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Articles 1 - 5 of 5
Full-Text Articles in Computational Neuroscience
Synchronization Of Coupled Neurons Via Robust Feedback, Hector Puebla, Ricardo Aguilar-Lopez, Priti Kumar Roy
Synchronization Of Coupled Neurons Via Robust Feedback, Hector Puebla, Ricardo Aguilar-Lopez, Priti Kumar Roy
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Wilson-Cowan Coupled Dynamics In A Model Of The Cortico-Striato-Thalamo-Cortical Circuit, Anca R. Radulescu
Wilson-Cowan Coupled Dynamics In A Model Of The Cortico-Striato-Thalamo-Cortical Circuit, Anca R. Radulescu
Biology and Medicine Through Mathematics Conference
No abstract provided.
Virtual V1sion: A Collaborative Coding Project, Cheryl Olman
Virtual V1sion: A Collaborative Coding Project, Cheryl Olman
MODVIS Workshop
Virtual V1sion is a new idea for fostering modeling collaborations and data sharing. While still in its infancy, the ultimate goal is a website that hosts repositories for (1) interchangeable model elements, (2) datasets that can be fit/predicted by those models, and (3) educational modules that explain the background for both the models and the datasets. The scope of the modeling is limited to predictions of V1 responses, although not all computations represented by model elements in Virtual V1sion are required to be V1-intrinsic: a goal of the project is to provide a framework in which predictions for modulation by …
Failure Of Surface Color Cues Under Natural Changes In Lighting, David H. Foster, Iván Marín-Franch
Failure Of Surface Color Cues Under Natural Changes In Lighting, David H. Foster, Iván Marín-Franch
MODVIS Workshop
Color allows us to effortlessly discriminate and identify surfaces and objects by their reflected light. Although the reflected spectrum changes with the illumination spectrum, cone photoreceptor signals can be transformed to give useful cues for surface color. But what happens when both the spectrum and the geometry of the illumination change, as with lighting from the sun and sky? Is it possible, as a matter of principle, to obtain reliable cues by processing cone signals alone? This question was addressed here by estimating the information provided by cone signals from time-lapse hyperspectral radiance images of five outdoor scenes under natural …
A Geometric Approach To Sparse Coding Yields Insight Into Nonlinear Responses, Kedarnath Vilankar, James Golden, David Field
A Geometric Approach To Sparse Coding Yields Insight Into Nonlinear Responses, Kedarnath Vilankar, James Golden, David Field
MODVIS Workshop
In artificial and biological networks, it is a common accepted practice to describe a neurons (biological or artificial) response properties by a two-dimensional feature map (receptive field). However, real neurons have nonlinear response properties which are not represented by their receptive fields. The efficient coding mechanisms such as sparse coding network or ICA, learn the response properties of V1 neurons from natural images using neural networks. These networks learn the receptive fields which are similar to the receptive fields of V1 neurons. These networks also produces some of the nonlinearities (such as end-stopping and non-classical surround effect), which are exhibited …