A computational study of the role of spatial receptive field structure in processing natural and non-natural scenes

DOI10.1016/J.JTBI.2018.06.011zbMath1406.92006OpenAlexW2807849321WikidataQ56976781 ScholiaQ56976781MaRDI QIDQ1714199

Xiuqi George Zhu, Victor J. Barranca

Publication date: 31 January 2019

Published in: Journal of Theoretical Biology (Search for Journal in Brave)

Full work available at URL: https://works.swarthmore.edu/cgi/viewcontent.cgi?article=1235&context=fac-math-stat

zbMATH Keywords

nonlinear dynamicsneuronal networkscompressive sensingsensory processingoptical illusions

Mathematics Subject Classification ID

Neural biology (92C20) Neural networks for/in biological studies, artificial life and related topics (92B20) Pattern recognition, speech recognition (68T10)

Related Items (3)

Physiological constraints of visual pathway lead to more efficient coding of information in retina ⋮ Reconstruction of sparse recurrent connectivity and inputs from the nonlinear dynamics of neuronal networks ⋮ Data-Driven Reconstruction and Encoding of Sparse Stimuli across Convergent Sensory Layers from Downstream Neuronal Network Dynamics

Uses Software

• NEURON

Cites Work

• Dynamics of the exponential integrate-and-fire model with slow currents and adaptation
• Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
• Fast Solution of $\ell _{1}$-Norm Minimization Problems When the Solution May Be Sparse
• Mean-field analysis of neuronal spike dynamics
• Continuous and Discrete Wavelet Transforms
• Stable signal recovery from incomplete and inaccurate measurements

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