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Articles 1 - 4 of 4
Full-Text Articles in Computational Neuroscience
Parametrically Constrained Lightness Model Incorporating Edge Classification And Increment-Decrement Neural Response Asymmetries, Michael E. Rudd
Parametrically Constrained Lightness Model Incorporating Edge Classification And Increment-Decrement Neural Response Asymmetries, Michael E. Rudd
MODVIS Workshop
Lightness matching data from disk-annulus experiments has the form of a parabolic (2nd-order polynomial) function when matches are plotted against annulus luminance on log-log axes. Rudd (2010) has proposed a computational cortical model to account for this fact and has subsequently (Rudd, 2013, 2014, 2015) extended the model to explain data from other lightness paradigms, including staircase-Gelb and luminance gradient illusions (Galmonte, Soranzo, Rudd, & Agostini, 2015). Here, I re-analyze parametric lightness matching data from disk-annulus experiments by Rudd and Zemach (2007) and Rudd (2010) for the purpose of further testing the model and to try to constrain …
How Deep Is The Feature Analysis Underlying Rapid Visual Categorization?, Sven Eberhardt, Jonah Cader, Thomas Serre
How Deep Is The Feature Analysis Underlying Rapid Visual Categorization?, Sven Eberhardt, Jonah Cader, Thomas Serre
MODVIS Workshop
Rapid categorization paradigms have a long history in experimental psychology: Characterized by short presentation times and fast behavioral responses, these tasks highlight both the speed and ease with which our visual system processes natural object categories. Previous studies have shown that feed-forward hierarchical models of the visual cortex provide a good fit to human visual decisions. At the same time, recent work has demonstrated significant gains in object recognition accuracy with increasingly deep hierarchical architectures: From AlexNet to VGG to Microsoft CNTK – the field of computer vision has championed both depth and accuracy. But it is unclear how well …
Predicting Phase Resetting Due To Multiple Stimuli, Kelsey M. Vollmer, Davy C. Vanderweyen, Derek R. Tuck, Sorinel A. Oprisan
Predicting Phase Resetting Due To Multiple Stimuli, Kelsey M. Vollmer, Davy C. Vanderweyen, Derek R. Tuck, Sorinel A. Oprisan
Journal of the South Carolina Academy of Science
We generalized the phase resetting curve (PRC) to a more realistic case of neural oscillators receiving two or more inputs per cycle. The PRC tabulates the transient change in the firing period of a neuron due to an external perturbation, such as a presynaptic stimulus. We used a conductance-based model neuron to estimate experimentally the two-stimulus PRC and compared the results against our mathematical prediction based on the assumption of instantaneous recurrent stimulation. Within the limits of the recurrent stimulation assumptions, we found that the newly introduced prediction for the two-stimulus PRC matched experimental measurements. Our new results open the …
Rejection Positivity Predicts Trial-To-Trial Reaction Times In An Auditory Selective Attention Task: A Computational Analysis Of Inhibitory Control, Sufen Chen, Robert D. Melara
Rejection Positivity Predicts Trial-To-Trial Reaction Times In An Auditory Selective Attention Task: A Computational Analysis Of Inhibitory Control, Sufen Chen, Robert D. Melara
Publications and Research
A series of computer simulations using variants of a formal model of attention (Melara and Algom, 2003) probed the role of rejection positivity (RP), a slow-wave electroencephalographic (EEG) component, in the inhibitory control of distraction. Behavioral and EEG data were recorded as participants performed auditory selective attention tasks. Simulations that modulated processes of distractor inhibition accounted well for reaction-time (RT) performance, whereas those that modulated target excitation did not. A model that incorporated RP from actual EEG recordings in estimating distractor inhibition was superior in predicting changes in RT as a function of distractor salience across conditions. A …