Search Results for author: Odelia Schwartz

Found 6 papers, 2 papers with code

Inference via Sparse Coding in a Hierarchical Vision Model

1 code implementation3 Aug 2021 Joshua Bowren, Luis Sanchez-Giraldo, Odelia Schwartz

Sparse coding has been incorporated in models of the visual cortex for its computational advantages and connection to biology.

Image Classification Texture Classification

Correspondence of Deep Neural Networks and the Brain for Visual Textures

1 code implementation7 Jun 2018 Md Nasir Uddin Laskar, Luis G. Sanchez Giraldo, Odelia Schwartz

Deep convolutional neural networks (CNNs) trained on objects and scenes have shown intriguing ability to predict some response properties of visual cortical neurons.

Integrating Flexible Normalization into Mid-Level Representations of Deep Convolutional Neural Networks

no code implementations5 Jun 2018 Luis Gonzalo Sanchez Giraldo, Odelia Schwartz

Deep convolutional neural networks (CNNs) are becoming increasingly popular models to predict neural responses in visual cortex.

Decoding of finger trajectory from ECoG using deep learning

no code implementations Journal of Neural Engineering 2018 Ziqian Xie, Odelia Schwartz, Abhishek Prasad

We predicted the finger trajectory using ECoG signals and compared results for the least angle regression (LARS), CNN-LSTM, random forest, LSTM model (LSTM_HC, for using hard-coded features) and a decoding pipeline consisting of band-pass filtering, energy extraction, feature selection and linear regression.

Brain Decoding feature selection +1

Statistical Models of Linear and Nonlinear Contextual Interactions in Early Visual Processing

no code implementations NeurIPS 2009 Ruben Coen-Cagli, Peter Dayan, Odelia Schwartz

A central hypothesis about early visual processing is that it represents inputs in a coordinate system matched to the statistics of natural scenes.

Characterizing Neural Gain Control using Spike-triggered Covariance

no code implementations NeurIPS 2001 Odelia Schwartz, E.J. Chichilnisky, Eero P. Simoncelli

Spike-triggered averaging techniques are effective for linear characterization of neural responses.

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