Spike-frequency adaptation: Phenomenological model and experimental tests

Benda, Jan; Bethge, Matthias; Hennig, Matthias R; Pawelzik, Klaus; Herz, Andreas V. M.

doi:10.1016/s0925-2312(01)00545-8

Cited by 13 publications

(13 citation statements)

References 6 publications

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“…Both operations can be subsumed under the phenomenon of spike-frequency adaptation, the decrease of neuronal firing in response to prolonged stimulation (Benda et al, 2001;Lundstrom et al, 2008;Tripp and Eliasmith, 2010). The relation between spike-frequency adaptation and temporal sparseness has been reported previously (Farkhooi et al, 2009;Houghton, 2009;Nawrot, 2012).…”

mentioning

confidence: 65%

“…The relation between spike-frequency adaptation and temporal sparseness has been reported previously (Farkhooi et al, 2009;Houghton, 2009;Nawrot, 2012). Adaptation has been described in terms of differentiation (Lundstrom et al, 2008) or as a highpass filter (Benda et al, 2001), transformations that reduce temporal correlations and thereby increase temporal sparseness (Wang et al, 2003;Tripp and Eliasmith, 2010). The STC filter in derivative-like cells thus likely implements adaptation by determining the derivative of the stimulus (Figs.…”

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confidence: 68%

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Nonlinear Computations Underlying Temporal and Population Sparseness in the Auditory System of the Grasshopper

Clemens

Wohlgemuth²,

Ronacher³

2012

Journal of Neuroscience

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Sparse coding schemes are employed by many sensory systems and implement efficient coding principles. Yet, the computations yielding sparse representations are often only partly understood. The early auditory system of the grasshopper produces a temporally and population-sparse representation of natural communication signals. To reveal the computations generating such a code, we estimated 1D and 2D linear-nonlinear models. We then used these models to examine the contribution of different model components to response sparseness.2D models were better able to reproduce the sparseness measured in the system: while 1D models only captured 55% of the population sparseness at the network's output, 2D models accounted for 88% of it. Looking at the model structure, we could identify two types of computation, which increase sparseness. First, a sensitivity to the derivative of the stimulus and, second, the combination of a fast, excitatory and a slow, suppressive feature. Both were implemented in different classes of cells and increased the specificity and diversity of responses. The two types produced more transient responses and thereby amplified temporal sparseness. Additionally, the second type of computation contributed to population sparseness by increasing the diversity of feature selectivity through a wide range of delays between an excitatory and a suppressive feature.Both kinds of computation can be implemented through spike-frequency adaptation or slow inhibition-mechanisms found in many systems. Our results from the auditory system of the grasshopper are thus likely to reflect general principles underlying the emergence of sparse representations.

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confidence: 65%

mentioning

confidence: 68%

Nonlinear Computations Underlying Temporal and Population Sparseness in the Auditory System of the Grasshopper

Clemens

Wohlgemuth²,

Ronacher³

2012

Journal of Neuroscience

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“…Experimental characterizations of the strength and the time constants of adaptation indicate that adaptation primarily depends on the output level of neural activity, the firing rate (Benda et al, 2001;Benda, 2002). Here, we systematically test for additional input-driven adaptation components and their effects on the spiking activity of the auditory receptors by analyzing in vivo recordings of spike trains from single fibers in the auditory nerve.…”

Section: Resultsmentioning

confidence: 99%

“…The steadystate level of firing is equal for the two tones, and the initial firing-rate transients after stimulus onset are also strikingly similar. Generally, the time constants of adaptation in these cells show large variations; they are substantially higher when the cell is more strongly driven (Benda et al, 2001). The fact that equal firing rates lead to approximately equal time courses of adaptation thus indicates that the dynamics of adaptation are governed primarily by the firing rate (i.e., output of the neuron).…”

Section: Experimental Approachmentioning

confidence: 98%

“…So far, output-driven components have been identified as a substantial source of spike-frequency adaptation in insect mechanoreceptors (French, 1984a,b). Indications that spike-frequency adaptation in locust auditory receptor neurons is primarily output driven come from the dependence of the adaptation time constants on the firing rate of the receptors (Benda et al, 2001;Benda, 2002).…”

Section: Introductionmentioning

confidence: 99%

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Input-Driven Components of Spike-Frequency Adaptation Can Be UnmaskedIn Vivo

Gollisch¹,

Herz²

2004

J. Neurosci.

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Spike-frequency adaptation affects the response characteristics of many sensory neurons, and different biophysical processes contribute to this phenomenon. Many cellular mechanisms underlying adaptation are triggered by the spike output of the neuron in a feedback manner (e.g., specific potassium currents that are primarily activated by the spiking activity). In contrast, other components of adaptation may be caused by, in a feedforward way, the sensory or synaptic input, which the neuron receives. Examples include viscoelasticity of mechanoreceptors, transducer adaptation in hair cells, and short-term synaptic depression. For a functional characterization of spike-frequency adaptation, it is essential to understand the dependence of adaptation on the input and output of the neuron. Here, we demonstrate how an input-driven component of adaptation can be uncovered in vivo from recordings of spike trains in an insect auditory receptor neuron, even if the total adaptation is dominated by output-driven components. Our method is based on the identification of different inputs that yield the same output and sudden switches between these inputs. In particular, we determined for different sound frequencies those intensities that are required to yield a predefined steady-state firing rate of the neuron. We then found that switching between these sound frequencies causes transient deviations of the firing rate. These firing-rate deflections are evidence of input-driven adaptation and can be used to quantify how this adaptation component affects the neural activity. Based on previous knowledge of the processes in auditory transduction, we conclude that for the investigated auditory receptor neurons, this adaptation phenomenon is of mechanical origin.

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Influence of sound pressure level on the processing of amplitude modulations by auditory neurons of the locust

Weschke

Ronacher

2007

J Comp Physiol A

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Typical features of natural sounds are amplitude changes at different time scales. In many species, amplitude modulations constitute decisive cues to recognize communication signals. Since these signals should be recognizable over a broad intensity range, we investigated how the encoding of amplitude modulations by auditory neurons depends on sound pressure level. Identified neurons that represent different processing stages in the locusts' auditory pathway were stimulated with sinusoidal modulations of a broad band noise carrier, at different intensities, and characteristic parameters of modulation transfer functions (MTFs) were determined. The corner frequencies of temporal MTFs turned out to be independent of intensity for all neurons except one. Furthermore, for none of the neurons investigated corner frequencies were significantly correlated with spike rate, indicating a remarkable intensity invariance of the upper limits of temporal resolution. The shape of the tMTFs changed with increasing intensity from a low-pass to a band-pass for receptors and local neurons, while no consistent change was observed for ascending neurons. The best modulation frequency depended on intensity and spike rate, especially for receptors and local neurons. Remarkably, the adaptation state of some neurons turned out to be independent of the spike rate during the modulation part of the stimulus.

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Spike-frequency adaptation: Phenomenological model and experimental tests

Cited by 13 publications

References 6 publications

Nonlinear Computations Underlying Temporal and Population Sparseness in the Auditory System of the Grasshopper

Nonlinear Computations Underlying Temporal and Population Sparseness in the Auditory System of the Grasshopper

Input-Driven Components of Spike-Frequency Adaptation Can Be UnmaskedIn Vivo

Influence of sound pressure level on the processing of amplitude modulations by auditory neurons of the locust

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