Synaptic Noise Research Articles

Stimulus Feature Selectivity in Excitatory and Inhibitory Neurons in Primary Visual Cortex

Although several lines of evidence suggest that stimulus selectivity in somatosensory and visual cortices is critically dependent on unselective inhibition, particularly in the thalamorecipient layer 4, no comprehensive comparison of the responses of excitatory and inhibitory cells has been conducted. Here, we recorded intracellularly from a large population of regular spiking (RS; presumed excitatory) and fast spiking (FS; presumed inhibitory) cells in layers 2-6 of primary visual cortex. In layer 4, where selectivity for orientation and spatial frequency first emerges, we found no untuned FS cells. Instead, the tuning of the spike output of layer 4 FS cells was significantly but moderately broader than that of RS cells. However, the tuning of the underlying synaptic responses was not different, indicating that the difference in spike-output selectivity resulted from differences in the transformation of synaptic input into firing rate. Layer 4 FS cells exhibited significantly lower input resistance and faster time constants than layer 4 RS cells, leading to larger and faster membrane potential (V(m)) fluctuations. FS cell V(m) fluctuations were more broadly tuned than those of RS cells and matched spike-output tuning, suggesting that the broader spike tuning of these cells was driven by visually evoked synaptic noise. These differences were not observed outside of layer 4. Thus, cell type-specific differences in stimulus feature selectivity at the first level of cortical sensory processing may arise as a result of distinct biophysical properties that determine the dynamics of synaptic integration.

Neurodynamical amplification of perceptual signals via system-size resonance

We study the effect of finite-size synaptic noise on the response to time-varying stimuli of a detailed neurodynamical model of perceptual-decision making. Specifically, the reaction to a periodic stimulus is analyzed, while controlling the noise level through the number of neurons in the perception module. We find an enhanced response to the dynamical stimulus for intermediate noise, i.e. for an optimal system size. We thus propose a possible functional role of system-size resonance in a complex cognitive task.

Impact of synaptic noise and conductance state on spontaneous cortical firing

Cortical neurons in-vivo operate in a continuum of overall conductance states, depending on the average level of background synaptic input throughout the dendritic tree. We compare how variability, or fluctuations, in this input affects the statistics of the resulting 'spontaneous' or 'background' firing activity, between two extremes of the mean input corresponding to a low-conductance (LC) and a high-conductance (HC) state. In the HC state, we show that both firing rate and regularity increase with increasing variability. In the LC state, firing rate also increases with input variability, but in contrast to the HC state, firing regularity first decreases and then increases with an increase in the variability. At high levels of input variability, firing regularity in both states converge to similar values.

ADAPTIVE THRESHOLDS FOR NEURAL NETWORKS WITH SYNAPTIC NOISE

The inclusion of a macroscopic adaptive threshold is studied for the retrieval dynamics of both layered feedforward and fully connected neural network models with synaptic noise. These two types of architectures require a different method to be solved numerically. In both cases it is shown that, if the threshold is chosen appropriately as a function of the cross-talk noise and of the activity of the stored patterns, adapting itself automatically in the course of the recall process, an autonomous functioning of the network is guaranteed. This self-control mechanism considerably improves the quality of retrieval, in particular the storage capacity, the basins of attraction and the mutual information content.

The Thouless–Anderson–Palmer equation for an analogue neural network with temporally fluctuating white synaptic noise

Effects of synaptic noise on the retrieval process of associative memory neural networks are studied from the viewpoint of neurobiological and biophysical understanding of information processing in the brain. We investigate the statistical mechanical properties of stochastic analogue neural networks with temporally fluctuating synaptic noise, which is assumed to be white noise. Such networks, in general, defy the use of the replica method, since they have no energy concept. The self-consistent signal-to-noise analysis (SCSNA), which is an alternative to the replica method for deriving a set of order parameter equations, requires no energy concept and thus becomes available in studying networks without energy functions. Applying the SCSNA to stochastic networks requires the knowledge of the Thouless–Anderson–Palmer (TAP) equation which defines the deterministic networks equivalent to the original stochastic ones. The study of the TAP equation which is of particular interest for the case without energy concept is very less, while it is closely related to the SCSNA in the case with energy concept. This paper aims to derive the TAP equation for networks with synaptic noise together with a set of order parameter equations by a hybrid use of the cavity method and the SCSNA.

On the stationary state of a network of inhibitory spiking neurons

The background activity of a cortical neural network is modeled by a homogeneous integrate-and-fire network with unreliable inhibitory synapses. For the case of fast synapses, numerical and analytical calculations show that the network relaxes into a stationary state of high attention. The majority of the neurons has a membrane potential just below the threshold; as a consequence the network can react immediately - on the time scale of synaptic transmission- on external pulses. The neurons fire with a low rate and with a broad distribution of interspike intervals. Firing events of the total network are correlated over short time periods. The firing rate increases linearly with external stimuli. In the limit of infinitely large networks, the synaptic noise decreases to zero. Nevertheless, the distribution of interspike intervals remains broad.

Simultaneous estimation of global background synaptic inhibition and excitation from membrane potential fluctuations in layer III neurons of the rat entorhinal cortex in vitro

It is becoming clear that the detection and integration of synaptic input and its conversion into an output signal in cortical neurons are strongly influenced by background synaptic activity or “noise.” The majority of this noise results from the spontaneous release of synaptic transmitters, interacting with ligand-gated ion channels in the postsynaptic neuron [Berretta N, Jones RSG (1996); A comparison of spontaneous synaptic EPSCs in layer V and layer II neurones in the rat entorhinal cortex in vitro. J Neurophysiol 76:1089–1110; Jones RSG, Woodhall GL (2005) Background synaptic activity in rat entorhinal cortical neurons: differential control of transmitter release by presynaptic receptors. J Physiol 562:107–120; LoTurco JJ, Mody I, Kriegstein AR (1990) Differential activation of glutamate receptors by spontaneously released transmitter in slices of neocortex. Neurosci Lett 114:265–271; Otis TS, Staley KJ, Mody I (1991) Perpetual inhibitory activity in mammalian brain slices generated by spontaneous GABA release. Brain Res 545:142–150; Ropert N, Miles R, Korn H (1990) Characteristics of miniature inhibitory postsynaptic currents in CA1 pyramidal neurones of rat hippocampus. J Physiol 428:707–722; Salin PA, Prince DA (1996) Spontaneous GABAA receptor-mediated inhibitory currents in adult rat somatosensory cortex. J Neurophysiol 75:1573–1588; Staley KJ (1999) Quantal GABA release: noise or not? Nat Neurosci 2:494–495; Woodhall GL, Bailey SJ, Thompson SE, Evans DIP, Stacey AE, Jones RSG (2005) Fundamental differences in spontaneous synaptic inhibition between deep and superficial layers of the rat entorhinal cortex. Hippocampus 15:232–245]. The function of synaptic noise has been the subject of debate for some years, but there is increasing evidence that it modifies or controls neuronal excitability and, thus, the integrative properties of cortical neurons. In the present study we have investigated a novel approach [Rudolph M, Piwkowska Z, Badoual M, Bal T, Destexhe A (2004) A method to estimate synaptic conductances from membrane potential fluctuations. J Neurophysiol 91:2884–2896] to simultaneously quantify synaptic inhibitory and excitatory synaptic noise, together with postsynaptic excitability, in rat entorhinal cortical neurons in vitro. The results suggest that this is a viable and useful approach to the study of the function of synaptic noise in cortical networks.

Thouless–Anderson–Palmer equation for associative memory neural network with synaptic noise

We study the effects of temporal fluctuations in synaptic couplings on the properties of analog neural networks. Since no energy concept exists in networks with such couplings, the use of the replica method does not make sense. On the other hand, the self-consistent signal-to-noise analysis (SCSNA), which is an alternative to the replica method for deriving a set of order parameter equations, requires no energy concept and thus plays an important role for studying such networks. To apply the SCSNA to stochastic networks, it is necessary to define the deterministic networks equivalent to the original stochastic ones, which are given by the Thouless–Anderson–Palmer (TAP) equations. Therefore the TAP equation is of interest for studying the statistical properties of the networks with synaptic noise, while such study is very few. In this paper, we show the TAP equation together with a set of order parameter equations for such networks by using both the cavity method and the SCSNA.

Calcium and synaptic dynamics underlying reverberatory activity in neuronal networks

Persistent activity is postulated to drive neural network plasticity and learning. To investigate its underlying cellular mechanisms, we developed a biophysically tractable model that explains the emergence, sustenance and eventual termination of short-term persistent activity. Using the model, we reproduced the features of reverberating activity that were observed in small (50–100 cells) networks of cultured hippocampal neurons, such as the appearance of polysynaptic current clusters, the typical inter-cluster intervals, the typical duration of reverberation, and the response to changes in extra-cellular ionic composition. The model relies on action potential-triggered residual pre-synaptic calcium, which we suggest plays an important role in sustaining reverberations. We show that reverberatory activity is maintained by enhanced asynchronous transmitter release from pre-synaptic terminals, which in itself depends on the dynamics of residual pre-synaptic calcium. Hence, asynchronous release, rather than being a ‘synaptic noise’, can play an important role in network dynamics. Additionally, we found that a fast timescale synaptic depression is responsible for oscillatory network activation during reverberations, whereas the onset of a slow timescale depression leads to the termination of reverberation. The simplicity of our model enabled a number of predictions that were confirmed by additional analyses of experimental manipulations.

Spike Timing and Reliability in Cortical Pyramidal Neurons: Effects of EPSC Kinetics, Input Synchronization and Background Noise on Spike Timing

In vivo studies have shown that neurons in the neocortex can generate action potentials at high temporal precision. The mechanisms controlling timing and reliability of action potential generation in neocortical neurons, however, are still poorly understood. Here we investigated the temporal precision and reliability of spike firing in cortical layer V pyramidal cells at near-threshold membrane potentials. Timing and reliability of spike responses were a function of EPSC kinetics, temporal jitter of population excitatory inputs, and of background synaptic noise. We used somatic current injection to mimic population synaptic input events and measured spike probability and spike time precision (STP), the latter defined as the time window (Δt) holding 80% of response spikes. EPSC rise and decay times were varied over the known physiological spectrum. At spike threshold level, EPSC decay time had a stronger influence on STP than rise time. Generally, STP was highest (≤2.45 ms) in response to synchronous compounds of EPSCs with fast rise and decay kinetics. Compounds with slow EPSC kinetics (decay time constants>6 ms) triggered spikes at lower temporal precision (≥6.58 ms). We found an overall linear relationship between STP and spike delay. The difference in STP between fast and slow compound EPSCs could be reduced by incrementing the amplitude of slow compound EPSCs. The introduction of a temporal jitter to compound EPSCs had a comparatively small effect on STP, with a tenfold increase in jitter resulting in only a five fold decrease in STP. In the presence of simulated synaptic background activity, precisely timed spikes could still be induced by fast EPSCs, but not by slow EPSCs.

Reliability of the interval death rate analysis for estimating the time course of the motoneurone afterhyperpolarization in humans

The reliability of the afterhyperpolarization (AHP) time course, as estimated by the interval death rate (IDR) analysis was evaluated both within and between investigators. The IDR analysis uses the firing history of a single motor unit train at low tonic firing rates to calculate an estimate of the AHP time course [Matthews PB. Relationship of firing intervals of human motor units to the trajectory of post-spike after-hyperpolarization and synaptic noise. J Physiol 1996;492:597–628]. Single motor unit trains were collected from the tibialis anterior (TA) to determine intra-rater reliability (within investigator). Data from the first dorsal interosseus (FDI), collected in a previous investigation [Gossen ER, Ivanova TD, Garland SJ. The time course of the motoneurone afterhyperpolarization is related to motor unit twitch speed in human skeletal muscle. J Physiol 2003;552:657–64], were used to examine the inter-rater reliability (between investigators). The lead author was blinded to the original time constants and file identities for the re-analysis. The intra-rater reliability of the AHP time constant in the TA data was high (r2=0.88; p<0.001; ICC=0.91). The inter-rater reliability for the FDI data was also strong (r2=0.92; p<0.001; ICC=0.95). The standard error of measurement was 0.61ms for the TA and 0.55ms for FDI. It is concluded that the interval death rate analysis is a reliable tool for estimating the AHP time course with experienced investigators.

Fast-reset of pacemaking and theta-frequency resonance patterns in cerebellar golgi cells: Simulations of their impact in vivo

The Golgi cells are inhibitory interneurons of the cerebellar granular layer, which respond to afferent stimulation in vivo with a burst-pause sequence interrupting their irregular background low-frequency firing (Vos et al., 1999a. Eur. J. Neurosci. 11, 2621–2634). However, Golgi cells in vitro are regular pacemakers (Forti et al., 2006. J. Physiol. 574, 711–729), raising the question how their ionic mechanisms could impact on responses during physiological activity. Using patch-clamp recordings in cerebellar slices we show that the pacemaker cycle can be suddenly reset by spikes, making the cell highly sensitive to input variations. Moreover, the neuron resonates around the pacemaker frequency, making it specifically sensitive to patterned stimulation in the theta-frequency band. Computational analysis based on a model developed to reproduce Golgi cell pacemaking (Solinas et al., 2008 Front. Neurosci., 2:2) predicted that phase-reset required spike-triggered activation of SK channels and that resonance was sustained by a slow voltage-dependent potassium current and amplified by a persistent sodium current. Adding balanced synaptic noise to mimic the irregular discharge observed in vivo, we found that pacemaking converts into spontaneous irregular discharge, that phase-reset plays an important role in generating the burst-pause pattern evoked by sensory stimulation, and that repetitive stimulation at theta-frequency enhances the time-precision of spike coding in the burst. These results suggest that Golgi cell intrinsic properties exert a profound impact on time-dependent signal processing in the cerebellar granular layer.

The spikes trains probability distributions: A stochastic calculus approach

We discuss the statistics of spikes trains for different types of integrate-and-fire neurons and different types of synaptic noise models. In contrast with the usual approaches in neuroscience, mainly based on statistical physics methods such as the Fokker–Planck equation or the mean-field theory, we chose the point of the view of the stochastic calculus theory to characterize neurons in noisy environments. We present four stochastic calculus techniques that can be used to find the probability distributions attached to the spikes trains. We illustrate the power of these techniques for four types of widely used neuron models. Despite the fact that these techniques are mathematically intricate we believe that they can be useful for answering questions in neuroscience that naturally arise from the variability of neuronal activity. For each technique we indicate its range of applicability and its limitations.

Unilateral Tinnitus Caused by Cerumen Impaction and Stochastic Resonance

Objectives: The objectives of this study were: (i) To investigate hearing lost in patients with tinnitus caused by cerumen impaction before and after cerumen removal and its relationship to the tinnitus. (ii) To discuss possible role of synaptic noise by mechanism of stochastic resonance. Methods: Nine patients, who had unilateral tinnitus and cerumen impaction, were selected for the study. Standard audiometric evaluation was performed, including case history, and measures of hearing sensitivity at 0.25-8 kHz. The testing protocol had three response tasks: threshold testing, loudness matching, and pitch matching. The time after cerumen removal to the end of tinnitus was measured. Results: In all patients the tinnitus disappeared in 20 minutes after cerumen removal. Control audiometry five minutes after cerumen removal showed recovery of normal hearing. Conclusion: Fast dissipation of tinnitus after cerumen removal and recovery of normal hearing shows possible connection between mechanism of weak signal enhancement for example by mechanism of stochastic resonance and tinnitus. Such a mechanism may also explain presence of tinnitus in individuals with intact auditory perception. Study conducted at Otorhinolaryngology Outpatient Clinic, Rychnov nad Kneznou The work has been supported by the research grant of the Faculty of Chemical Engineering of the Institute of Chemical Technology, Prague No. MSM 6046137306.

On the Use of Analytical Expressions for the Voltage Distribution to Analyze Intracellular Recordings

Different analytical expressions for the membrane potential distribution of membranes subject to synaptic noise have been proposed and can be very helpful in analyzing experimental data. However, all of these expressions are either approximations or limit cases, and it is not clear how they compare and which expression should be used in a given situation. In this note, we provide a comparison of the different approximations available, with an aim of delineating which expression is most suitable for analyzing experimental data.

Control of neural chaos by synaptic noise

We study neural automata – or neurobiologically inspired cellular automata – which exhibits chaotic itinerancy among the different stored patterns or memories. This is a consequence of activity-dependent synaptic fluctuations, which continuously destabilize the attractor and induce irregular hopping to other possible attractors. The nature of these irregularities depends on the dynamic details, namely, on the intensity of the synaptic noise and the number of sites of the network, which are synchronously updated at each time step. Varying these factors, different regimes occur, ranging from regular to chaotic dynamics. As a result, and in absence of external agents, the chaotic behavior may turn regular after tuning the noise intensity. It is argued that a similar mechanism might be on the basis of self-controlling chaos in natural systems.

Statistics of subthreshold neuronal voltage fluctuations due to conductance-based synaptic shot noise

Neurons in the central nervous system, and in the cortex in particular, are subject to a barrage of pulses from their presynaptic populations. These synaptic pulses are mediated by conductance changes and therefore lead to increases or decreases of the neuronal membrane potential with amplitudes that are dependent on the voltage: synaptic noise is multiplicative. The statistics of the membrane potential are of experimental interest because the measurement of a single subthreshold voltage can be used to probe the activity occurring across the presynaptic population. Though the interpulse interval is not always significantly smaller than the characteristic decay time of the pulses, and so the fluctuations have the nature of shot noise, the majority of results available in the literature have been calculated in the diffusion limit, which is valid for high-rate pulses. Here the effects that multiplicative conductance noise and shot noise have on the voltage fluctuations are examined. It is shown that both these aspects of synaptic drive sculpt high-order features of the subthreshold voltage distribution, such as the skew. It is further shown that the diffusion approximation can only capture the effects arising from the multiplicative conductance noise, predicting a negative voltage skew for excitatory drive. Exact results for the full dynamics are derived from a master-equation approach, predicting positively skewed distributions with long tails in voltage ranges typical for action potential generation. It is argued that, although the skew is a high-order feature of subthreshold voltage distributions, the increased probability of reaching firing threshold suggests a potential role for shot noise in shaping the neuronal transfer function.

When Response Variability Increases Neural Network Robustness to Synaptic Noise

Cortical sensory neurons are known to be highly variable, in the sense that responses evoked by identical stimuli often change dramatically from trial to trial. The origin of this variability is uncertain, but it is usually interpreted as detrimental noise that reduces the computational accuracy of neural circuits. Here we investigate the possibility that such response variability might in fact be beneficial, because it may partially compensate for a decrease in accuracy due to stochastic changes in the synaptic strengths of a network. We study the interplay between two kinds of noise, response (or neuronal) noise and synaptic noise, by analyzing their joint influence on the accuracy of neural networks trained to perform various tasks. We find an interesting, generic interaction: when fluctuations in the synaptic connections are proportional to their strengths (multiplicative noise), a certain amount of response noise in the input neurons can significantly improve network performance, compared to the same network without response noise. Performance is enhanced because response noise and multiplicative synaptic noise are in some ways equivalent. So if the algorithm used to find the optimal synaptic weights can take into account the variability of the model neurons, it can also take into account the variability of the synapses. Thus, the connection patterns generated with response noise are typically more resistant to synaptic degradation than those obtained without response noise. As a consequence of this interplay, if multiplicative synaptic noise is present, it is better to have response noise in the network than not to have it. These results are demonstrated analytically for the most basic network consisting of two input neurons and one output neuron performing a simple classification task, but computer simulations show that the phenomenon persists in a wide range of architectures, including recurrent (attractor) networks and sensorimotor networks that perform coordinate transformations. The results suggest that response variability could play an important dynamic role in networks that continuously learn.

Restoring balance to the reflex actions of the muscle spindle: the secondary endings also matter

Restoring balance to the reflex actions of the muscle spindle: the secondary endings also matter

Synaptic noise as a means of implementing weight-perturbation learning

Weight-perturbation (WP) algorithms for supervised and/or reinforcement learning offer improved biological plausibility over backpropagation because of their reduced circuitry requirements for realization in neural hardware. This paper explores the hypothesis that biological synaptic noise might serve as the substrate by which weight perturbation is implemented. We explore the basic synaptic noise hypothesis (BSNH), which embodies the weakest assumptions about the underlying neural circuitry required to implement WP algorithms. This paper identifies relevant biological constraints consistent with the BSNH, taxonomizes existing WP algorithms with regard to consistency with those constraints, and proposes a new WP algorithm that is fully consistent with the constraints. By comparing the learning effectiveness of these algorithms via simulation studies, it is found that all of the algorithms can support traditional neural network learning tasks and have similar generalization characteristics, although the results suggest a trade-off between learning efficiency and biological accuracy.