Sparse neural representations of odor and associative learning
نویسندگان
چکیده
منابع مشابه
Sparse Coding Neural Gas: Learning of overcomplete data representations
We consider the problem of learning an unknown (overcomplete) basis from data that are generated from unknown and sparse linear combinations. Introducing the Sparse Coding Neural Gas algorithm, we show how to employ a combination of the original Neural Gas algorithm and Oja’s rule in order to learn a simple sparse code that represents each training sample by only one scaled basis vector. We gen...
متن کاملNeural associative memories and sparse coding
The theoretical, practical and technical development of neural associative memories during the last 40 years is described. The importance of sparse coding of associative memory patterns is pointed out. The use of associative memory networks for large scale brain modeling is also mentioned.
متن کاملcompactifications and representations of transformation semigroups
this thesis deals essentially (but not from all aspects) with the extension of the notion of semigroup compactification and the construction of a general theory of semitopological nonaffine (affine) transformation semigroup compactifications. it determines those compactification which are universal with respect to some algebric or topological properties. as an application of the theory, it is i...
15 صفحه اولLearning Sparse Wavelet Representations
In this work we propose a method for learning wavelet filters directly from data. We accomplish this by framing the discrete wavelet transform as a modified convolutional neural network. We introduce an autoencoder wavelet transform network that is trained using gradient descent. We show that the model is capable of learning structured wavelet filters from synthetic and real data. The learned w...
متن کاملLearning Data Representations with Sparse Coding Neural Gas
We consider the problem of learning an unknown (overcomplete) basis from an unknown sparse linear combination. Introducing the “sparse coding neural gas” algorithm, we show how to employ a combination of the original neural gas algorithm and Oja’s rule in order to learn a simple sparse code that represents each training sample by a multiple of one basis vector. We generalise this algorithm usin...
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: Frontiers in Systems Neuroscience
سال: 2009
ISSN: 1662-5137
DOI: 10.3389/conf.neuro.06.2009.03.093