Rat sensitivity to multipoint statistics is predicted by efficient coding of natural scenes

Efficient processing of sensory data requires adapting the neuronal encoding strategy to the statistics of natural stimuli. Previously, in Hermundstad et al., 2014, we showed that local multipoint correlation patterns that are most variable in natural images are also the most perceptually salient for human observers, in a way that is compatible with the efficient coding principle. Understanding the neuronal mechanisms underlying such adaptation to image statistics will require performing invasive experiments that are impossible in humans. Therefore, it is important to understand whether a similar phenomenon can be detected in animal species that allow for powerful experimental manipulations, such as rodents. Here we selected four image statistics (from single- to four-point correlations) and trained four groups of rats to discriminate between white noise patterns and binary textures containing variable intensity levels of one of such statistics. We interpreted the resulting psychometric data with an ideal observer model, finding a sharp decrease in sensitivity from two- to four-point correlations and a further decrease from four- to three-point. This ranking fully reproduces the trend we previously observed in humans, thus extending a direct demonstration of efficient coding to a species where neuronal and developmental processes can be interrogated and causally manipulated.

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Rat sensitivity to multipoint statistics is predicted by efficient coding of natural scenes

10.1101/2021.05.17.444510 ◽

2021 ◽

Author(s):

Riccardo Caramellino ◽

Eugenio Piasini ◽

Andrea Buccellato ◽

Anna Carboncino ◽

Vijay Balasubramanian ◽

...

Keyword(s):

Natural Images ◽

Natural Scenes ◽

Developmental Processes ◽

Efficient Coding ◽

Sensory Data ◽

Natural Stimuli ◽

Efficient Processing ◽

Multipoint Statistics ◽

Encoding Strategy ◽

Neuronal Encoding

Efficient processing of sensory data requires adapting the neuronal encoding strategy to the statistics of natural stimuli. Humans, for instance, are most sensitive to multipoint correlations that vary the most across natural images. Here we show that rats possess the same sensitivity ranking to multipoint statistics as humans, thus extending a classic demonstration of efficient coding to a species where neuronal and developmental processes can be interrogated and causally manipulated.

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Efficient coding of natural scene statistics predicts discrimination thresholds for grayscale textures

eLife ◽

10.7554/elife.54347 ◽

2020 ◽

Vol 9 ◽

Author(s):

Tiberiu Tesileanu ◽

Mary M Conte ◽

John J Briguglio ◽

Ann M Hermundstad ◽

Jonathan D Victor ◽

...

Keyword(s):

Dimensional Space ◽

Second Order ◽

Visual Sensitivity ◽

Natural Scenes ◽

Large Set ◽

Natural Scene Statistics ◽

Image Statistics ◽

Efficient Coding ◽

Fractional Error ◽

Relative Salience

Previously, in Hermundstad et al., 2014, we showed that when sampling is limiting, the efficient coding principle leads to a ‘variance is salience’ hypothesis, and that this hypothesis accounts for visual sensitivity to binary image statistics. Here, using extensive new psychophysical data and image analysis, we show that this hypothesis accounts for visual sensitivity to a large set of grayscale image statistics at a striking level of detail, and also identify the limits of the prediction. We define a 66-dimensional space of local grayscale light-intensity correlations, and measure the relevance of each direction to natural scenes. The ‘variance is salience’ hypothesis predicts that two-point correlations are most salient, and predicts their relative salience. We tested these predictions in a texture-segregation task using un-natural, synthetic textures. As predicted, correlations beyond second order are not salient, and predicted thresholds for over 300 second-order correlations match psychophysical thresholds closely (median fractional error <0.13).

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Role of Homeostasis in Learning Sparse Representations

Neural Computation ◽

10.1162/neco.2010.05-08-795 ◽

2010 ◽

Vol 22 (7) ◽

pp. 1812-1836 ◽

Cited By ~ 26

Author(s):

Laurent U. Perrinet

Keyword(s):

Sparse Coding ◽

Hebbian Learning ◽

Receptive Fields ◽

Sparse Representations ◽

Natural Images ◽

Neural Representation ◽

Natural Scenes ◽

Efficient Coding ◽

Sensory Data

Neurons in the input layer of primary visual cortex in primates develop edge-like receptive fields. One approach to understanding the emergence of this response is to state that neural activity has to efficiently represent sensory data with respect to the statistics of natural scenes. Furthermore, it is believed that such an efficient coding is achieved using a competition across neurons so as to generate a sparse representation, that is, where a relatively small number of neurons are simultaneously active. Indeed, different models of sparse coding, coupled with Hebbian learning and homeostasis, have been proposed that successfully match the observed emergent response. However, the specific role of homeostasis in learning such sparse representations is still largely unknown. By quantitatively assessing the efficiency of the neural representation during learning, we derive a cooperative homeostasis mechanism that optimally tunes the competition between neurons within the sparse coding algorithm. We apply this homeostasis while learning small patches taken from natural images and compare its efficiency with state-of-the-art algorithms. Results show that while different sparse coding algorithms give similar coding results, the homeostasis provides an optimal balance for the representation of natural images within the population of neurons. Competition in sparse coding is optimized when it is fair. By contributing to optimizing statistical competition across neurons, homeostasis is crucial in providing a more efficient solution to the emergence of independent components.

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Efficient coding of natural scene statistics predicts discrimination thresholds for grayscale textures

10.1101/2019.12.11.872994 ◽

2019 ◽

Author(s):

Tiberiu Teşileanu ◽

Mary M. Conte ◽

John J. Briguglio ◽

Ann M. Hermundstad ◽

Jonathan D. Victor ◽

...

Keyword(s):

Dimensional Space ◽

Second Order ◽

Visual Sensitivity ◽

Natural Scenes ◽

Large Set ◽

Natural Scene Statistics ◽

Image Statistics ◽

Efficient Coding ◽

Fractional Error ◽

Relative Salience

AbstractPreviously, in [1], we showed that when sampling is limiting, the efficient coding principle leads to a “variance is salience” hypothesis, and that this hypothesis accounts for visual sensitivity to binary image statistics. Here, using extensive new psychophysical data and image analysis, we show that this hypothesis accounts for visual sensitivity to a large set of grayscale image statistics at a striking level of detail, and also identify the limits of the prediction. We define a 66-dimensional space of local grayscale light-intensity correlations, and measure the relevance of each direction to natural scenes. The “variance is salience” hypothesis predicts that two-point correlations are most salient, and predicts their relative salience. We tested these predictions in a texture-segregation task using un-natural, synthetic textures. As predicted, correlations beyond second order are not salient, and predicted thresholds for over 300 second-order correlations match psychophysical thresholds closely (median fractional error < 0.13).

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Object Detection Using an Ideal Observer Model

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.16.avm-041 ◽

2020 ◽

Vol 2020 (16) ◽

pp. 41-1-41-7

Author(s):

Orit Skorka ◽

Paul J. Kane

Keyword(s):

Object Detection ◽

Matched Filter ◽

Image Sensors ◽

Ideal Observer ◽

Noise Power ◽

Relative Evaluation ◽

Detection And Identification ◽

Cross Correlation Analysis ◽

Observer Model ◽

The Ideal

Many of the metrics developed for informational imaging are useful in automotive imaging, since many of the tasks – for example, object detection and identification – are similar. This work discusses sensor characterization parameters for the Ideal Observer SNR model, and elaborates on the noise power spectrum. It presents cross-correlation analysis results for matched-filter detection of a tribar pattern in sets of resolution target images that were captured with three image sensors over a range of illumination levels. Lastly, the work compares the crosscorrelation data to predictions made by the Ideal Observer Model and demonstrates good agreement between the two methods on relative evaluation of detection capabilities.

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How Optimal is Word-Referent Identification Under Multimodal Uncertainty?

10.31234/osf.io/qvr9j ◽

2018 ◽

Author(s):

Abdellah Fourtassi ◽

Michael C. Frank

Keyword(s):

Visual Cues ◽

Word Meaning ◽

Spoken Word ◽

Ideal Observer ◽

Optimal Model ◽

Sources Of Uncertainty ◽

Multimodal Information ◽

Model Predictions ◽

Observer Model ◽

Novel Words

Identifying a spoken word in a referential context requires both the ability to integrate multimodal input and the ability to reason under uncertainty. How do these tasks interact with one another? We study how adults identify novel words under joint uncertainty in the auditory and visual modalities and we propose an ideal observer model of how cues in these modalities are combined optimally. Model predictions are tested in four experiments where recognition is made under various sources of uncertainty. We found that participants use both auditory and visual cues to recognize novel words. When the signal is not distorted with environmental noise, participants weight the auditory and visual cues optimally, that is, according to the relative reliability of each modality. In contrast, when one modality has noise added to it, human perceivers systematically prefer the unperturbed modality to a greater extent than the optimal model does. This work extends the literature on perceptual cue combination to the case of word recognition in a referential context. In addition, this context offers a link to the study of multimodal information in word meaning learning.

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Brain signatures of surprise in EEG and MEG data

10.1101/2020.01.06.895664 ◽

2020 ◽

Cited By ~ 1

Author(s):

Zahra Mousavi ◽

Mohammad Mahdi Kiani ◽

Hamid Aghajan

Keyword(s):

Sensory Modality ◽

Ideal Observer ◽

Neural Signals ◽

Sensory Data ◽

Temporal Features ◽

Recorded Data ◽

Recent Trends ◽

Temporal Sample ◽

Past Experiences ◽

The Brain

AbstractThe brain is constantly anticipating the future of sensory inputs based on past experiences. When new sensory data is different from predictions shaped by recent trends, neural signals are generated to report this surprise. Existing models for quantifying surprise are based on an ideal observer assumption operating under one of the three definitions of surprise set forth as the Shannon, Bayesian, and Confidence-corrected surprise. In this paper, we analyze both visual and auditory EEG and auditory MEG signals recorded during oddball tasks to examine which temporal components in these signals are sufficient to decode the brain’s surprise based on each of these three definitions. We found that for both recording systems the Shannon surprise is always significantly better decoded than the Bayesian surprise regardless of the sensory modality and the selected temporal features used for decoding.Author summaryA regression model is proposed for decoding the level of the brain’s surprise in response to sensory sequences using selected temporal components of recorded EEG and MEG data. Three surprise quantification definitions (Shannon, Bayesian, and Confidence-corrected surprise) are compared in offering decoding power. Four different regimes for selecting temporal samples of EEG and MEG data are used to evaluate which part of the recorded data may contain signatures that represent the brain’s surprise in terms of offering a high decoding power. We found that both the middle and late components of the EEG response offer strong decoding power for surprise while the early components are significantly weaker in decoding surprise. In the MEG response, we found that the middle components have the highest decoding power while the late components offer moderate decoding powers. When using a single temporal sample for decoding surprise, samples of the middle segment possess the highest decoding power. Shannon surprise is always better decoded than the other definitions of surprise for all the four temporal feature selection regimes. Similar superiority for Shannon surprise is observed for the EEG and MEG data across the entire range of temporal sample regimes used in our analysis.

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Evidence accumulation and change rate inference in dynamic environments

10.1101/066480 ◽

2016 ◽

Cited By ~ 1

Author(s):

Adrian E Radillo ◽

Alan Veliz-Cuba ◽

Kresimir Josic ◽

Zachary Kilpatrick

Keyword(s):

Rate Of Change ◽

Ideal Observer ◽

Moment Closure ◽

Dimensional System ◽

Change Rate ◽

Observer Model ◽

Low Dimensional ◽

State Of The Environment ◽

Rate Correlation ◽

Moment Closure Approximation

In a constantly changing world, animals must account for environmental volatility when making decisions. To appropriately discount older, irrelevant information, they need to learn the rate at which the environment changes. We develop an ideal observer model capable of inferring the present state of the environment along with its rate of change. Key to this computation is updating the posterior probability of all possible changepoint counts. This computation can be challenging, as the number of possibilities grows rapidly with time. However, we show how the computations can be simplified in the continuum limit by a moment closure approximation. The resulting low-dimensional system can be used to infer the environmental state and change rate with accuracy comparable to the ideal observer. The approximate computations can be performed by a neural network model via a rate-correlation based plasticity rule. We thus show how optimal observers accumulates evidence in changing environments, and map this computation to reduced models which perform inference using plausible neural mechanisms.

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