The effects of synaptic noise on measurements of evoked excitatory postsynaptic response amplitudes

Prolonged time course of population excitatory postsynaptic potentials in motoneurons of chronic stroke survivors

Hyperexcitability of spinal motoneurons may contribute to muscular hypertonia after hemispheric stroke. The origins of this hyperexcitability are not clear, but we hypothesized that prolongation of the Ia excitatory postsynaptic potential (EPSP) in spastic motoneurons may be one potential mechanism, by enabling more effective temporal summation of Ia EPSPs, making action potential initiation easier. Thus, the purpose of this study is to quantify the time course of putative EPSPs in spinal motoneurons of chronic stroke survivors. To estimate the EPSP time course, a pair of low-intensity electrical stimuli was delivered sequentially to the median nerve in seven hemispheric stroke survivors and in six intact individuals, to induce an H-reflex response from the flexor carpi radialis muscle. H-reflex response probability was then used to quantify the time course of the underlying EPSPs in the motoneuron pool. A population EPSP estimate was then derived, based on the probability of evoking an H-reflex from the second test stimulus in the absence of a reflex response to the first conditioning stimulus. Our experimental results showed that in six of seven hemispheric stroke survivors, the apparent rate of decay of the population EPSP was markedly slower in spastic compared with contralateral (stroke) and intact motoneuron pools. There was no significant difference in EPSP time course between the contralateral side of stroke survivors and control subject muscles. We propose that one potential mechanism for hyperexcitability of spastic motoneurons in chronic stroke survivors may be associated with this prolongation of the Ia EPSP time course. Our subthreshold double-stimulation approach could provide a noninvasive tool for quantifying the time course of EPSPs in both healthy and pathological conditions. NEW & NOTEWORTHY Spastic motoneurons in stroke survivors showed a prolonged Ia excitatory postsynaptic potential (EPSP) time course compared with contralateral and intact motoneurons, suggesting that one potential mechanism for hyperexcitability of spastic motoneurons in chronic stroke survivors may be associated with this prolongation of the Ia EPSP time course.

Modularity Induced Gating and Delays in Neuronal Networks

Neural networks, despite their highly interconnected nature, exhibit distinctly localized and gated activation. Modularity, a distinctive feature of neural networks, has been recently proposed as an important parameter determining the manner by which networks support activity propagation. Here we use an engineered biological model, consisting of engineered rat cortical neurons, to study the role of modular topology in gating the activity between cell populations. We show that pairs of connected modules support conditional propagation (transmitting stronger bursts with higher probability), long delays and propagation asymmetry. Moreover, large modular networks manifest diverse patterns of both local and global activation. Blocking inhibition decreased activity diversity and replaced it with highly consistent transmission patterns. By independently controlling modularity and disinhibition, experimentally and in a model, we pose that modular topology is an important parameter affecting activation localization and is instrumental for population-level gating by disinhibition.

The capacity to transmit information between connected parts of a neuronal network is fundamental to its function. The organization of network connections (the topology of the network) is therefore expected to play an important role in determining network transmission. Since modular topology characterizes many brain circuits on multiple scales, investigating the role of modularity in activity gating is clearly desirable. By engineering such modular networks in vitro, we were able to perform such an investigation. Under these experimental conditions, we can independently control the degree of modularity, as well as inhibition in the network. We show that a combination of these two properties is highly beneficial from a communication perspective. Namely, it equips connected modules and large modular networks with the capacity to gate and temporally coordinate activity between the different parts of the network.

Synaptic noise and physiological coupling generate high-frequency oscillations in a hippocampal computational model

There is great interest in the role of coherent oscillations in the brain. In some cases, high-frequency oscillations (HFOs) are integral to normal brain function, whereas at other times they are implicated as markers of epileptic tissue. Mechanisms underlying HFO generation, especially in abnormal tissue, are not well understood. Using a physiological computer model of hippocampus, we investigate random synaptic activity (noise) as a potential initiator of HFOs. We explore parameters necessary to produce these oscillations and quantify the response using the tools of stochastic resonance (SR) and coherence resonance (CR). As predicted by SR, when noise was added to the network the model was able to detect a subthreshold periodic signal. Addition of basket cell interneurons produced two novel SR effects: 1) improved signal detection at low noise levels and 2) formation of coherent oscillations at high noise that were entrained to harmonics of the signal frequency. The periodic signal was then removed to study oscillations generated only by noise. The combined effects of network coupling and synaptic noise produced coherent, periodic oscillations within the network, an example of CR. Our results show that, under normal coupling conditions, synaptic noise was able to produce gamma (30-100 Hz) frequency oscillations. Synaptic noise generated HFOs in the ripple range (100-200 Hz) when the network had parameters similar to pathological findings in epilepsy: increased gap junctions or recurrent synaptic connections, loss of inhibitory interneurons such as basket cells, and increased synaptic noise. The model parameters that generated these effects are comparable with published experimental data. We propose that increased synaptic noise and physiological coupling mechanisms are sufficient to generate gamma oscillations and that pathologic changes in noise and coupling similar to those in epilepsy can produce abnormal ripples.

Membrane targeting in secretion

Regulated secretion and exocytosis require the selective packaging of regulated secretory proteins in secretory storage organelles and the controlled docking and fusion of these organelles with the plasma membrane. Secretory granule biogenesis involves sorting of secretory proteins and membrane components both at the level of the trans-Golgi network and the immature secretory granule. Sorting is thought to be mediated by selective protein aggregation and the interaction of these proteins with specific membrane domains. There is now considerable interest in the understanding of the complex lipid-protein and protein-protein interactions at the trans-Golgi network and the granule membrane. A role for lipid microdomains and associated sorting receptors in membrane targeting and granule formation is vividly discussed for (neuro)endocrine cells. In exocrine cells, however, little has been known of granule membrane composition and membrane protein function. With the cloning and characterization of granule membrane proteins and their interactions at the inner leaflet of zymogen granules of pancreatic acinar cells, it is now possible to elucidate their function in membrane targeting and sorting of zymogens at the molecular level.

Noise and coupling affect signal detection and bursting in a simulated physiological neural network

Signal detection in the CNS relies on a complex interaction between the numerous synaptic inputs to the detecting cells. Two effects, stochastic resonance (SR) and coherence resonance (CR) have been shown to affect signal detection in arrays of basic neuronal models. Here, an array of simulated hippocampal CA1 neurons was used to test the hypothesis that physiological noise and electrical coupling can interact to modulate signal detection in the CA1 region of the hippocampus. The array was tested using varying levels of coupling and noise with different input signals. Detection of a subthreshold signal in the network improved as the number of detecting cells increased and as coupling was increased as predicted by previous studies in SR; however, the response depended greatly on the noise characteristics present and varied from SR predictions at times. Careful evaluation of noise characteristics may be necessary to form conclusions about the role of SR in complex systems such as physiological neurons. The coupled array fired synchronous, periodic bursts when presented with noise alone. The synchrony of this firing changed as a function of noise and coupling as predicted by CR. The firing was very similar to certain models of epileptiform activity, leading to a discussion of CR as a possible simple model of epilepsy. A single neuron was unable to recruit its neighbors to a periodic signal unless the signal was very close to the synchronous bursting frequency. These findings, when viewed in comparison with physiological parameters in the hippocampus, suggest that both SR and CR can have significant effects on signal processing in vivo.

Synaptic noise improves detection of subthreshold signals in hippocampal CA1 neurons

Stochastic resonance (SR) is a phenomenon whereby the detection of a low-level signal is enhanced in a nonlinear system by the introduction of noise. Studies of the effects of SR in neurons have suggested that noise could play a prominent role in improving detection of small signals. Most experimental SR research has focused on the role of noise in sensory neurons using physiological stimuli. Computer simulations show that signal detection in hippocampal neurons is improved by the addition of physiological levels of noise applied extracellularly to synaptic inputs. These results were confirmed experimentally. We now report that endogenous noise sources can also improve signal detection. The noise source was generated by modulating the random synaptic activity on the apical dendrites of CA1 cells in rat hippocampal slices using subthreshold cathodic current. Intracellular recordings of CA1 cells showed that even small increases of synaptic noise are able to greatly improve the detection of an independent, synaptic, subthreshold stimulus as predicted by the simulations. The noise variance in the CA1 cell was compared with the resting variance and with variance changes caused by several endogenous noise sources. In all cases, the increased noise variance was well within the physiological range. These results were supplemented and analyzed with a CA1 computer model. The improved signal detection with small amounts of endogenous noise suggests that the diverse inputs to CA1 are able to improve detection of subthreshold synaptic signals and could provide a means to modulate detection of specific inputs in the hippocampus.

Influence of temporal correlation of synaptic input on the rate and variability of firing in neurons

The spike trains that transmit information between neurons are stochastic. We used the theory of random point processes and simulation methods to investigate the influence of temporal correlation of synaptic input current on firing statistics. The theory accounts for two sources for temporal correlation: synchrony between spikes in presynaptic input trains and the unitary synaptic current time course. Simulations show that slow temporal correlation of synaptic input leads to high variability in firing. In a leaky integrate-and-fire neuron model with spike afterhyperpolarization the theory accurately predicts the firing rate when the spike threshold is higher than two standard deviations of the membrane potential fluctuations. For lower thresholds the spike afterhyperpolarization reduces the firing rate below the theory's predicted level when the synaptic correlation decays rapidly. If the synaptic correlation decays slower than the spike afterhyperpolarization, spike bursts can occur during single broad peaks of input fluctuations, increasing the firing rate over the prediction. Spike bursts lead to a coefficient of variation for the interspike intervals that can exceed one, suggesting an explanation of high coefficient of variation for interspike intervals observed in vivo.

Stochastic resonance improves signal detection in hippocampal CA1 neurons

Stochastic resonance (SR) is a phenomenon observed in nonlinear systems whereby the introduction of noise enhances the detection of a subthreshold signal for a certain range of noise intensity. The nonlinear threshold detection mechanism that neurons employ and the noisy environment in which they reside makes it likely that SR plays a role in neural signal detection. Although the role of SR in sensory neural systems has been studied extensively, its role in central neurons is unknown. In many central neurons, such as the hippocampal CA1 cell, very large dendritic trees are responsible for detecting neural input in a noisy environment. Attenuation due to the electrotonic length of these trees is significant, suggesting that a method other than passive summation is necessary if signals at the distal ends of the tree are to be detected. The hypothesis that SR plays an important role in the detection of distal synaptic inputs first was tested in a computer simulation of a CA1 cell and then verified with in vitro rat hippocampal slices. The results clearly showed that SR can enhance signal detection in CA1 hippocampal cells. Moreover, high levels of noise were found to equalize detection of synaptic signals received at varying positions on the dendritic tree. The amount of noise needed to evoke the effect is compared with physiological noise in slices and in vivo.

A new analytical method of studying post-synaptic currents

A powerful methodology for analyzing post-synaptic currents recorded from central neurons is presented. An unknown quantity of transmitter molecules released from presynaptic terminals by electrical stimulation of nerve fibers generates a post-synaptic response at the synaptic site. The current induced at the synaptic junction is assumed to rise rapidly and decay slowly with its peak amplitude being proportional to the number of released transmitter molecules. The signal so generated is then distorted by the cable properties of the dendrite, modeled as a time-invariant, linear filter with unknown parameters. The response recorded from the cell body of the neuron following the electrical stimulation is contaminated by zero-mean, white, Gaussian noise. The parameters of the signal are then evaluated from the observation sequence using a quasi-profile likelihood estimation procedure. These parameter values are then employed to deconvolve each measured post-synaptic response to produce an optimal estimate of the transmembrane current flux. From these estimates we derive the amplitude of the synaptic current and the relative amount of transmitter molecules that elicited each response. The underlying amplitude fluctuations in the entire data sequence are investigated using a non-parametric technique based on kernel smoothing procedures. The effectiveness of the new methodology is illustrated in various simulation examples.