Auditory Discrimination of Amplitude Modulations Based on Metric Distances of Spike Trains

2007 ◽  
Vol 97 (4) ◽  
pp. 3082-3092 ◽  
Author(s):  
Sandra Wohlgemuth ◽  
Bernhard Ronacher
Keyword(s):  
Spike Train ◽  
Temporal Resolution ◽  
Transfer Functions ◽  
Time Window ◽  
Discrimination Performance ◽  
Spike Trains ◽  
Spike Timing ◽  
Spike Count ◽  
Communication Signals ◽  
Metric Distances

Sound envelope cues play a crucial role for the recognition and discrimination of communication signals in diverse taxa, such as vertebrates and arthropods. Using a classification based on metric similarities of spike trains we investigate how well amplitude modulations (AMs) of sound signals can be distinguished at three levels of the locust's auditory pathway: receptors and local and ascending neurons. The spike train metric has the advantage of providing information about the necessary evaluation time window and about the optimal temporal resolution of processing, thereby yielding clues to possible coding principles. It further allows one to disentangle the respective contributions of spike count and spike timing to the fidelity of discrimination. These results are compared with the traditional paradigm using modulation transfer functions. Spike trains of receptors and two primary-like local interneurons enable an excellent discrimination of different AM frequencies, up to about 150 Hz. In these neurons discriminability depends almost completely on the timing of spikes, which must be evaluated with a temporal resolution of <5 ms. Even short spike-train segments of 150 ms, equivalent to five to eight spikes, suffice for a high (70%) discrimination performance. For the third level of processing, the ascending interneurons, the overall discrimination accuracy is reduced. Spike count differences become more important for the discrimination whereas the exact timing of spikes contributes less. This shift in temporal resolution does not primarily depend on the investigated stimulus space. Rather it appears to reflect a transformation of how amplitude modulations are represented at more central stages of processing.

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.

Spike-Timing Precision Underlies the Coding Efficiency of Auditory Receptor Neurons

2006 ◽  
Vol 95 (4) ◽  
pp. 2541-2552 ◽  
Author(s):  
Ariel Rokem ◽  
Sebastian Watzl ◽  
Tim Gollisch ◽  
Martin Stemmler ◽  
Andreas V. M. Herz ◽  
...  
Keyword(s):  
Spike Train ◽  
Spike Trains ◽  
Neural Representation ◽  
Neural Firing ◽  
Specific Stimulus ◽  
Time Jitter ◽  
Information Rates ◽  
Communication Signals ◽  
Receptor Neurons ◽  
Stimulus Features

Sensory systems must translate incoming signals quickly and reliably so that an animal can act successfully in its environment. Even at the level of receptor neurons, however, functional aspects of the sensory encoding process are not yet fully understood. Specifically, this concerns the question how stimulus features and neural response characteristics lead to an efficient transmission of sensory information. To address this issue, we have recorded and analyzed spike trains from grasshopper auditory receptors, while systematically varying the stimulus statistics. The stimulus variations profoundly influenced the efficiency of neural encoding. This influence was largely attributable to the presence of specific stimulus features that triggered remarkably precise spikes whose trial-to-trial timing variability was as low as 0.15 ms—one order of magnitude shorter than typical stimulus time scales. Precise spikes decreased the noise entropy of the spike trains, thereby increasing the rate of information transmission. In contrast, the total spike train entropy, which quantifies the variety of different spike train patterns, hardly changed when stimulus conditions were altered, as long as the neural firing rate remained the same. This finding shows that stimulus distributions that were transmitted with high information rates did not invoke additional response patterns, but instead displayed exceptional temporal precision in their neural representation. The acoustic stimuli that led to the highest information rates and smallest spike-time jitter feature pronounced sound-pressure deflections lasting for 2–3 ms. These upstrokes are reminiscent of salient structures found in natural grasshopper communication signals, suggesting that precise spikes selectively encode particularly important aspects of the natural stimulus environment.

scholarly journals Repetitive firing: a quantitative study of feedback in model encoders.

1977 ◽  
Vol 69 (6) ◽  
pp. 815-848 ◽  
Author(s):  
J F Fohlmeister ◽  
R E Poppele ◽  
R L Purple
Keyword(s):  
Spike Train ◽  
Transfer Functions ◽  
Accurate Method ◽  
Spike Trains ◽  
Repetitive Firing ◽  
Model Parameters ◽  
Sinusoidal Analysis ◽  
Leaky Integrator ◽  
Spike Train Data ◽  
The Times

Recognition of nonlinearities in the neuronal encoding of repetitive spike trains has generated a number of models to explain this behavior. Here we develop the mathematics and a set of tests for two such models: the leaky integrator and the variable-gamma model. Both of these are nearly sufficient to explain the dynamic behavior of a number of repetitively firing, sensory neurons. Model parameters can be related to possible underlying basic mechanisms. Summed and nonsummed, spike-locked negative feedback are examined in conjunction with the models. Transfer functions are formulated to predict responses to steady state, steps, and sinusoidally varying stimuli in which output data are the times of spike-train events only. An electrical analog model for the leaky integrator is tested to verify predicted responses. Curve fitting and parameter variation techniques are explored for the purpose of extracting basic model parameters from spike train data. Sinusoidal analysis of spike trains appear to be a very accurate method for determining spike-locked feedback parameters, and it is to a large extent a model independent method that may also be applied to neuronal responses.

scholarly journals Designing optimal stimuli to control neuronal spike timing

2011 ◽  
Vol 106 (2) ◽  
pp. 1038-1053 ◽  
Author(s):  
Yashar Ahmadian ◽  
Adam M. Packer ◽  
Rafael Yuste ◽  
Liam Paninski
Keyword(s):  
Spike Train ◽  
Laser Light ◽  
Pyramidal Cells ◽  
Electrical Current ◽  
Optimization Methods ◽  
Spike Trains ◽  
Spike Timing ◽  
Safety Measures ◽  
Physiological Constraints ◽  
Cortical Slices

Recent advances in experimental stimulation methods have raised the following important computational question: how can we choose a stimulus that will drive a neuron to output a target spike train with optimal precision, given physiological constraints? Here we adopt an approach based on models that describe how a stimulating agent (such as an injected electrical current or a laser light interacting with caged neurotransmitters or photosensitive ion channels) affects the spiking activity of neurons. Based on these models, we solve the reverse problem of finding the best time-dependent modulation of the input, subject to hardware limitations as well as physiologically inspired safety measures, that causes the neuron to emit a spike train that with highest probability will be close to a target spike train. We adopt fast convex constrained optimization methods to solve this problem. Our methods can potentially be implemented in real time and may also be generalized to the case of many cells, suitable for neural prosthesis applications. With the use of biologically sensible parameters and constraints, our method finds stimulation patterns that generate very precise spike trains in simulated experiments. We also tested the intracellular current injection method on pyramidal cells in mouse cortical slices, quantifying the dependence of spiking reliability and timing precision on constraints imposed on the applied currents.

scholarly journals Wavelet Average Coefficient: a New Statistical Feature for Capturing the Temporal Pattern of Spike Trains, With Application in Brain Machine Interface

2021 ◽  
Author(s):  
Shixian Wen ◽  
Allen Yin ◽  
Po-He Tseng ◽  
Laurent Itti ◽  
Mikhail Lebedev ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Sliding Window ◽  
Temporal Patterns ◽  
Spike Trains ◽  
Spike Count ◽  
Neurological Injury ◽  
High Temporal Resolution ◽  
Statistical Feature ◽  
Long Time ◽  
Window Approach

Abstract Motor brain machine interfaces (BMI) directly link the brain to artificial actuators and have the potential to mitigate severe body paralysis caused by neurological injury or disease. Most BMI systems involve a decoder that analyzes neural spike counts to infer movement intent. However, many classical BMI decoders 1) fail to take advantage of temporal patterns of spike trains, possibly over long time horizons; 2) are insufficient to achieve good BMI performance at high temporal resolution, as the underlying Gaussian assumption of decoders based on spike counts is violated. Here, we propose a new statistical feature that represents temporal patterns or temporal codes of spike events with richer description - wavelet average coefficients (WAC) - to be used as decoder input instead of spike counts. We constructed a wavelet decoder framework by using WAC features with a sliding-window approach, and compared the resulting decoder against classical decoders (Wiener and Kalman family) using spike count features. We found that the sliding-window approach boosts decoding temporal resolution, and using WAC features significantly improves decoding performance over using spike count features.

scholarly journals Fano Factor: A Potentially Useful Information

2020 ◽  
Vol 14 ◽  
Author(s):  
Kamil Rajdl ◽  
Petr Lansky ◽  
Lubomir Kostal
Keyword(s):  
Experimental Data ◽  
Spike Train ◽  
Time Window ◽  
Fano Factor ◽  
Spike Trains ◽  
Renewal Processes ◽  
Neuronal Spike ◽  
Markov Renewal Processes ◽  
Neuronal Spike Trains ◽  
Operational Time

The Fano factor, defined as the variance-to-mean ratio of spike counts in a time window, is often used to measure the variability of neuronal spike trains. However, despite its transparent definition, careless use of the Fano factor can easily lead to distorted or even wrong results. One of the problems is the unclear dependence of the Fano factor on the spiking rate, which is often neglected or handled insufficiently. In this paper we aim to explore this problem in more detail and to study the possible solution, which is to evaluate the Fano factor in the operational time. We use equilibrium renewal and Markov renewal processes as spike train models to describe the method in detail, and we provide an illustration on experimental data.

Cortical Representation of Auditory Space: Information-Bearing Features of Spike Patterns

2002 ◽  
Vol 87 (4) ◽  
pp. 1749-1762 ◽  
Author(s):  
Shigeto Furukawa ◽  
John C. Middlebrooks
Keyword(s):  
Sound Source ◽  
Neuronal Response ◽  
Source Location ◽  
Spike Timing ◽  
Spike Count ◽  
Cortical Representation ◽  
Auditory Space ◽  
Transmitted Information ◽  
Related Information ◽  
Spike Patterns

Previous studies have demonstrated that the spike patterns of cortical neurons vary systematically as a function of sound-source location such that the response of a single neuron can signal the location of a sound source throughout 360° of azimuth. The present study examined specific features of spike patterns that might transmit information related to sound-source location. Analysis was based on responses of well-isolated single units recorded from cortical area A2 in α-chloralose-anesthetized cats. Stimuli were 80-ms noise bursts presented from loudspeakers in the horizontal plane; source azimuths ranged through 360° in 20° steps. Spike patterns were averaged across samples of eight trials. A competitive artificial neural network (ANN) identified sound-source locations by recognizing spike patterns; the ANN was trained using the learning vector quantization learning rule. The information about stimulus location that was transmitted by spike patterns was computed from joint stimulus-response probability matrices. Spike patterns were manipulated in various ways to isolate particular features. Full-spike patterns, which contained all spike-count information and spike timing with 100-μs precision, transmitted the most stimulus-related information. Transmitted information was sensitive to disruption of spike timing on a scale of more than ∼4 ms and was reduced by an average of ∼35% when spike-timing information was obliterated entirely. In a condition in which all but the first spike in each pattern were eliminated, transmitted information decreased by an average of only ∼11%. In many cases, that condition showed essentially no loss of transmitted information. Three unidimensional features were extracted from spike patterns. Of those features, spike latency transmitted ∼60% more information than that transmitted either by spike count or by a measure of latency dispersion. Information transmission by spike patterns recorded on single trials was substantially reduced compared with the information transmitted by averages of eight trials. In a comparison of averaged and nonaveraged responses, however, the information transmitted by latencies was reduced by only ∼29%, whereas information transmitted by spike counts was reduced by 79%. Spike counts clearly are sensitive to sound-source location and could transmit information about sound-source locations. Nevertheless, the present results demonstrate that the timing of the first poststimulus spike carries a substantial amount, probably the majority, of the location-related information present in spike patterns. The results indicate that any complete model of the cortical representation of auditory space must incorporate the temporal characteristics of neuronal response patterns.

scholarly journals A Biologically Plausible Computational Model for Auditory Object Recognition

2009 ◽  
Vol 101 (1) ◽  
pp. 323-331 ◽  
Author(s):  
Eric Larson ◽  
Cyrus P. Billimoria ◽  
Kamal Sen
Keyword(s):  
Object Recognition ◽  
Spike Train ◽  
Nearest Neighbor ◽  
Primary Auditory Cortex ◽  
Spike Trains ◽  
Coincidence Detection ◽  
Model Based ◽  
Plausible Model ◽  
Field L ◽  
Song Discrimination

Object recognition is a task of fundamental importance for sensory systems. Although this problem has been intensively investigated in the visual system, relatively little is known about the recognition of complex auditory objects. Recent work has shown that spike trains from individual sensory neurons can be used to discriminate between and recognize stimuli. Multiple groups have developed spike similarity or dissimilarity metrics to quantify the differences between spike trains. Using a nearest-neighbor approach the spike similarity metrics can be used to classify the stimuli into groups used to evoke the spike trains. The nearest prototype spike train to the tested spike train can then be used to identify the stimulus. However, how biological circuits might perform such computations remains unclear. Elucidating this question would facilitate the experimental search for such circuits in biological systems, as well as the design of artificial circuits that can perform such computations. Here we present a biologically plausible model for discrimination inspired by a spike distance metric using a network of integrate-and-fire model neurons coupled to a decision network. We then apply this model to the birdsong system in the context of song discrimination and recognition. We show that the model circuit is effective at recognizing individual songs, based on experimental input data from field L, the avian primary auditory cortex analog. We also compare the performance and robustness of this model to two alternative models of song discrimination: a model based on coincidence detection and a model based on firing rate.

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 Response recovery in the locust auditory pathway

2016 ◽  
Vol 115 (1) ◽  
pp. 510-519
Author(s):  
Sarah Wirtssohn ◽  
Bernhard Ronacher
Keyword(s):  
Temporal Resolution ◽  
Auditory Processing ◽  
Locusta Migratoria ◽  
Nonlinear Effects ◽  
Auditory Pathway ◽  
Relevant Information ◽  
Spike Timing ◽  
Waveform Analysis ◽  
Grasshopper Species ◽  
Response Recovery

Temporal resolution and the time courses of recovery from acute adaptation of neurons in the auditory pathway of the grasshopper Locusta migratoria were investigated with a response recovery paradigm. We stimulated with a series of single click and click pair stimuli while performing intracellular recordings from neurons at three processing stages: receptors and first and second order interneurons. The response to the second click was expressed relative to the single click response. This allowed the uncovering of the basic temporal resolution in these neurons. The effect of adaptation increased with processing layer. While neurons in the auditory periphery displayed a steady response recovery after a short initial adaptation, many interneurons showed nonlinear effects: most prominent a long-lasting suppression of the response to the second click in a pair, as well as a gain in response if a click was preceded by a click a few milliseconds before. Our results reveal a distributed temporal filtering of input at an early auditory processing stage. This set of specified filters is very likely homologous across grasshopper species and thus forms the neurophysiological basis for extracting relevant information from a variety of different temporal signals. Interestingly, in terms of spike timing precision neurons at all three processing layers recovered very fast, within 20 ms. Spike waveform analysis of several neuron types did not sufficiently explain the response recovery profiles implemented in these neurons, indicating that temporal resolution in neurons located at several processing layers of the auditory pathway is not necessarily limited by the spike duration and refractory period.

Sign in / Sign up

Export Citation Format

Share Document