Journal of Computational Neuroscience最新文献

Understanding how neurons encode multiple simultaneous stimuli is a fundamental question in neuroscience. We have previously introduced a novel theory of stochastic encoding patterns wherein a neuron's spiking activity dynamically switches among its constituent single-stimulus activity patterns when presented with multiple stimuli (Groh et al., 2024). Here, we present an enhanced, comprehensive statistical testing framework for such "multiplexing". As before, our approach evaluates whether dual-stimulus responses can be accounted for as mixtures of Poissons related to single-stimulus benchmarks. Our enhanced framework improves upon previous methods in two key ways. First, it introduces a stronger set of foils for multiplexing, including an "overreaching" category that captures overdispersed activity patterns unrelated to the single-stimulus benchmarks, reducing false detection of multiplexing. Second, it detects continuous mixtures, potentially indicating faster fluctuations - i.e. at sub-trial timescales - that would have been overlooked before. We utilize a Bayesian inference framework, considering the hypothesis with the highest posterior probability as the winner, and employ the predictive recursion marginal likelihood method for non-parametric estimation of the latent mixing distributions. Reanalysis of previous findings confirms the general observation of fluctuating activity and indicates that fluctuations may well occur on faster timescales than previously suggested. We further confirm that multiplexing is more prevalent for (a) combinations of face stimuli than for faces and non-face objects in the inferotemporal face patch system; and (b) distinct vs fused objects in the primary visual cortex.

Depolarizations that occur after action potentials, known as afterdepolarization potentials or ADPs, are important for neuronal excitability and stimulus evoked transient bursting. Slow inward and fast outward currents underlie the generation of such ADPs with modulation of ADP amplitudes occurring as a result of neuronal morphology. However, the relative contribution and role of these slow inward and fast outward currents in ADP generation is poorly understood in the context of somatic and dendritic localization as well as with varied dendritic properties. Using a two-compartment Hodgkin-Huxley type model of cerebellar stellate cells, the role of somatic and dendritic compartmentalization of ADP associated currents is investigated, revealing that dendritic (rather than somatic) slow inward and fast outward currents are the main contributors to ADP and spike-adding during both brief step current and AMPA current input. Additionally, dendritic size and passive properties of the dendrites were found to be key modulators of ADP amplitude. However, increasing magnitudes of NMDA current input resulted in nonmonotonic spike-adding in a manner dependent on dendritic Ca²⁺ influx and Ca²⁺ activated K⁺ currents, which was found to arise from tight regulation of stimulus evoked transient bursting through positive feedback on action potential generation by dendritic Ca²⁺ and subsequent negative feedback through Ca²⁺ activated K⁺ currents. This novel mechanism of ADPs and spike-adding regulation highlights the role of currents with slow timescales in ADPs, stimulus evoked transient bursting and neuronal excitability with implications for Ca²⁺ dependent synaptic plasticity and neuromodulation.

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.

This paper presents the results of combining (i) theoretical analysis regarding connections between the orientation selectivity and the elongation of receptive fields for the affine Gaussian derivative model with (ii) biological measurements of orientation selectivity in the primary visual cortex to investigate if (iii) the receptive fields can be regarded as spanning a variability in the degree of elongation. From an in-depth theoretical analysis of idealized models for the receptive fields of simple and complex cells in the primary visual cortex, we established that the orientation selectivity becomes more narrow with increasing elongation of the receptive fields. Combined with previously established biological results, concerning broad vs. sharp orientation tuning of visual neurons in the primary visual cortex, as well as previous experimental results concerning distributions of the resultant of the orientation selectivity curves for simple and complex cells, we show that these results are consistent with the receptive fields spanning a variability over the degree of elongation of the receptive fields. We also show that our principled theoretical model for visual receptive fields leads to qualitatively similar types of deviations from a uniform histogram of the resultant descriptor of the orientation selectivity curves for simple cells, as can be observed in the results from biological experiments. To firmly investigate the validity of the underlying working hypothesis, we finally formulate a set of testable predictions for biological experiments, to characterize the predicted systematic variability in the elongation over the orientation maps in higher mammals, and its relations to the pinwheel structure.

Understanding the mechanism of accumulating evidence over time in deliberate decision-making is crucial for both humans and animals. While numerous models have been proposed over the past few decades to characterize the temporal weighting of evidence, the dynamical principle governing the neural circuits in decision making remain elusive. In this study, we proposed a solvable rank-1 neural circuit model to address this problem. We first derived an analytical expression for integration kernel, a key quantity describing how sensory evidence at different time points is weighted with respect to the final decision. Based on this expression, we illustrated that how the dynamics introduced in the auxiliary space-namely, a subspace orthogonal to the decision variable-modulates the flow fields of decision variable through a gain modulation mechanism, resulting in a variety of integration kernel types, including not only monotonic ones (recency and primacy) but also non-monotonic ones (convex and concave). Furthermore, we quantitatively validated that integration kernel shapes can be understood from dynamical landscapes and non-monotonic temporal weighting reflects topological transitions in the landscape. Additionally, we showed that training on networks with non-optimal weighting leads to convergence toward optimal weighting. Finally, we demonstrate that rank-1 connectivity induces symmetric competition to generate pitchfork bifurcation. In summary, we present a solvable neural circuit model that unifies diverse types of temporal weighting, providing an intriguing link between non-monotonic integration kernel structure and topological transitions of dynamical landscape.