Slow Covariations in Neuronal Resting Potentials Can Lead to Artefactually Fast Cross-Correlations in Their Spike Trains

1998 ◽  
Vol 80 (6) ◽  
pp. 3345-3351 ◽  
Author(s):  
Carlos D. Brody
Keyword(s):  
Spike Trains ◽  
Spike Timing ◽  
Resting Potential ◽  
Timing Synchronization ◽  
General Point ◽  
Order Of Magnitude ◽  
Cross Correlations ◽  
Peak Widths ◽  
Slow Timescale ◽  
Fast Spike

Brody, Carlos D. Slow covariations in neuronal resting potentials can lead to artefactually fast cross-correlations in their spike trains. J. Neurophysiol. 80: 3345–3351, 1998. A model of two lateral geniculate nucleus (LGN) cells, which interact only through slow (tens of seconds) covariations in their resting membrane potentials, is used here to investigate the effect of such covariations on cross-correlograms taken during stimulus-driven conditions. Despite the slow timescale of the interactions, the model generates cross-correlograms with peak widths in the range of 25–200 ms. These bear a striking resemblance to those reported in studies of LGN cells by Sillito et al., which were taken at the time as evidence of a fast spike timing synchronization interaction; the model highlights the possibility that those correlogram peaks may have been caused by a mechanism other than spike synchronization. Slow resting potential covariations are suggested instead as the dominant generating mechanism. How can a slow interaction generate covariogram peaks with a width 100–1,000 times thinner than its timescale? Broad peaks caused by slow interactions are modulated by the cells' poststimulus time histograms (PSTHs). When the PSTHs have thin peaks (e.g., tens of milliseconds), the cross-correlogram peaks generated by slow interactions will also be thin; such peaks are easily misinterpretable as being caused by fast interactions. Although this point is explored here in the context of LGN recordings, it is a general point and applies elsewhere. When cross-correlogram peak widths are of the same order of magnitude as PSTH peak widths, experiments designed to reveal short-timescale interactions must be interpreted with the issue of possible contributions from slower interactions in mind.

scholarly journals Correlations Without Synchrony

1999 ◽  
Vol 11 (7) ◽  
pp. 1537-1551 ◽  
Author(s):  
Carlos D. Brody
Keyword(s):  
Spike Train ◽  
Response Latency ◽  
Quantitative Methods ◽  
Neuronal Excitability ◽  
Spike Trains ◽  
Spike Timing ◽  
Previous Article ◽  
Timing Synchronization

Peaks in spike train correlograms are usually taken as indicative of spike timing synchronization between neurons. Strictly speaking, however, a peak merely indicates that the two spike trains were not independent. Two biologically plausible ways of departing from independence that are capable of generating peaks very similar to spike timing peaks are described here: covariations over trials in response latency and covariations over trials in neuronal excitability. Since peaks due to these interactions can be similar to spike timing peaks, interpreting a correlogram may be a problem with ambiguous solutions. What peak shapes do latency or excitability interactions generate? When are they similar to spike timing peaks? When can they be ruled out from having caused an observed correlogram peak? These are the questions addressed here. The previous article in this issue proposes quantitative methods to tell cases apart when latency or excitability covariations cannot be ruled out.

A Computational Model as Neurodecoder Based on Synchronous Oscillation in the Visual Cortex

2003 ◽  
Vol 15 (10) ◽  
pp. 2399-2418 ◽  
Author(s):  
Zhao Songnian ◽  
Xiong Xiaoyun ◽  
Yao Guozheng ◽  
Fu Zhi
Keyword(s):  
Visual Cortex ◽  
Computational Model ◽  
Visual Information ◽  
Spike Trains ◽  
Spike Timing ◽  
Contour Detection ◽  
System Level ◽  
Neuronal Spike ◽  
Neuronal Spike Trains ◽  
External Stimuli

Based on synchronized responses of neuronal populations in the visual cortex to external stimuli, we proposed a computational model consisting primarily of a neuronal phase-locked loop (NPLL) and multiscaled operator. The former reveals the function of synchronous oscillations in the visual cortex. Regardless of which of these patterns of the spike trains may be an average firing-rate code, a spike-timing code, or a rate-time code, the NPLL can decode original visual information from neuronal spike trains modulated with patterns of external stimuli, because a voltage-controlled oscillator (VCO), which is included in the NPLL, can precisely track neuronal spike trains and instantaneous variations, that is, VCO can make a copy of an external stimulus pattern. The latter, however, describes multi-scaled properties of visual information processing, but not merely edge and contour detection. In this study, in which we combined NPLL with a multiscaled operator and maximum likelihood estimation, we proved that the model, as a neurodecoder, implements optimum algorithm decoding visual information from neuronal spike trains at the system level. At the same time, the model also obtains increasingly important supports, which come from a series of experimental results of neurobiology on stimulus-specific neuronal oscillations or synchronized responses of the neuronal population in the visual cortex. In addition, the problem of how to describe visual acuity and multiresolution of vision by wavelet transform is also discussed. The results indicate that the model provides a deeper understanding of the role of synchronized responses in decoding visual information.

scholarly journals Low error discrimination using a correlated population code

2012 ◽  
Vol 108 (4) ◽  
pp. 1069-1088 ◽  
Author(s):  
Greg Schwartz ◽  
Jakob Macke ◽  
Dario Amodei ◽  
Hanlin Tang ◽  
Michael J. Berry
Keyword(s):  
Spatial Information ◽  
Ganglion Cells ◽  
Discrimination Performance ◽  
Spike Timing ◽  
Multiple Time ◽  
Entropy Model ◽  
Decoding Algorithms ◽  
Single Spike ◽  
Order Of Magnitude ◽  
Spike Latency

We explored the manner in which spatial information is encoded by retinal ganglion cell populations. We flashed a set of 36 shape stimuli onto the tiger salamander retina and used different decoding algorithms to read out information from a population of 162 ganglion cells. We compared the discrimination performance of linear decoders, which ignore correlation induced by common stimulation, with nonlinear decoders, which can accurately model these correlations. Similar to previous studies, decoders that ignored correlation suffered only a modest drop in discrimination performance for groups of up to ∼30 cells. However, for more realistic groups of 100+ cells, we found order-of-magnitude differences in the error rate. We also compared decoders that used only the presence of a single spike from each cell with more complex decoders that included information from multiple spike counts and multiple time bins. More complex decoders substantially outperformed simpler decoders, showing the importance of spike timing information. Particularly effective was the first spike latency representation, which allowed zero discrimination errors for the majority of shape stimuli. Furthermore, the performance of nonlinear decoders showed even greater enhancement compared with linear decoders for these complex representations. Finally, decoders that approximated the correlation structure in the population by matching all pairwise correlations with a maximum entropy model fit to all 162 neurons were quite successful, especially for the spike latency representation. Together, these results suggest a picture in which linear decoders allow a coarse categorization of shape stimuli, whereas nonlinear decoders, which take advantage of both correlation and spike timing, are needed to achieve high-fidelity discrimination.

scholarly journals Reconstructing neuronal circuitry from parallel spike trains

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Ryota Kobayashi ◽  
Shuhei Kurita ◽  
Anno Kurth ◽  
Katsunori Kitano ◽  
Kenji Mizuseki ◽  
...  
Keyword(s):  
Generalized Linear Model ◽  
State Of The Art ◽  
Synthetic Data ◽  
Brain Regions ◽  
Spike Trains ◽  
Circuit Diagram ◽  
Estimation Errors ◽  
Neuronal Circuitry ◽  
Large Numbers ◽  
Cross Correlations

Abstract State-of-the-art techniques allow researchers to record large numbers of spike trains in parallel for many hours. With enough such data, we should be able to infer the connectivity among neurons. Here we develop a method for reconstructing neuronal circuitry by applying a generalized linear model (GLM) to spike cross-correlations. Our method estimates connections between neurons in units of postsynaptic potentials and the amount of spike recordings needed to verify connections. The performance of inference is optimized by counting the estimation errors using synthetic data. This method is superior to other established methods in correctly estimating connectivity. By applying our method to rat hippocampal data, we show that the types of estimated connections match the results inferred from other physiological cues. Thus our method provides the means to build a circuit diagram from recorded spike trains, thereby providing a basis for elucidating the differences in information processing in different brain regions.

Spike-timing-dependent synaptic modification induced by natural spike trains

Nature ◽  
2002 ◽  
Vol 416 (6879) ◽  
pp. 433-438 ◽  
Author(s):  
Robert C. Froemke ◽  
Yang Dan
Keyword(s):  
Spike Trains ◽  
Spike Timing ◽  
Synaptic Modification

Competitive STDP-Based Spike Pattern Learning

2009 ◽  
Vol 21 (5) ◽  
pp. 1259-1276 ◽  
Author(s):  
Timothée Masquelier ◽  
Rudy Guyonneau ◽  
Simon J. Thorpe
Keyword(s):  
Single Neuron ◽  
Spike Trains ◽  
Spike Timing ◽  
Temporal Coding ◽  
Spike Pattern ◽  
Distributed Coding ◽  
Coding Scheme ◽  
Winner Take All ◽  
The Brain ◽  
Repeating Patterns

Recently it has been shown that a repeating arbitrary spatiotemporal spike pattern hidden in equally dense distracter spike trains can be robustly detected and learned by a single neuron equipped with spike-timing-dependent plasticity (STDP) (Masquelier, Guyonneau, & Thorpe, 2008). To be precise, the neuron becomes selective to successive coincidences of the pattern. Here we extend this scheme to a more realistic scenario with multiple repeating patterns and multiple STDP neurons “listening” to the incoming spike trains. These “listening” neurons are in competition: as soon as one fires, it strongly inhibits the others through lateral connections (one-winner-take-all mechanism). This tends to prevent the neurons from learning the same (parts of the) repeating patterns, as shown in simulations. Instead, the population self-organizes, trying to cover the different patterns or coding one pattern by the successive firings of several neurons, and a powerful distributed coding scheme emerges. Taken together, these results illustrate how the brain could easily encode and decode information in the spike times, a theory referred to as temporal coding, and how STDP could play a key role by detecting repeating patterns and generating selective response to them.

Synaptic Depression Leads to Nonmonotonic Frequency Dependence in the Coincidence Detector

2003 ◽  
Vol 15 (10) ◽  
pp. 2339-2358 ◽  
Author(s):  
Shawn Mikula ◽  
Ernst Niebur
Keyword(s):  
Synaptic Depression ◽  
Spike Trains ◽  
Input Rate ◽  
Output Rate ◽  
Coincidence Detector ◽  
Perfect Correlation ◽  
Zero Correlation ◽  
Cross Correlations ◽  
Input Spike ◽  
Special Case

In this letter, we extend our previous analytical results (Mikula & Niebur, 2003) for the coincidence detector by taking into account probabilistic frequency-dependent synaptic depression. We present a solution for the steady-state output rate of an ideal coincidence detector receiving an arbitrary number of input spike trains with identical binomial count distributions (which includes Poisson statistics as a special case) and identical arbitrary pairwise cross-correlations, from zero correlation (independent processes) to perfect correlation (identical processes). Synapses vary their efficacy probabilistically according to the observed depression mechanisms. Our results show that synaptic depression, if made sufficiently strong, will result in an inverted U-shaped curve for the output rate of a coincidence detector as a function of input rate. This leads to the counterintuitive prediction that higher presynaptic (input) rates may lead to lower postsynaptic (output) rates where the output rate may fall faster than the inverse of the input rate.

What Can a Neuron Learn with Spike-Timing-Dependent Plasticity?

2005 ◽  
Vol 17 (11) ◽  
pp. 2337-2382 ◽  
Author(s):  
Robert Legenstein ◽  
Christian Naeger ◽  
Wolfgang Maass
Keyword(s):  
Supervised Learning ◽  
Computer Simulations ◽  
Convergence Theorem ◽  
Spike Trains ◽  
Spike Timing ◽  
Spiking Neurons ◽  
Linear Separability ◽  
Poisson Input ◽  
Dynamic Synapses ◽  
Input Spike

Spiking neurons are very flexible computational modules, which can implement with different values of their adjustable synaptic parameters an enormous variety of different transformations F from input spike trains to output spike trains. We examine in this letter the question to what extent a spiking neuron with biologically realistic models for dynamic synapses can be taught via spike-timing-dependent plasticity (STDP) to implement a given transformation F. We consider a supervised learning paradigm where during training, the output of the neuron is clamped to the target signal (teacher forcing). The well-known perceptron convergence theorem asserts the convergence of a simple supervised learning algorithm for drastically simplified neuron models (McCulloch-Pitts neurons). We show that in contrast to the perceptron convergence theorem, no theoretical guarantee can be given for the convergence of STDP with teacher forcing that holds for arbitrary input spike patterns. On the other hand, we prove that average case versions of the perceptron convergence theorem hold for STDP in the case of uncorrelated and correlated Poisson input spike trains and simple models for spiking neurons. For a wide class of cross-correlation functions of the input spike trains, the resulting necessary and sufficient condition can be formulated in terms of linear separability, analogously as the well-known condition of learnability by perceptrons. However, the linear separability criterion has to be applied here to the columns of the correlation matrix of the Poisson input. We demonstrate through extensive computer simulations that the theoretically predicted convergence of STDP with teacher forcing also holds for more realistic models for neurons, dynamic synapses, and more general input distributions. In addition, we show through computer simulations that these positive learning results hold not only for the common interpretation of STDP, where STDP changes the weights of synapses, but also for a more realistic interpretation suggested by experimental data where STDP modulates the initial release probability of dynamic synapses.

scholarly journals Predicting spike timing in highly synchronous auditory neurons at different sound levels

2013 ◽  
Vol 110 (7) ◽  
pp. 1672-1688 ◽  
Author(s):  
Bertrand Fontaine ◽  
Victor Benichoux ◽  
Philip X. Joris ◽  
Romain Brette
Keyword(s):  
Temporal Structure ◽  
Spike Trains ◽  
Sound Level ◽  
Spike Timing ◽  
Spike Threshold ◽  
Integrate And Fire ◽  
Fire Models ◽  
Wide Range

A challenge for sensory systems is to encode natural signals that vary in amplitude by orders of magnitude. The spike trains of neurons in the auditory system must represent the fine temporal structure of sounds despite a tremendous variation in sound level in natural environments. It has been shown in vitro that the transformation from dynamic signals into precise spike trains can be accurately captured by simple integrate-and-fire models. In this work, we show that the in vivo responses of cochlear nucleus bushy cells to sounds across a wide range of levels can be precisely predicted by deterministic integrate-and-fire models with adaptive spike threshold. Our model can predict both the spike timings and the firing rate in response to novel sounds, across a large input level range. A noisy version of the model accounts for the statistical structure of spike trains, including the reliability and temporal precision of responses. Spike threshold adaptation was critical to ensure that predictions remain accurate at different levels. These results confirm that simple integrate-and-fire models provide an accurate phenomenological account of spike train statistics and emphasize the functional relevance of spike threshold adaptation.

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