11 papers · ranked by Valyu relevance
Michael Beyeler, Emily L. Rounds, Kristofor D. Carlson, Nikil Dutt + 2 more
'Jeffrey L. Krichmar' 'Aldo A. Faisal'] Supported by recent computational studies, there is increasing evidence that a wide range of neuronal responses can be understood as an emergent property of nonnegative sparse coding (NSC), an efficient population coding scheme based on dimensionality reduction and sparsity…
Yanbo Lian, Anthony N. Burkitt, Boris S. Gutkin
Sparse coding, predictive coding and divisive normalization have each been found to be principles that underlie the function of neural circuits in many parts of the brain, supported by substantial experimental evidence. However, the connections between these related principles are still poorly understood. Sparse coding…
Yuwei Cui, Subutai Ahmad, Jeff Hawkins
Hierarchical temporal memory (HTM) provides a theoretical framework that models several key computational principles of the neocortex. In this paper, we analyze an important component of HTM, the HTM spatial pooler (SP). The SP models how neurons learn feedforward connections and form efficient representations of the…
Eva L Dyer, Ueli Rutishauser, Richard G Baraniuk
Degeneracy is a ubiquitous feature of computation and coding in biological systems. Degenerate codes-codes in which multiple code words have the same meaning or interpretation-arise in a wide range of biological processes, from the many-to-one mapping of codons to amino acids to the numerous instances of degenerate…
Jonathan J. Hunt, Peter Dayan, Geoffrey J. Goodhill, Matthias Bethge
Receptive fields acquired through unsupervised learning of sparse representations of natural scenes have similar properties to primary visual cortex (V1) simple cell receptive fields. However, what drives in vivo development of receptive fields remains controversial. The strongest evidence for the importance of sensory…
Eric McVoy Dodds, Michael Robert DeWeese
Sparse coding models of natural images and sounds have been able to predict several response properties of neurons in the visual and auditory systems. While the success of these models suggests that the structure they capture is universal across domains to some degree, it is not yet clear which aspects of this…
Jianing V. Shi, Jim Wielaard, R. Theodore Smith, Paul Sajda
Sparse coding has been posited as an efficient information processing strategy employed by sensory systems, particularly visual cortex. Substantial theoretical and experimental work has focused on the issue of sparse encoding, namely how the early visual system maps the scene into a sparse representation. In this paper…
Sanjar Adilov
Machine learning models for molecular-property prediction typically work with molecular representations in the form of fingerprints, descriptors, or graphs. In case of fingerprints and descriptors, molecular representations usually comprise thousands of features, which causes the curse of dimensionality for many…
Kelsey Hatzell, Yanjie Zheng
X-ray Computed Tomography (CT) is a non-invasive, non-destructive approach to imaging materials, material systems and engineered components in two- and three- dimensions. Acquisition of 3D images requires the collection of hundreds or thousands of through-thickness X-ray radiographic images from different angles. Such…
Authors not listed
Real-world datasets in chemical engineering and bioengineering processes--such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials--can often be unlabelled or disorganized, rendering the training of existing supervised learning models ineffective at learning the…
Hasan M. Sayeed, Sterling G. Baird, Taylor D. Sparks
Capturing structure-property relationships of materials for property prediction using machine learning requires the representation or featurization of the structural aspects of materials at different levels, including atomic, crystal, and microscales. While crystal structure-based modeling techniques are effective for…