scholarly journals Optimal Time Scale for Spike-Time Reliability: Theory, Simulations, and Experiments

2008 ◽  
Vol 99 (1) ◽  
pp. 277-283 ◽  
Author(s):  
Roberto F. Galán ◽  
G. Bard Ermentrout ◽  
Nathaniel N. Urban
Keyword(s):  
Time Scale ◽  
High Reliability ◽  
Pyramidal Cells ◽  
Spike Timing ◽  
Optimal Time ◽  
Spike Time ◽  
Neuron Models ◽  
Temporal Precision ◽  
Gamma Range ◽  
Theoretical Predictions

Use of spike timing to encode information requires that neurons respond with high temporal precision and with high reliability. Fast fluctuating stimuli are known to result in highly reproducible spike times across trials, whereas constant stimuli result in variable spike times. Here, we first studied mathematically how spike-time reliability depends on the rapidness of aperiodic stimuli. Then, we tested our theoretical predictions in computer simulations of neuron models (Hodgkin-Huxley and modified quadratic integrate-and-fire), as well as in patch-clamp experiments with real neurons (mitral cells in the olfactory bulb and pyramidal cells in the neocortex). As predicted by our theory, we found that for firing frequencies in the beta/gamma range, spike-time reliability is maximal when the time scale of the input fluctuations (autocorrelation time) is in the range of a few milliseconds (2–5 ms), coinciding with the time scale of fast synapses, and decreases substantially for faster and slower inputs. Finally, we comment how these findings relate to mechanisms causing neuronal synchronization.

scholarly journals Self-tuning of inhibition by endocannabinoids shapes spike-time precision in CA1 pyramidal neurons

2013 ◽  
Vol 110 (8) ◽  
pp. 1930-1944 ◽  
Author(s):  
Franck Dubruc ◽  
David Dupret ◽  
Olivier Caillard
Keyword(s):  
Pyramidal Neurons ◽  
Pyramidal Cells ◽  
Memory Formation ◽  
Spike Timing ◽  
Excitatory Postsynaptic Potential ◽  
Spike Time ◽  
Transient Improvement ◽  
Self Tuning ◽  
Feedforward Inhibition

In the hippocampus, activity-dependent changes of synaptic transmission and spike-timing coordination are thought to mediate information processing for the purpose of memory formation. Here, we investigated the self-tuning of intrinsic excitability and spiking reliability by CA1 hippocampal pyramidal cells via changes of their GABAergic inhibitory inputs and endocannabinoid (eCB) signaling. Firing patterns of CA1 place cells, when replayed in vitro, induced an eCB-dependent transient reduction of spontaneous GABAergic activity, sharing the main features of depolarization-induced suppression of inhibition (DSI), and conditioned a transient improvement of spike-time precision during consecutive burst discharges. When evaluating the consequences of DSI on excitatory postsynaptic potential (EPSP)-spike coupling, we found that transient reductions of uncorrelated (spontaneous) or correlated (feedforward) inhibition improved EPSP-spike coupling probability. The relationship between EPSP-spike-timing reliability and inhibition was, however, more complex: transient reduction of correlated (feedforward) inhibition disrupted or improved spike-timing reliability according to the initial spike-coupling probability. Thus eCB-mediated tuning of pyramidal cell spike-time precision is governed not only by the initial level of global inhibition, but also by the ratio between spontaneous and feedforward GABAergic activities. These results reveal that eCB-mediated self-tuning of spike timing by the discharge of pyramidal cells can constitute an important contribution to place-cell assemblies and memory formation in the hippocampus.

scholarly journals Background Synaptic Conductance and Precision of EPSP-Spike Coupling at Pyramidal Cells

2005 ◽  
Vol 93 (6) ◽  
pp. 3248-3256 ◽  
Author(s):  
Veronika Zsiros ◽  
Shaul Hestrin
Keyword(s):  
Time Course ◽  
Pyramidal Cells ◽  
Spike Timing ◽  
Synaptic Activity ◽  
Synaptic Conductance ◽  
Temporal Precision ◽  
Voltage Dependent ◽  
Cortical Slices

The temporal precision of converting excitatory postsynaptic potentials (EPSPs) into spikes at pyramidal cells is critical for the coding of information in the cortex. Several in vitro studies have shown that voltage-dependent conductances in pyramidal cells can prolong the EPSP time course resulting in an imprecise EPSP-spike coupling. We have used dynamic-clamp techniques to mimic the in vivo background synaptic conductance in cortical slices and investigated how the ongoing synaptic activity may affect the EPSP time course near threshold and the EPSP spike coupling. We report here that background synaptic conductance dramatically diminished the depolarization related prolongation of the EPSPs in pyramidal cells and improved the precision of spike timing. Furthermore, we found that background synaptic conductance can affect the interaction among action potentials in a spike train. Thus the level of ongoing synaptic activity in the cortex may regulate the capacity of pyramidal cells to process temporal information.

scholarly journals Action Potential Timing Precision in Dorsal Cochlear Nucleus Pyramidal Cells

2007 ◽  
Vol 97 (6) ◽  
pp. 4162-4172 ◽  
Author(s):  
Sarah E. Street ◽  
Paul B. Manis
Keyword(s):  
Firing Rate ◽  
Cochlear Nucleus ◽  
Pyramidal Neurons ◽  
Pyramidal Cells ◽  
Spike Trains ◽  
Spike Timing ◽  
Dorsal Cochlear Nucleus ◽  
Information Encoding ◽  
Temporal Precision ◽  
Noise Current

Many studies of the dorsal cochlear nucleus (DCN) have focused on the representation of acoustic stimuli in terms of average firing rate. However, recent studies have emphasized the role of spike timing in information encoding. We sought to ascertain whether DCN pyramidal cells might employ similar strategies and to what extent intrinsic excitability regulates spike timing. Gaussian distributed low-pass noise current was injected into pyramidal cells in a brain slice preparation. The shuffled autocorrelation-based analysis was used to compute a correlation index of spike times across trials. The noise causes the cells to fire with temporal precision (SD ≅ 1–2 ms) and high reproducibility. Increasing the coefficient of variation of the noise improved the reproducibility of the spike trains, whereas increasing the firing rate of the neuron decreased the neurons' ability to respond with predictable patterns of spikes. Simulated inhibitory postsynaptic potentials superimposed on the noise stimulus enhanced spike timing for >300 ms, although the enhancement was greatest during the first 100 ms. We also found that populations of pyramidal neurons respond to the same noise stimuli with correlated spike trains, suggesting that ensembles of neurons in the DCN receiving shared input can fire with similar timing. These results support the hypothesis that spike timing can be an important aspect of information coding in the DCN.

Dynamical Constraints on Using Precise Spike Timing to Compute in Recurrent Cortical Networks

2008 ◽  
Vol 20 (4) ◽  
pp. 974-993 ◽  
Author(s):  
Arunava Banerjee ◽  
Peggy Seriès ◽  
Alexandre Pouget
Keyword(s):  
Dynamical System ◽  
Initial Conditions ◽  
Internal State ◽  
Spike Timing ◽  
Spiking Neurons ◽  
Spatiotemporal Patterns ◽  
Cortical Networks ◽  
Temporal Precision ◽  
Cortical Areas ◽  
Dynamical Constraints

Several recent models have proposed the use of precise timing of spikes for cortical computation. Such models rely on growing experimental evidence that neurons in the thalamus as well as many primary sensory cortical areas respond to stimuli with remarkable temporal precision. Models of computation based on spike timing, where the output of the network is a function not only of the input but also of an independently initializable internal state of the network, must, however, satisfy a critical constraint: the dynamics of the network should not be sensitive to initial conditions. We have previously developed an abstract dynamical system for networks of spiking neurons that has allowed us to identify the criterion for the stationary dynamics of a network to be sensitive to initial conditions. Guided by this criterion, we analyzed the dynamics of several recurrent cortical architectures, including one from the orientation selectivity literature. Based on the results, we conclude that under conditions of sustained, Poisson-like, weakly correlated, low to moderate levels of internal activity as found in the cortex, it is unlikely that recurrent cortical networks can robustly generate precise spike trajectories, that is, spatiotemporal patterns of spikes precise to the millisecond timescale.

Postsynaptic Receptive Field Size and Spike Threshold Determine Encoding of High-Frequency Information Via Sensitivity to Synchronous Presynaptic Activity

2009 ◽  
Vol 101 (3) ◽  
pp. 1160-1170 ◽  
Author(s):  
Jason W. Middleton ◽  
André Longtin ◽  
Jan Benda ◽  
Leonard Maler
Keyword(s):  
High Frequency ◽  
Time Window ◽  
Pyramidal Cells ◽  
Spike Trains ◽  
Small Time ◽  
Field Size ◽  
Recent Experimental Data ◽  
Neuron Models ◽  
High Frequency Signal ◽  
Unit Spike

Parallel sensory streams carrying distinct information about various stimulus properties have been observed in several sensory systems, including the visual system. What remains unclear is why some of these streams differ in the size of their receptive fields (RFs). In the electrosensory system, neurons with large RFs have short-latency responses and are tuned to high-frequency inputs. Conversely, neurons with small RFs are low-frequency tuned and exhibit longer-latency responses. What principle underlies this organization? We show experimentally that synchronous electroreceptor afferent (P-unit) spike trains selectively encode high-frequency stimulus information from broadband signals. This finding relies on a comparison of stimulus-spike output coherence using output trains obtained by either summing pairs of recorded afferent spike trains or selecting synchronous spike trains based on coincidence within a small time window. We propose a physiologically realistic decoding mechanism, based on postsynaptic RF size and postsynaptic output rate normalization that tunes target pyramidal cells in different electrosensory maps to low- or high-frequency signal components. By driving realistic neuron models with experimentally obtained P-unit spike trains, we show that a small RF is matched with a postsynaptic integration regime leading to responses over a broad range of frequencies, and a large RF with a fluctuation-driven regime that requires synchronous presynaptic input and therefore selectively encodes higher frequencies, confirming recent experimental data. Thus our work reveals that the frequency content of a broadband stimulus extracted by pyramidal cells, from P-unit afferents, depends on the amount of feedforward convergence they receive.

scholarly journals Balanced networks under spike-time dependent plasticity

2021 ◽  
Vol 17 (5) ◽  
pp. e1008958
Author(s):  
Alan Eric Akil ◽  
Robert Rosenbaum ◽  
Krešimir Josić
Keyword(s):  
Spike Timing ◽  
Recurrent Networks ◽  
Spike Time ◽  
Weight Changes ◽  
Initial State ◽  
Dependent Plasticity ◽  
Plausible Mechanism ◽  
Spike Time Dependent Plasticity ◽  
Induced Changes ◽  
Balanced Networks

The dynamics of local cortical networks are irregular, but correlated. Dynamic excitatory–inhibitory balance is a plausible mechanism that generates such irregular activity, but it remains unclear how balance is achieved and maintained in plastic neural networks. In particular, it is not fully understood how plasticity induced changes in the network affect balance, and in turn, how correlated, balanced activity impacts learning. How do the dynamics of balanced networks change under different plasticity rules? How does correlated spiking activity in recurrent networks change the evolution of weights, their eventual magnitude, and structure across the network? To address these questions, we develop a theory of spike–timing dependent plasticity in balanced networks. We show that balance can be attained and maintained under plasticity–induced weight changes. We find that correlations in the input mildly affect the evolution of synaptic weights. Under certain plasticity rules, we find an emergence of correlations between firing rates and synaptic weights. Under these rules, synaptic weights converge to a stable manifold in weight space with their final configuration dependent on the initial state of the network. Lastly, we show that our framework can also describe the dynamics of plastic balanced networks when subsets of neurons receive targeted optogenetic input.

Spike Coding in Pyramidal Cells of the Piriform Cortex of Rat

2001 ◽  
Vol 86 (3) ◽  
pp. 1504-1510 ◽  
Author(s):  
Alexander D. Protopapas ◽  
James M. Bower
Keyword(s):  
Cognitive Function ◽  
Piriform Cortex ◽  
Pyramidal Cells ◽  
Spike Trains ◽  
Gamma Range ◽  
Cortical Oscillations ◽  
Cell Recording ◽  
Spike Coding ◽  
Theta Range

The study of cortical oscillations has undergone a renaissance in recent years because of their presumed role in cognitive function. Of particular interest are frequencies in the gamma (30–100 Hz) and theta (3–12 Hz) ranges. In this paper, we use spike coding techniques and in vitro whole cell recording to assess the ability of individual pyramidal cells of the piriform cortex to code inputs occurring in these frequencies. The results suggest that the spike trains of individual neurons are much better at representing frequencies in the theta range than those in the gamma range.

Band-Pass Response Properties of Rat SI Neurons

2003 ◽  
Vol 90 (3) ◽  
pp. 1379-1391 ◽  
Author(s):  
Catherine E. Garabedian ◽  
Stephanie R. Jones ◽  
Michael M. Merzenich ◽  
Anders Dale ◽  
Christopher I. Moore
Keyword(s):  
Inhibitory Neuron ◽  
Stimulus Onset ◽  
Spike Timing ◽  
Spike Rate ◽  
Frequency Range ◽  
Response Properties ◽  
Temporal Precision ◽  
Band Pass ◽  
Temporal Measures ◽  
Vector Strength

Rats typically employ 4- to 12-Hz “whisking” movements of their vibrissae during tactile exploration. The intentional sampling of signals in this frequency range suggests that neural processing of tactile information may be differentially engaged in this bandwidth. We examined action potential responses in rat primary somatosensory cortex (SI) to a range of frequencies of vibrissa motion. Single vibrissae were mechanically deflected with 5-s pulse trains at rates ≤40 Hz. As previously reported, vibrissa deflection evoked robust neural responses that consistently adapted to stimulus rates ≥3 Hz. In contrast with this low-pass feature of the response, several other characteristics of the response revealed bandpass response properties. While average evoked response amplitudes measured 0–35 ms after stimulus onset typically decreased with increasing frequency, the later components of the response (>15 ms post stimulus) were augmented at frequencies between 3 and 10 Hz. Further, during the steady state, both rate and temporal measures of neural activity, measured as total spike rate or vector strength (a measure of temporal fidelity of spike timing across cycles), showed peak signal values at 5–10 Hz. A minimal biophysical network model of SI layer IV, consisting of an excitatory and inhibitory neuron and thalamocortical input, captured the spike rate and vector strength band-pass characteristics. Further analyses in which specific elements were selectively removed from the model suggest that slow inhibitory influences give rise to the band-pass peak in temporal precision, while thalamocortical adaptation can account for the band-pass peak in spike rate. The presence of these band-pass characteristics may be a general property of thalamocortical circuits that lead rodents to target this frequency range with their whisking behavior.

scholarly journals Ion Channel Stochasticity May Be Critical in Determining the Reliability and Precision of Spike Timing

1998 ◽  
Vol 10 (7) ◽  
pp. 1679-1703 ◽  
Author(s):  
Elad Schneidman ◽  
Barry Freedman ◽  
Idan Segev
Keyword(s):  
Ion Channels ◽  
High Reliability ◽  
Spike Timing ◽  
Spike Rate ◽  
Subthreshold Oscillations ◽  
Neocortical Neurons ◽  
Current Input ◽  
Membrane Patch ◽  
Spike Firing ◽  
Exact Timing

The firing reliability and precision of an isopotential membrane patch consisting of a realistically large number of ion channels is investigated using a stochastic Hodgkin-Huxley (HH) model. In sharp contrast to the deterministic HH model, the biophysically inspired stochastic model reproduces qualitatively the different reliability and precision characteristics of spike firing in response to DC and fluctuating current input in neocortical neurons, as reported by Mainen & Sejnowski (1995). For DC inputs, spike timing is highly unreliable; the reliability and precision are significantly increased for fluctuating current input. This behavior is critically determined by the relatively small number of excitable channels that are opened near threshold for spike firing rather than by the total number of channels that exist in the membrane patch. Channel fluctuations, together with the inherent bistability in the HH equations, give rise to three additional experimentally observed phenomena: subthreshold oscillations in the membrane voltage for DC input, “spontaneous” spikes for subthreshold inputs, and “missing” spikes for suprathreshold inputs. We suggest that the noise inherent in the operation of ion channels enables neurons to act as “smart” encoders. Slowly varying, uncorrelated inputs are coded with low reliability and accuracy and, hence, the information about such inputs is encoded almost exclusively by the spike rate. On the other hand, correlated presynaptic activity produces sharp fluctuations in the input to the postsynaptic cell, which are then encoded with high reliability and accuracy. In this case, information about the input exists in the exact timing of the spikes. We conclude that channel stochasticity should be considered in realistic models of neurons.

scholarly journals Theta-Frequency Resonance at the Cerebellum Input Stage Improves Spike Timing on the Millisecond Time-Scale

2013 ◽  
Vol 7 ◽  
Author(s):  
Daniela Gandolfi ◽  
Paola Lombardo ◽  
Jonathan Mapelli ◽  
Sergio Solinas ◽  
Egidio D’Angelo
Keyword(s):  
Time Scale ◽  
Spike Timing ◽  
Input Stage ◽  
Frequency Resonance ◽  
Theta Frequency ◽  
Millisecond Time Scale
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