کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4946574 1439413 2017 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Extending the Stabilized Supralinear Network model for binocular image processing
ترجمه فارسی عنوان
گسترش شبکۀ مدل ثابتی شبکهای برای پردازش تصویر دو رنگی
کلمات کلیدی
شبکه فوقانی تثبیت شده تحرک متعادل / مهار، انتقال غلطکی از سرکوب، قشر بینایی اولیه، شبکه های عصبی انعقادی،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
The visual cortex is both extensive and intricate. Computational models are needed to clarify the relationships between its local mechanisms and high-level functions. The Stabilized Supralinear Network (SSN) model was recently shown to account for many receptive field phenomena in V1, and also to predict subtle receptive field properties that were subsequently confirmed in vivo. In this study, we performed a preliminary exploration of whether the SSN is suitable for incorporation into large, functional models of the visual cortex, considering both its extensibility and computational tractability. First, whereas the SSN receives abstract orientation signals as input, we extended it to receive images (through a linear-nonlinear stage), and found that the extended version behaved similarly. Secondly, whereas the SSN had previously been studied in a monocular context, we found that it could also reproduce data on interocular transfer of surround suppression. Finally, we reformulated the SSN as a convolutional neural network, and found that it scaled well on parallel hardware. These results provide additional support for the plausibility of the SSN as a model of lateral interactions in V1, and suggest that the SSN is well suited as a component of complex vision models. Future work will use the SSN to explore relationships between local network interactions and sophisticated vision processes in large networks.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 90, June 2017, Pages 29-41
نویسندگان
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