Journal of Computational Neuroscience最新文献

The human brain is a complex adaptive system characterized by dynamic processes operating across multiple spatio-temporal scales. Capturing these dynamics requires computational models that can integrate different levels of resolution. In this work we present a multiscale co-simulation framework that couples whole-brain modeling with a detailed point-neuron model of the human hippocampal CA1 region. We used a high-resolution implementation of the "The Virtual Brain" (TVB), in which cortical surface mesh vertices are embedded with the Spatial Epileptor Model (SEM). At the microscale, the CA1 model captures neuronal activity at micrometer spatial and sub-millisecond temporal resolution. This integration enables the simulation of macroscale epileptic dynamics with microscale neuronal precision within anatomically grounded brain regions, facilitating cross-scale communication. These results demonstrate the potential of this approach to advance mechanism-driven, personalized medicine in clinical neuroscience.

Stick insect stepping patterns have been studied for insights about locomotor rhythm generation and control, because the underlying neural system is relatively accessible experimentally and produces a variety of rhythmic outputs. Harnessing the experimental identification of effective interactions among neuronal units involved in stick insect stepping pattern generation, previous studies proposed computational models simulating aspects of stick insect locomotor activity. While these models generate diverse stepping patterns and transitions between them, there has not been an in-depth analysis of the mechanisms underlying their dynamics. In this study, we focus on modeling rhythm generation by the neurons associated with the protraction-retraction, levation-depression, and extension-flexion antagonistic muscle pairs of the mesothoracic (middle) leg of stick insects. Our model features a reduced central pattern generator (CPG) circuit for each joint and includes synaptic interactions among the CPGs; we also consider extensions such as the inclusion of motoneuron pools controlled by the CPG components. The resulting network is described by an 18-dimensional system of ordinary differential equations. We use fast-slow decomposition, projection into interacting phase planes, and a heavy reliance on input-dependent nullclines to analyze this model. Specifically, we identify and eludicate dynamic mechanisms capable of generating a stepping rhythm, with a sequence of biologically constrained phase relationships, in a three-joint stick insect limb model. Furthermore, we explain the robustness to parameter changes and tunability of these patterns. In particular, the model allows us to identify possible mechanisms by which neuromodulatory and top-down effects could tune stepping pattern output frequency.

Visual interneurons come in many different flavors, representing luminance changes at one location as ON or OFF signals with different dynamics, ranging from purely sustained to sharply transient responses. While the functional relevance of this representation for subsequent computations like direction-selective motion detection is well understood, the mechanisms by which these differences in dynamics arise are unclear. Here, I study this question in the fly optic lobe. Taking advantage of the known connectome I simulate a network of five adjacent optical columns each comprising 65 different cell types. Each neuron is modeled as an electrically compact single compartment, conductance-based element that receives input from other neurons within its column and from its neighboring columns according to the intra- and inter-columnar connectivity matrix. The sign of the input is determined according to the known transmitter type of the presynaptic neuron and the receptor on the postsynaptic side. In addition, some of the neurons are given voltage-dependent conductances known from the fly transcriptome. As free parameters, each neuron has an input and an output gain, applied to all its input and output synapses, respectively. The parameters are adjusted such that the spatio-temporal receptive field properties of 13 out of the 65 simulated neurons match the experimentally determined ones as closely as possible. Despite the fact that all neurons have identical leak conductance and membrane capacitance, this procedure leads to a surprisingly good fit to the data, where specific neurons respond transiently while others respond in a sustained way to luminance changes. This fit critically depends on the presence of an H-current in some of the first-order interneurons, i.e., lamina cells L1 and L2: turning off the H-current eliminates the transient response nature of many neurons leaving only sustained responses in all of the examined interneurons. I conclude that the diverse dynamic response behavior of the columnar neurons in the fly optic lobe starts in the lamina and is created by their different intrinsic membrane properties. I predict that eliminating the hyperpolarization-activated current by RNAi should strongly affect the dynamics of many medulla neurons and, consequently, also higher-order functions depending on them like direction-selectivity in T4 and T5 neurons.

Neuronal frequency filters can be thought of as constituent building blocks underlying the ability of neuronal systems to process information, generate rhythms and perform computations. How neuronal filters are generated by the concerted activity of a multiplicity of processes (e.g., electric circuit, history-dependent) and interacting time scales within and across levels of neuronal network organization is poorly understood. In this paper, we use mathematical modeling, numerical simulations and analytical calculations of the postsynaptic response to presynaptic spike trains to address this issue in a basic feedforward network motif in the presence of synaptic short-term plasticity (STP, depression and facilitation). The network motif consists of a presynaptic spike-train, a postsynaptic passive cell, and an excitatory (AMPA) chemical synapse. The dynamics of each network component are controlled by one or more time scales. We explain the mechanisms by which the participating time scales shape the neuronal filters at the (i) synaptic update level (the target of the synaptic variable in response to presynaptic spikes), which is shaped by STP, (ii) the synaptic level, and (iii) the postsynaptic membrane potential (PSP) level. We focus on three metrics that gives rise to three types of profiles (curves of the corresponding metrics as a function of the spike-train input frequency or firing rate): (i) peak profiles, (ii) peak-to-trough amplitude profiles, and (iii) phase profiles. The effects of STP are present at the synaptic update level and are communicated to the synaptic level where they interact with the synaptic time scales. The PSP filters result from the interaction between these variables and time scales and the biophysical properties and time scales of the postsynaptic cell. Band-pass filters (BPFs) result from a combination of low-pass filters (LPFs) and high-pass filters (HPFs) operating at the same or different levels of organization. PSP BPFs can be inherited from the synaptic level (STP-mediated BPFs) or they can be generated across levels of organization due to the interaction between (i) a synaptic LPF and the PSP summation-mediated HPF (PSP peaks), and (ii) a synaptic HPF and the PSP summation-mediated LPF (PSP amplitude). These types of BPFs persist in response to more realistic presynaptic spike trains: jittered (randomly perturbed) periodic spike trains and Poisson-distributed spike trains. The response variability is frequency-dependent and is controlled by STP in a non-monotonic frequency manner. The results and lessons learned from the investigation of this basic network motif are a necessary step for the construction of a framework to analyze the mechanisms of generation of neuronal filters in networks with more complex architectures and a variety of interacting cellular, synaptic and plasticity time scales.

The paper addresses the problem of parameter estimation (or identification) in dynamical networks composed of an arbitrary number of FitzHugh-Nagumo neuron models with diffusive couplings between each other. It is assumed that only the membrane potential of each model is measured, while the other state variable and all derivatives remain unmeasured. Additionally, constant potential measurement errors in the membrane potential due to sensor imprecision are considered. To solve this problem, firstly, the original FitzHugh-Nagumo network is transformed into a linear regression model, where the regressors are obtained by applying a filter-differentiator to specific combinations of the measured variables. Secondly, the speed-gradient method is applied to this linear model, leading to the design of an identification algorithm for the FitzHugh-Nagumo neural network. Sufficient conditions for the asymptotic convergence of the parameter estimates to their true values are derived for the proposed algorithm. Parameter estimation for some networks is demonstrated through computer simulation. The results confirm that the sufficient conditions are satisfied in the numerical experiments conducted. Furthermore, the algorithm's capabilities for adjusting the identification accuracy and time are investigated. The proposed approach has potential applications in nervous system modeling, particularly in the context of human brain modeling. For instance, EEG signals could serve as the measured variables of the network, enabling the integration of mathematical neural models with empirical data collected by neurophysiologists.

Transcranial alternating current stimulation (tACS) enables non-invasive modulation of brain activity, holding promise for cognitive research and clinical applications. However, it remains unclear how the spiking activity of cortical neurons is modulated by specific electric field (E-field) distributions. Here, we use a multi-scale computational framework that integrates an anatomically accurate head model with morphologically realistic neuron models to simulate the responses of layer 5 pyramidal cells (L5 PCs) to the E-fields generated by conventional M1-SO tACS. Neural entrainment is quantified by calculating the phase-locking value (PLV) and preferred phase (PPh). We find that the tACS-induced E-field distributions across the L5 surface of interest (SOI) are heterogeneous, resulting in diverse neural entrainment of L5 PCs due to their sensitivities to the direction and intensity of the E-fields. Both PLV and PPh follow a smooth cosine dependency on the E-field polar angle, with minimal sensitivity to the azimuthal angle. PLV exhibits a positive linear dependence on the E-field intensity. However, PPh either increases or decreases logarithmically with E-field intensity that depends on the E-field direction. Correlation analysis reveals that neural entrainment can be largely explained by the normal component of the E-field or by somatic polarization, especially for E-field directed outward relative to the cortical surface. Moreover, cell morphology plays a crucial role in shaping the diverse neural entrainment to tACS. Although the uniform E-field extracted at the soma provides a good approximation for modeling tACS at the cellular level, the non-uniform E-field distribution should be considered for investigating more accurate cellular mechanisms of tACS. These findings highlight the crucial roles of heterogeneous E-field distributions, cell morphology, and E-field non-uniformity in modulating neuronal spiking activity by tACS in realistic neuroanatomy, deepening our understanding of the cellular mechanism underlying tACS. Our work bridges macroscopic brain stimulation with microscopic neural activity, which benefits the development of brain models and derived clinical applications relying on model-driven brain stimulation with tACS-induced weak E-fields.