scholarly journals A Compressive Sensing Approach to Inferring Cognitive Representations with Reverse Correlation

2021 ◽  
Author(s):  
Benjamin W Roop ◽  
Ben Parrell ◽  
Adam C Lammert
Keyword(s):  
Signal Processing ◽  
Compressive Sensing ◽  
Human Subjects ◽  
Correlation Method ◽  
Reverse Correlation ◽  
Sampling Efficiency ◽  
Cognitive Representations ◽  
Stimulus Response ◽  
Recent Advances ◽  
Improve Representation

Uncovering cognitive representations is an elusive goal that is increasingly pursued using the reverse correlation method. Employing reverse correlation often entails collecting thousands of stimulus-response pairs from human subjects, a burdensome task that limits the feasibility of many such studies. This methodological barrier can potentially be overcome using recent advances in signal processing designed to improve sampling efficiency, specifically compressive sensing. Here, compressive sensing is shown to be directly compatible with reverse correlation, and a trio of simulations are performed to demonstrate that compressive sensing can improve the accuracy of reconstructed representations while dramatically reducing the required number of samples. This work concludes by outlining the potential of compressive sensing to improve representation reconstruction throughout the field of neuroscience and beyond.

scholarly journals Compressive Sensing in Signal Processing: Algorithms and Transform Domain Formulations

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Irena Orović ◽  
Vladan Papić ◽  
Cornel Ioana ◽  
Xiumei Li ◽  
Srdjan Stanković
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Compressive Sensing ◽  
Signal Reconstruction ◽  
Signal Acquisition ◽  
Transform Domain ◽  
Hermite Transform ◽  
Time Frequency ◽  
Reconstruction Methods ◽  
Fourier Transform Domain

Compressive sensing has emerged as an area that opens new perspectives in signal acquisition and processing. It appears as an alternative to the traditional sampling theory, endeavoring to reduce the required number of samples for successful signal reconstruction. In practice, compressive sensing aims to provide saving in sensing resources, transmission, and storage capacities and to facilitate signal processing in the circumstances when certain data are unavailable. To that end, compressive sensing relies on the mathematical algorithms solving the problem of data reconstruction from a greatly reduced number of measurements by exploring the properties of sparsity and incoherence. Therefore, this concept includes the optimization procedures aiming to provide the sparsest solution in a suitable representation domain. This work, therefore, offers a survey of the compressive sensing idea and prerequisites, together with the commonly used reconstruction methods. Moreover, the compressive sensing problem formulation is considered in signal processing applications assuming some of the commonly used transformation domains, namely, the Fourier transform domain, the polynomial Fourier transform domain, Hermite transform domain, and combined time-frequency domain.

Time Course of Cross-Orientation Suppression in the Early Visual Cortex

2009 ◽  
Vol 101 (3) ◽  
pp. 1463-1479 ◽  
Author(s):  
Rui Kimura ◽  
Izumi Ohzawa
Keyword(s):  
Visual Cortex ◽  
Time Course ◽  
Individual Component ◽  
Correlation Method ◽  
Suppressive Effect ◽  
Negative Response ◽  
Reverse Correlation ◽  
Response Onset ◽  
Early Visual Cortex ◽  
Visual Neuron

Responses of a visual neuron to optimally oriented stimuli can be suppressed by a superposition of another grating with a different orientation. This effect is known as cross-orientation suppression. However, it is still not clear whether the effect is intracortical in origin or a reflection of subcortical processes. To address this issue, we measured spatiotemporal responses to a plaid pattern, a superposition of two gratings, as well as to individual component gratings (optimal and mask) using a subspace reverse-correlation method. Suppression for the plaid was evaluated by comparing the response to that for the optimal grating. For component stimuli, excitatory and negative responses were defined as responses more positive and negative, respectively, than that to a blank stimulus. The suppressive effect for plaids was observed in the vast majority of neurons. However, only ∼30% of neurons showed the negative response to mask-only gratings. The magnitudes of negative responses to mask-only stimuli were correlated with the degree of suppression for plaid stimuli. Comparing the latencies, we found that the suppression for the plaids starts at about the same time or slightly later than the response onset for the optimal grating and reaches its maximum at about the same time as the peak latency for the mask-only grating. Based on these results, we propose that in addition to the suppressive effect originating at the subcortical stage, delayed suppressive signals derived from the intracortical networks act on the neuron to generate cross-orientation suppression.

scholarly journals Gaussian Processes for Nonlinear Signal Processing: An Overview of Recent Advances

2013 ◽  
Vol 30 (4) ◽  
pp. 40-50 ◽  
Author(s):  
Fernando Perez-Cruz ◽  
Steven Van Vaerenbergh ◽  
Juan Jose Murillo-Fuentes ◽  
Miguel Lazaro-Gredilla ◽  
Ignacio Santamaria
Keyword(s):  
Signal Processing ◽  
Gaussian Processes ◽  
Nonlinear Signal Processing ◽  
Recent Advances ◽  
Nonlinear Signal

Medical and Biological Analysis

2013 ◽  
pp. 605-633
Author(s):  
Eugene N. Bruce
Keyword(s):  
Signal Processing ◽  
Human Subjects ◽  
Disease Process ◽  
Noise Removal ◽  
Clinical Applications ◽  
Historical Background ◽  
Processing Methods ◽  
Biological Analysis ◽  
The Status ◽  
Signal Processing Methods

Medical and biological analysis refers to the engineering methods of signal processing as applied to measurements from human subjects, with the purpose of defining the differences between normal and pathological signals, in order to detect the presence of a disease process or detect changes in the status of a patient associated with treatment. As such, the focus of this chapter is on the identification of the sources of biomedical signals and their classification. This is followed by a historical background with emphasis on clinical applications and early quantitative and engineering approaches. Subsequently, the chapter presents classical engineering methods addressing signals in one dimension, focusing on traditional signal processing methods. It then describes some contemporary engineering approaches to medical and biological analysis, and concludes by addressing filters and noise removal and signal compensation.

scholarly journals Recent Advances in Seismocardiography

Vibration ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 64-86 ◽  
Author(s):  
Amirtahà Taebi ◽  
Brian Solar ◽  
Andrew Bomar ◽  
Richard Sandler ◽  
Hansen Mansy
Keyword(s):  
Signal Processing ◽  
Low Cost ◽  
Cardiac Activity ◽  
Diagnostic Tools ◽  
Noninvasive Evaluation ◽  
Machine Learning Methods ◽  
Recent Advances ◽  
Signal Processing Algorithms ◽  
Potential Clinical Utility

Cardiovascular disease is a major cause of death worldwide. New diagnostic tools are needed to provide early detection and intervention to reduce mortality and increase both the duration and quality of life for patients with heart disease. Seismocardiography (SCG) is a technique for noninvasive evaluation of cardiac activity. However, the complexity of SCG signals introduced challenges in SCG studies. Renewed interest in investigating the utility of SCG accelerated in recent years and benefited from new advances in low-cost lightweight sensors, and signal processing and machine learning methods. Recent studies demonstrated the potential clinical utility of SCG signals for the detection and monitoring of certain cardiovascular conditions. While some studies focused on investigating the genesis of SCG signals and their clinical applications, others focused on developing proper signal processing algorithms for noise reduction, and SCG signal feature extraction and classification. This paper reviews the recent advances in the field of SCG.

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