کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6263654 1613903 2013 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Research ReportNatural image sequences constrain dynamic receptive fields and imply a sparse code
ترجمه فارسی عنوان
گزارش تحقیقاتی توالی های تصویری طبیعی محدوده زمینه های پذیرش پویا و یک کد ناقص را به دنبال دارد
موضوعات مرتبط
علوم زیستی و بیوفناوری علم عصب شناسی علوم اعصاب (عمومی)
چکیده انگلیسی


- We extend the Restricted Boltzmann Machine with a novel Temporal autoencoding method.
- We use this to predict dynamic receptive fields from natural movies.
- They account for both, spatial and temporal sparse activation in biological neurons.
- A spike response model demonstrates how this translates to a neural code.
- Our method outperforms existing models of this class on a benchmark dataset.This article is part of a Special Issue entitled Neural Coding 2012.This article is part of a Special Issue entitled Neural Coding 2012.This article is part of a Special Issue entitled Neural Coding 2012.This article is part of a Special Issue entitled Neural Coding 2012.This article is part of a Special Issue entitled Neural Coding 2012.This article is part of a Special Issue entitled Neural Coding 2012.

In their natural environment, animals experience a complex and dynamic visual scenery. Under such natural stimulus conditions, neurons in the visual cortex employ a spatially and temporally sparse code. For the input scenario of natural still images, previous work demonstrated that unsupervised feature learning combined with the constraint of sparse coding can predict physiologically measured receptive fields of simple cells in the primary visual cortex. This convincingly indicated that the mammalian visual system is adapted to the natural spatial input statistics. Here, we extend this approach to the time domain in order to predict dynamic receptive fields that can account for both spatial and temporal sparse activation in biological neurons. We rely on temporal restricted Boltzmann machines and suggest a novel temporal autoencoding training procedure. When tested on a dynamic multi-variate benchmark dataset this method outperformed existing models of this class. Learning features on a large dataset of natural movies allowed us to model spatio-temporal receptive fields for single neurons. They resemble temporally smooth transformations of previously obtained static receptive fields and are thus consistent with existing theories. A neuronal spike response model demonstrates how the dynamic receptive field facilitates temporal and population sparseness. We discuss the potential mechanisms and benefits of a spatially and temporally sparse representation of natural visual input.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Brain Research - Volume 1536, 6 November 2013, Pages 53-67
نویسندگان
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