is the concentration of magnesium, and V the membrane potential in mV.Peer reviewe
Different θ-ING connectivities retain negative CFD [Dataset]
No full text(a) The θ-ING motif analyzed in this study is visualized for reference (same as in Fig 1b-v) with the additional CFD of the transmembrane currents at the proximal and distal dendrites. (b) The theta input excites the BC and PC population closer to their somata. (c) The inhibitory population is positioned and projects at the same layer as the theta input.Peer reviewe
Synaptic weights for Fig 3 [Dataset]
No full textAll other synaptic parameters are as in θ-ING (see S1 Table).Peer reviewe
Mean firing rates for BCs (fr,BC(Hz)) and PCs (fr,PC(Hz)) in the θ-ING model for different synaptic weights wi < wii < wiii < wiv in the circuit connection (Conn.) [Dataset]
No full textThe simulations are the same as in Fig 3. Blue-colored rates are associated with motifs that exhibit negative CFD.Peer reviewe
S5 Fig - The role of feedforward and feedback inhibition in modulating theta-gamma cross-frequency interactions in neural circuits [Dataset]
No full textSingle perturbation MI analysis. A single spike is introduced in the proximal dendrite at a predefined time within the interval (1-1.5) s (here at 1 s, indicated by the black dashed line). Panels (a) and (b) show the dynamics for a θ-ING and a θ-PING motif, respectively, both with similar firing rates. Blue lines represent the mean membrane potential at the PC soma (VPC,g) for the unperturbed case, while gray lines show the evolution after the perturbation (VPC,p). Open circles indicate spikes in the perturbed simulations (gray for PCs, brown for BCs), and solid circles represent spikes in the baseline condition (blue for PCs, red for BCs). Since only one perturbation is applied, the resulting encoding value between output and perturbation can be related to the network state at the time of perturbation. (c) Encoding values are plotted against the θ phase of VPC,p, with their histogram overlaid, where 180° represents the trough and 0°/360° the peak. (d) Same as (c), using a high-pass filter cutting of frequencies lower than 20 Hz to capture the gamma activity of both motifs. (e) Same as (d), but for encoding values only when the θ phase is between -90° and 90°, i.e., when the network is more depolarized by the θ input. In both panels (e) and (d) θ-PING MI depends on the γ phase more than θ-ING.Peer reviewe
Synaptic weights for Fig 4 [Dataset]
No full textAs also stated in S1 Table, the difference in the order of magnitude between PC → BC and θ → BC is due to differences in the number of presynaptic neurons (80 vs 500) and their activity (0.49Hz vs 8Hz).Peer reviewe
Synaptic weights for Fig 6 [Dataset]
No full textInteractions among brain rhythms play a crucial role in organizing neuronal firing sequences during specific cognitive functions. In memory formation, the coupling between the phase of the theta rhythm and the amplitude of gamma oscillations has been extensively studied in the hippocampus. Prevailing perspectives suggest that the phase of the slower oscillation modulates the fast activity. However, recent metrics, such as Cross-Frequency Directionality (CFD), indicate that these electrophysiological interactions can be bidirectional. Using a computational model, we demonstrate that feedforward inhibition modeled by a theta-modulated ING (Interneuron Network Gamma) mechanism induces fast-to-slow interactions, while feedback inhibition through a theta-modulated PING (Pyramidal Interneuron Network Gamma) model drives slow-to-fast interactions. Importantly, in circuits combining both feedforward and feedback motifs, as commonly found experimentally, directionality is flexibly modulated by synaptic strength within biologically realistic ranges. A signature of this interaction is that fast-to-slow dominance in feedforward motifs is associated with gamma oscillations of higher frequency, and vice versa. Using previously acquired electrophysiological data from the hippocampus of rats freely navigating in a familiar environment or in a novel one, we show that CFD is dynamically regulated and linked to the frequency of the gamma band, as predicted by the model. Finally, the model attributes each theta-gamma interaction scheme, determined by the balance between feedforward and feedback inhibition, to distinct modes of information transmission and integration, adding computational flexibility. Our results offer a plausible neurobiological interpretation for cross-frequency directionality measurements associated with the activation of different underlying motifs that serve distinct computational needs.Peer reviewe
Same analysis as in Fig 2, but using the external θ drive phase as the θ reference for spiking and CFD calculations [Dataset]
No full textPanels (a) and (b) show the θ phase of PC and BC spiking, respectively. Panel (c) illustrates the CFD for itransm (top) and VPC (bottom). To derive the θ phase of the external population, spikes are passed through a decaying exponential kernel with a 5 ms time constant. Note that when using the external population’s θ phase, the BC phase remains unchanged, while the PC phase shifts significantly due to different offsets. Finally, as the local γ is consistently generated by the external θ driver, the CFD remains positive under all conditions.Peer reviewe
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