Using intersection information to map stimulus information transfer within neural networks
نویسندگان
چکیده
منابع مشابه
Maximum Information Transfer in Feedforward Neural Networks
The principle of maximum information preservation has been successfully used to derive learning algorithms for self-organizing neural networks. In this paper, we state and apply the corresponding principle for supervised networks: the principle of minimum information loss. We do not propose a new learning algorithm, but rather a pruning algorithm which works to achieve minimum information loss ...
متن کاملrodbar dam slope stability analysis using neural networks
در این تحقیق شبکه عصبی مصنوعی برای پیش بینی مقادیر ضریب اطمینان و فاکتور ایمنی بحرانی سدهای خاکی ناهمگن ضمن در نظر گرفتن تاثیر نیروی اینرسی زلزله ارائه شده است. ورودی های مدل شامل ارتفاع سد و زاویه شیب بالا دست، ضریب زلزله، ارتفاع آب، پارامترهای مقاومتی هسته و پوسته و خروجی های آن شامل ضریب اطمینان می شود. مهمترین پارامتر مورد نظر در تحلیل پایداری شیب، بدست آوردن فاکتور ایمنی است. در این تحقیق ...
Stimulus Size Dependence of Information Transfer from Retina to Thalamus
Relay cells in the mammalian lateral geniculate nucleus (LGN) are driven primarily by single retinal ganglion cells (RGCs). However, an LGN cell responds typically to less than half of the spikes it receives from the RGC that drives it, and without retinal drive the LGN is silent (Kaplan and Shapley, 1984). Recent studies, which used stimuli restricted to the receptive field (RF) center, show t...
متن کاملModeling Information Technology Competency using Neural Networks
Neural Network (NN) is one of the most important branches of AI that has been applied to an increasing number of real-world problems of considerable complexity from the financial markets to real estate, medicine and education. The most commonly used is multilayer perceptron with back propagation that is capable of representing non-linear functional mapping between inputs and outputs. In this pa...
متن کاملDirect Frequency Information Processing Using Neural Networks
Frequency information processing using complex valued neural networks is proposed Learning process is realized by adjusting delay time and conductance of neural connections Experimental results demonstrate that the network learns successfully an intended output pro le smoothly in frequency domain This result is applicable not only to frequency signal processing but also to future optical neural...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Biosystems
سال: 2019
ISSN: 0303-2647
DOI: 10.1016/j.biosystems.2019.104028