Journal of Computational Neuroscience最新文献

This paper presents an in-depth theoretical analysis of the orientation selectivity properties of simple cells and complex cells, that can be well modelled by the generalized Gaussian derivative model for visual receptive fields, with the purely spatial component of the receptive fields determined by oriented affine Gaussian derivatives for different orders of spatial differentiation. A detailed mathematical analysis is presented for the three different cases of either: (i) purely spatial receptive fields, (ii) space-time separable spatio-temporal receptive fields and (iii) velocity-adapted spatio-temporal receptive fields. Closed-form theoretical expressions for the orientation selectivity curves for idealized models of simple and complex cells are derived for all these main cases, and it is shown that the orientation selectivity of the receptive fields becomes more narrow, as a scale parameter ratio $\kappa$ , defined as the ratio between the scale parameters in the directions perpendicular to vs. parallel with the preferred orientation of the receptive field, increases. It is also shown that the orientation selectivity becomes more narrow with increasing order of spatial differentiation in the underlying affine Gaussian derivative operators over the spatial domain. A corresponding theoretical orientation selectivity analysis is also presented for purely spatial receptive fields according to an affine Gabor model, showing that: (i) the orientation selectivity becomes more narrow when making the receptive fields wider in the direction perpendicular to the preferred orientation of the receptive field; while (ii) an additional degree of freedom in the affine Gabor model does, however, also strongly affect the orientation selectivity properties.

To model the dynamics of neuron membrane excitability many models can be considered, from the most biophysically detailed to the highest level of phenomenological description. Recent works at the single neuron level have shown the importance of taking into account the evolution of slow variables such as ionic concentration. A reduction of such a model to models of the integrate-and-fire family is interesting to then go to large network models. In this paper, we introduce a way to consider the impairment of ionic regulation by adding a third, slow, variable to the adaptive Exponential integrate-and-fire model (AdEx). We then implement and simulate a network including this model. We find that this network was able to generate normal and epileptic discharges. This model should be useful for the design of network simulations of normal and pathological states.

Hippocampal representations of space and time seem to share a common coding scheme characterized by neurons with bell-shaped tuning curves called place and time cells. The properties of the tuning curves are consistent with Weber's law, such that, in the absence of visual inputs, width scales with the peak time for time cells and with distance for place cells. Building on earlier computational work, we examined how neurons with such properties can emerge through self-supervised learning. We found that a network based on autoencoders can, given a particular inputs and connectivity constraints, produce scale-invariant time cells. When the animal's velocity modulates the decay rate of the leaky integrators, the same network gives rise to scale-invariant place cells. Importantly, this is not the case when velocity is fed as a direct input to the leaky integrators, implying that weight modulation by velocity might be critical for developing scale-invariant spatial receptive fields. Finally, we demonstrated that after training, scale-invariant place cells emerge in environments larger than those used during training. Taken together, these findings bring us closer to understanding the emergence of neurons with bell-shaped tuning curves in the hippocampus and highlight the critical role of velocity modulation in the formation of scale-invariant place cells.

Traveling waves of neuronal spiking activity are commonly observed across the brain, but their intrinsic function is still a matter of investigation. Experiments suggest that they may be valuable in the consolidation of memory or learning, indicating that consideration of traveling waves in the presence of plasticity might be important. A possible outcome of this consideration is that the synaptic pathways, necessary for the propagation of these waves, will be modified by the waves themselves. This will create a feedback loop where both the traveling waves and the strengths of the available synaptic pathways will change. To computationally investigate this, we model a sheet of cortical tissue by considering a quasi two-dimensional network of model neurons locally connected with plastic synaptic weights using Spike-Timing Dependent Plasticity (STDP). By using different stimulation conditions (central, stochastic, and alternating stimulation), we demonstrate that starting from a random network, traveling waves with STDP will form and strengthen propagation pathways. With progressive formation of traveling waves, we observe increases in synaptic weight along the direction of wave propagation, increases in propagation speed when pathways are strengthened over time, and an increase in the local order of synaptic weights. We also present evidence that the interaction between traveling waves and plasticity can serve as a mechanism of network-wide competition between available pathways. With an improved understanding of the interactions between traveling waves and synaptic plasticity, we can approach a fuller understanding of mechanisms of learning, computation, and processing within the brain.

Childhood absence epilepsy (CAE) is a paediatric generalized epilepsy disorder with a confounding feature of resolving in adolescence in a majority of cases. In this study, we modelled how the small-scale (synapse-level) effect of progesterone metabolite allopregnanolone induces a large-scale (network-level) effect on a thalamocortical circuit associated with this disorder. In particular, our goal was to understand the role of sex steroid hormones in the spontaneous remission of CAE. The conductance-based computational model consisted of single-compartment cortical pyramidal, cortical interneurons, thalamic reticular and thalamocortical relay neurons, each described by a set of ordinary differential equations. Excitatory and inhibitory synapses were mediated by AMPA, GABAa and GABAb receptors. The model was implemented using the NetPyne modelling tool and the NEURON simulator. It was found that the action of allopregnanolone (ALLO) on individual GABAa-receptor mediated synapses can have an ameliorating effect on spike-wave discharges (SWDs) associated with absence seizures. This effect is region-specific and most significant in the thalamus, particularly the synapses between thalamic reticular neurons. The remedying effect of allopregnanolone on SWDs may possibly be true only for individuals that are predisposed to remission due to intrinsic connectivity differences or differences in tonic inhibition. These results are a useful first-step and prescribe directions for further investigation into the role of ALLO together with these differences to distinguish between models for CAE-remitting and non-remitting individuals.

Transcranial direct current stimulation (tDCS) generates a weak electric field (EF) within the brain, which induces opposite polarization in the soma and distal dendrite of cortical pyramidal neurons. The somatic polarization directly affects the spike timing, and dendritic polarization modulates the synaptically evoked dendritic activities. Ca²⁺ spike, the most dramatic dendritic activity, is crucial for synaptic integration and top-down signal transmission, thereby indirectly influencing the output spikes of pyramidal cells. Nevertheless, the role of dendritic Ca²⁺ spike in the modulation of neural spike timing with tDCS remains largely unclear. In this study, we use morphologically and biophysically realistic models of layer 5 pyramidal cells (L5 PCs) to simulate the dendritic Ca²⁺ spike and somatic Na⁺ spike in response to distal dendritic synaptic inputs under weak EF stimulation. Our results show that weak EFs modulate the spike timing through the modulation of dendritic Ca²⁺ spike and somatic polarization, and such field effects are dependent on synaptic inputs. At weak synaptic inputs, the spike timing is advanced due to the facilitation of dendritic Ca²⁺ spike by field-induced dendritic depolarization. Conversely, it is delayed by field-induced dendritic hyperpolarization. In this context, the Ca²⁺ spike exhibits heightened sensitivity to weak EFs, thereby governing the changes in spike timing. At strong synaptic inputs, somatic polarization dominates the changes in spike timing due to the decreased sensitivity of Ca²⁺ spike to EFs. Consequently, the spike timing is advanced/delayed by field-induced somatic depolarization/hyperpolarization. Moreover, EFs have significant effects on the changes in the timing of somatic spike and Ca²⁺ spike when synaptic current injection coincides with the onset of EFs. Field effects on spike timing follow a cosine dependency on the field polar angle, with maximum effects in the field direction parallel to the somato-dendritic axis. Furthermore, our results are robust to morphological and biological diversity. These findings clarify the modulation of spike timing with weak EFs and highlight the crucial role of dendritic Ca²⁺ spike. These predictions shed light on the neural basis of tDCS and should be considered when understanding the effect of tDCS on population dynamics and cognitive behavior.

Synaptic connections in neuronal circuits are modulated by pre- and post-synaptic spiking activity. Previous theoretical work has studied how such Hebbian plasticity rules shape network connectivity when firing rates are constant, or slowly varying in time. However, oscillations and fluctuations, which can arise through sensory inputs or intrinsic brain mechanisms, are ubiquitous in neuronal circuits. Here we study how oscillatory and fluctuating inputs shape recurrent network connectivity given a temporally asymmetric plasticity rule. We do this analytically using a separation of time scales approach for pairs of neurons, and then show that the analysis can be extended to understand the structure in large networks. In the case of oscillatory inputs, the resulting network structure is strongly affected by the phase relationship between drive to different neurons. In large networks, distributed phases tend to lead to hierarchical clustering. The analysis for stochastic inputs reveals a rich phase plane in which there is multistability between different possible connectivity motifs. Our results may be of relevance for understanding the effect of sensory-driven inputs, which are by nature time-varying, on synaptic plasticity, and hence on learning and memory.

We study the impact of bursts on spike statistics and neural signal transmission. We propose a stochastic burst algorithm that is applied to a burst-free spike train and adds a random number of temporally-jittered burst spikes to each spike. This simple algorithm ignores any possible stimulus-dependence of bursting but allows to relate spectra and signal-transmission characteristics of burst-free and burst-endowed spike trains. By averaging over the various statistical ensembles, we find a frequency-dependent factor connecting the linear and also the second-order susceptibility of the spike trains with and without bursts. The relation between spectra is more complicated: besides a frequency-dependent multiplicative factor it also involves an additional frequency-dependent offset. We confirm these relations for the (burst-free) spike trains of a stochastic integrate-and-fire neuron and identify frequency ranges in which the transmission is boosted or diminished by bursting. We then consider bursty spike trains of electroreceptor afferents of weakly electric fish and approach the role of burst spikes as follows. We compare the spectral statistics of the bursty spike train to (i) that of a spike train with burst spikes removed and to (ii) that of the spike train in (i) endowed by bursts according to our algorithm. Significant spectral features are explained by our signal-independent burst algorithm, e.g. the burst-induced boosting of the nonlinear response. A difference is seen in the information transfer for the original bursty spike train and our burst-endowed spike train. Our algorithm is thus helpful to identify different effects of bursting.