On the interpretation of sensitivity analyses of neural responses

Brette, Romain

doi:10.1121/1.3488311

Cited by 14 publications

(13 citation statements)

References 19 publications

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“…Figure 7A also directly contradicts the claim that the optimal code for ITD consists of two populations of identically tuned neurons (Harper and McAlpine, 2004). On the contrary, heterogeneity of tunings is critical for robust estimation, consistently with theoretical arguments (Brette, 2010). …”

Section: Discussionsupporting

confidence: 78%

“…This conclusion is wrong in general when there are stimulus-dependent correlations (Brette, 2010). In this case, as we have shown, the structure of neural correlations contains useful information that can be exploited by simple decoders.…”

Section: Discussionmentioning

confidence: 99%

“…In a simple situation where only the ITD varies, the optimal code is the most sensitive one. In complex situations where other dimensions also vary, there is a trade-off between sensitivity and robustness, so the optimal code is not the most sensitive one (Brette, 2010). In fact, theory predicts that in complex situations, the heterogeneity of ITD tunings is critical to produce robust estimates.…”

Section: Introductionmentioning

confidence: 99%

“…But the variability of the stimulus, which is shared, implies that a better decoder must use the structure of correlations. For example, in a Jeffress-like model, changes in the sound (e.g., level, background noise) results in changes in the activity of all neurons, but the identity of the most active neuron does not change (a theoretical argument made in Brette, 2010). …”

mentioning

confidence: 99%

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Decoding neural responses to temporal cues for sound localization

Goodman

Benichoux

Brette

2013

eLife

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The activity of sensory neural populations carries information about the environment. This may be extracted from neural activity using different strategies. In the auditory brainstem, a recent theory proposes that sound location in the horizontal plane is decoded from the relative summed activity of two populations in each hemisphere, whereas earlier theories hypothesized that the location was decoded from the identity of the most active cells. We tested the performance of various decoders of neural responses in increasingly complex acoustical situations, including spectrum variations, noise, and sound diffraction. We demonstrate that there is insufficient information in the pooled activity of each hemisphere to estimate sound direction in a reliable way consistent with behavior, whereas robust estimates can be obtained from neural activity by taking into account the heterogeneous tuning of cells. These estimates can still be obtained when only contralateral neural responses are used, consistently with unilateral lesion studies.DOI: http://dx.doi.org/10.7554/eLife.01312.001

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Section: Discussionsupporting

confidence: 78%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Decoding neural responses to temporal cues for sound localization

Goodman

Benichoux

Brette

2013

eLife

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“…First, best IPDs are more scattered in the data than in the model, with the subpopulations appearing more as peaks in a distribution than discrete points. This scatter may be due to the inherent variability of biological systems, or perhaps to an increase in the degree of heterogeneity in the neural responses to better deal with variation in other stimulus dimensions, or in order to carry out coding tasks not requiring discrimination [41]. Nevertheless, the overall frequency-dependent positioning of the peaks in the distribution of best IPDs is consistent with the optimal-coding model (across a wide range of species), indicating the importance of precise IPD estimation and discrimination for these neurons.…”

Section: Discussionmentioning

confidence: 99%

The Neural Code for Auditory Space Depends on Sound Frequency and Head Size in an Optimal Manner

et al. 2014

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A major cue to the location of a sound source is the interaural time difference (ITD)–the difference in sound arrival time at the two ears. The neural representation of this auditory cue is unresolved. The classic model of ITD coding, dominant for a half-century, posits that the distribution of best ITDs (the ITD evoking a neuron’s maximal response) is unimodal and largely within the range of ITDs permitted by head-size. This is often interpreted as a place code for source location. An alternative model, based on neurophysiology in small mammals, posits a bimodal distribution of best ITDs with exquisite sensitivity to ITDs generated by means of relative firing rates between the distributions. Recently, an optimal-coding model was proposed, unifying the disparate features of these two models under the framework of efficient coding by neural populations. The optimal-coding model predicts that distributions of best ITDs depend on head size and sound frequency: for high frequencies and large heads it resembles the classic model, for low frequencies and small head sizes it resembles the bimodal model. The optimal-coding model makes key, yet unobserved, predictions: for many species, including humans, both forms of neural representation are employed, depending on sound frequency. Furthermore, novel representations are predicted for intermediate frequencies. Here, we examine these predictions in neurophysiological data from five mammalian species: macaque, guinea pig, cat, gerbil and kangaroo rat. We present the first evidence supporting these untested predictions, and demonstrate that different representations appear to be employed at different sound frequencies in the same species.

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