17 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…
Jacquelyn A. Shelton, Abdul-Saboor Sheikh, Jörg Bornschein, Philip Sterne + 2 more
'Philip Sterne' 'Jörg Lücke' 'Marco Cristani'] Sparse coding is a popular approach to model natural images but has faced two main challenges: modelling low-level image components (such as edge-like structures and their occlusions) and modelling varying pixel intensities. Traditionally, images are modelled as a sparse…
Mengchen Zhu, Christopher J. Rozell, Matthias Bethge
Extensive electrophysiology studies have shown that many V1 simple cells have nonlinear response properties to stimuli within their classical receptive field (CRF) and receive contextual influence from stimuli outside the CRF modulating the cell's response. Models seeking to explain these non-classical receptive field…
Eric Barnhill
If sufficient information about the signal can be deduced from a small portion of a signal via a mathematical transform, then the benefits to the actor are obvious. Both computationally and metabolically, the organism that can reduce processing demands by such a large amount can expect to reap benefits. If…
Nicole L. Carlson, Vivienne L. Ming, Michael Robert DeWeese, Tim Behrens
We have developed a sparse mathematical representation of speech that minimizes the number of active model neurons needed to represent typical speech sounds. The model learns several well-known acoustic features of speech such as harmonic stacks, formants, onsets and terminations, but we also find more exotic…
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…
Taro Tezuka
In biological neural networks, it is widely accepted that the spikes are the fundamental building blocks of information representation [1]. In contrast, whether such building blocks exist at a higher level in terms of time and in a population of neurons is a topic of ongoing debate. One approach for finding candidates…
Joel Zylberberg, Michael Robert DeWeese, Olaf Sporns
The sparse coding hypothesis has enjoyed much success in predicting response properties of simple cells in primary visual cortex (V1) based solely on the statistics of natural scenes. In typical sparse coding models, model neuron activities and receptive fields are optimized to accurately represent input stimuli using…
András Lörincz, Zsolt Palotai, Gábor Szirtes, Lyle J. Graham
Sensory representations are not only sparse, but often overcomplete: coding units significantly outnumber the input units. For models of neural coding this overcompleteness poses a computational challenge for shaping the signal processing channels as well as for using the large and sparse representations in an…
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…
Oded Barzelay, Miriam Furst, Omri Barak, Matthias Bethge
Our acoustical environment abounds with repetitive sounds, some of which are related to pitch perception. It is still unknown how the auditory system, in processing these sounds, relates a physical stimulus and its percept. Since, in mammals, all auditory stimuli are conveyed into the nervous system through the…
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…
Wiktor Młynarski, Gašper Tkačik, Adam Kohn
Activity of sensory neurons is driven not only by external stimuli but also by feedback signals from higher brain areas. Attention is one particularly important internal signal whose presumed role is to modulate sensory representations such that they only encode information currently relevant to the organism at minimal…
Hanchen Xiong, Antonio J. Rodríguez-Sánchez, Sandor Szedmak, Justus Piater
'Justus Piater'] This paper investigates how utilizing diversity priors can discover early visual features that resemble their biological counterparts. The study is mainly motivated by the sparsity and selectivity of activations of visual neurons in area V1. Most previous work on computational modeling emphasizes…