Identifying Effective Connectivity between Stochastic Neurons with Variable-Length Memory Using a Transfer Entropy Rate Estimator.

Information theory explains how systems encode and transmit information. This article examines the neuronal system, which processes information via neurons that react to stimuli and transmit electrical signals. Specifically, we focus on transfer entropy to measure the flow of information between sequences and explore its use in determining effective neuronal connectivity. We analyze the causal relationships between two discrete time series, X:=Xt:t∈Z and Y:=Yt:t∈Z, which take values in binary alphabets. When the bivariate process (X,Y) is a jointly stationary ergodic variable-length Markov chain with memory no larger than k, we demonstrate that the null hypothesis of the test-no causal influence-requires a zero transfer entropy rate. The plug-in estimator for this function is identified with the test statistic of the log-likelihood ratios. Since under the null hypothesis, this estimator follows an asymptotic chi-squared distribution, it facilitates the calculation of p-values when applied to empirical data. The efficacy of the hypothesis test is illustrated with data simulated from a neuronal network model, characterized by stochastic neurons with variable-length memory. The test results identify biologically relevant information, validating the underlying theory and highlighting the applicability of the method in understanding effective connectivity between neurons.

Modeling Hippocampal CA1 Gabaergic Synapses of Audiogenic Rats.

Wistar Audiogenic Rats (WARs) are genetically susceptible to sound-induced seizures that start in the brainstem and, in response to repetitive stimulation, spread to limbic areas, such as hippocampus. Analysis of the distribution of interevent intervals of GABAergic inhibitory postsynaptic currents (IPSCs) in CA1 pyramidal cells showed a monoexponential trend in Wistar rats, suggestive of a homogeneous population of synapses, but a biexponential trend in WARs. Based on this, we hypothesize that there are two populations of GABAergic synaptic release sites in CA1 pyramidal neurons from WARs. To address this hypothesis, we used a well-established neuronal computational model of a CA1 pyramidal neuron previously developed to replicate physiological properties of these cells. Our simulations replicated the biexponential trend only when we decreased the release frequency of synaptic currents by a factor of six in at least 40% of distal synapses. Our results suggest that almost half of the GABAergic synapses of WARs have a drastically reduced spontaneous release frequency. The computational model was able to reproduce the temporal dynamics of GABAergic inhibition that could underlie susceptibility to the spread of seizures.

Optimal Interplay between Synaptic Strengths and Network Structure Enhances Activity Fluctuations and Information Propagation in Hierarchical Modular Networks.

In network models of spiking neurons, the joint impact of network structure and synaptic parameters on activity propagation is still an open problem. Here, we use an information-theoretical approach to investigate activity propagation in spiking networks with a hierarchical modular topology. We observe that optimized pairwise information propagation emerges due to the increase of either (i) the global synaptic strength parameter or (ii) the number of modules in the network, while the network size remains constant. At the population level, information propagation of activity among adjacent modules is enhanced as the number of modules increases until a maximum value is reached and then decreases, showing that there is an optimal interplay between synaptic strength and modularity for population information flow. This is in contrast to information propagation evaluated among pairs of neurons, which attains maximum value at the maximum values of these two parameter ranges. By examining the network behavior under the increase of synaptic strength and the number of modules, we find that these increases are associated with two different effects: (i) the increase of autocorrelations among individual neurons and (ii) the increase of cross-correlations among pairs of neurons. The second effect is associated with better information propagation in the network. Our results suggest roles that link topological features and synaptic strength levels to the transmission of information in cortical networks.

Firing properties of ventral medullary respiratory neurons in sino-aortic denervated rats.

NEW FINDINGS: What is the central question of this study? After sino-aortic denervation (SAD), rats present normal levels of mean arterial pressure (MAP), high MAP variability and changes in breathing. However, mechanisms involved in SAD-induced respiratory changes and their impact on the modulation of sympathetic activity remain unclear. Herein, we characterized the firing frequency of medullary respiratory neurons after SAD. What is the main finding and its importance? Sino-aortic denervation-induced prolonged inspiration was associated with a reduced interburst frequency of pre-inspiratory/inspiratory neurons and an increased long-term variability of late inspiratory neurons, but no changes were observed in the ramp-inspiratory and post-inspiratory neurons. This imbalance in the respiratory network might contribute to the modulation of sympathetic activity after SAD. ABSTRACT: In previous studies, we documented that after sino-aortic denervation (SAD) in rats there are significant changes in the breathing pattern, but no significant changes in sympathetic activity and mean arterial pressure compared with sham-operated rats. However, the neural mechanisms involved in the respiratory changes after SAD and the extent to which they might contribute to the observed normal sympathetic activity and mean arterial pressure remain unclear. Here, we hypothesized that after SAD, rats present with changes in the firing frequency of the ventral medullary inspiratory and post-inspiratory neurons. To test this hypothesis, male Wistar rats underwent SAD or sham surgery and 3 days later were surgically prepared for an in situ experiment. The duration of inspiration significantly increased in SAD rats. During inspiration, the total firing frequency of ramp-inspiratory, pre-inspiratory/inspiratory and late-inspiratory neurons was not different between groups. During post-inspiration, the total firing frequency of post-inspiratory neurons was also not different between groups. Furthermore, the data demonstrate a reduced interburst frequency of pre-inspiratory/inspiratory neurons and an increased long-term variability of late-inspiratory neurons in SAD compared with sham-operated rats. These findings indicate that the SAD-induced prolongation of inspiration was not accompanied by alterations in the total firing frequency of the ventral medullary respiratory neurons, but it was associated with changes in the long-term variability of late-inspiratory neurons. We suggest that the timing imbalance in the respiratory network in SAD rats might contribute to the modulation of presympathetic neurons after removal of baroreceptor afferents.

Dynamics of spontaneous activity in random networks with multiple neuron subtypes and synaptic noise : Spontaneous activity in networks with synaptic noise.

Spontaneous cortical population activity exhibits a multitude of oscillatory patterns, which often display synchrony during slow-wave sleep or under certain anesthetics and stay asynchronous during quiet wakefulness. The mechanisms behind these cortical states and transitions among them are not completely understood. Here we study spontaneous population activity patterns in random networks of spiking neurons of mixed types modeled by Izhikevich equations. Neurons are coupled by conductance-based synapses subject to synaptic noise. We localize the population activity patterns on the parameter diagram spanned by the relative inhibitory synaptic strength and the magnitude of synaptic noise. In absence of noise, networks display transient activity patterns, either oscillatory or at constant level. The effect of noise is to turn transient patterns into persistent ones: for weak noise, all activity patterns are asynchronous non-oscillatory independently of synaptic strengths; for stronger noise, patterns have oscillatory and synchrony characteristics that depend on the relative inhibitory synaptic strength. In the region of parameter space where inhibitory synaptic strength exceeds the excitatory synaptic strength and for moderate noise magnitudes networks feature intermittent switches between oscillatory and quiescent states with characteristics similar to those of synchronous and asynchronous cortical states, respectively. We explain these oscillatory and quiescent patterns by combining a phenomenological global description of the network state with local descriptions of individual neurons in their partial phase spaces. Our results point to a bridge from events at the molecular scale of synapses to the cellular scale of individual neurons to the collective scale of neuronal populations.

Interplay of activation kinetics and the derivative conductance determines resonance properties of neurons.

In a neuron with hyperpolarization activated current (I_{h}), the correct input frequency leads to an enhancement of the output response. This behavior is known as resonance and is well described by the neuronal impedance. In a simple neuron model we derive equations for the neuron's resonance and we link its frequency and existence with the biophysical properties of I_{h}. For a small voltage change, the component of the ratio of current change to voltage change (dI/dV) due to the voltage-dependent conductance change (dg/dV) is known as derivative conductance (G_{h}^{Der}). We show that both G_{h}^{Der} and the current activation kinetics (characterized by the activation time constant τ_{h}) are mainly responsible for controlling the frequency and existence of resonance. The increment of both factors (G_{h}^{Der} and τ_{h}) greatly contributes to the appearance of resonance. We also demonstrate that resonance is voltage dependent due to the voltage dependence of G_{h}^{Der}. Our results have important implications and can be used to predict and explain resonance properties of neurons with the I_{h} current.

Self-Consistent Scheme for Spike-Train Power Spectra in Heterogeneous Sparse Networks.

Recurrent networks of spiking neurons can be in an asynchronous state characterized by low or absent cross-correlations and spike statistics which resemble those of cortical neurons. Although spatial correlations are negligible in this state, neurons can show pronounced temporal correlations in their spike trains that can be quantified by the autocorrelation function or the spike-train power spectrum. Depending on cellular and network parameters, correlations display diverse patterns (ranging from simple refractory-period effects and stochastic oscillations to slow fluctuations) and it is generally not well-understood how these dependencies come about. Previous work has explored how the single-cell correlations in a homogeneous network (excitatory and inhibitory integrate-and-fire neurons with nearly balanced mean recurrent input) can be determined numerically from an iterative single-neuron simulation. Such a scheme is based on the fact that every neuron is driven by the network noise (i.e., the input currents from all its presynaptic partners) but also contributes to the network noise, leading to a self-consistency condition for the input and output spectra. Here we first extend this scheme to homogeneous networks with strong recurrent inhibition and a synaptic filter, in which instabilities of the previous scheme are avoided by an averaging procedure. We then extend the scheme to heterogeneous networks in which (i) different neural subpopulations (e.g., excitatory and inhibitory neurons) have different cellular or connectivity parameters; (ii) the number and strength of the input connections are random (Erdos-Rényi topology) and thus different among neurons. In all heterogeneous cases, neurons are lumped in different classes each of which is represented by a single neuron in the iterative scheme; in addition, we make a Gaussian approximation of the input current to the neuron. These approximations seem to be justified over a broad range of parameters as indicated by comparison with simulation results of large recurrent networks. Our method can help to elucidate how network heterogeneity shapes the asynchronous state in recurrent neural networks.

Non-Decaying postsynaptics potentials and delayed spikes in hippocampal pyramidal neurons generated by a zero slope conductance created by the persistent Na+ current.

The negative slope conductance created by the persistent sodium current (INaP) prolongs the decay phase of excitatory postsynaptic potentials (EPSPs). In a recent study, we demonstrated that this effect was due to an increase of the membrane time constant. When the negative slope conductance opposes completely the positive slope conductances of the other currents it creates a zero slope conductance region. In this region the membrane time constant is infinite and the decay phase of the EPSPs is virtually absent. Here we show that non-decaying EPSPs are present in CA1 hippocampal pyramidal cells in the zero slope conductance region, in the suprathreshold range of membrane potential. Na+ channel block with tetrodotoxin abolishes the non-decaying EPSPs. Interestingly, the non-decaying EPSPs are observed only in response to artificial excitatory postsynaptic currents (aEPSCs) of small amplitude, and not in response to aEPSCs of big amplitude. We also observed concomitantly delayed spikes with long latencies and high variability only in response to small amplitude aEPSCs. Our results showed that in CA1 pyramidal neurons INaP creates non-decaying EPSPs and delayed spikes in the subthreshold range of membrane potentials, which could potentiate synaptic integration of synaptic potentials coming from distal regions of the dendritic tree.