In contrast with V1 recordings, decoding of the irrelevant modality gradually declined in ACC after an initial transient. Our analytical proof and a recurrent neural network model of the task revealed mutually inhibiting connections that produced context-gated suppression as observed in mice. Using ...
based on a position of the object in the environment, updating a graph neural network (GNN) to encode the first feature and second feature into a graph node representing the object and encode relative positions of additional objects in the environment into one or more edges attached to the ...
Although it is beyond the scope of this paper, the virtual neu- ron is a vital component for composing subnetworks to scale-up neuromorphic algorithms, and it is also a vital component for network encoding and decoding capabilities. We would like to address these areas in future work. The ...
Although it is beyond the scope of this paper, the virtual neu- ron is a vital component for composing subnetworks to scale-up neuromorphic algorithms, and it is also a vital component for network encoding and decoding capabilities. We would like to address these areas in future work. The ...
Advances in network neuroscience may benefit from developing new frameworks for mapping brain connectomes. We present a framework to encode structural brain connectomes and diffusion-weighted magnetic resonance (dMRI) data using multidimensional arrays. The framework integrates the relation between connectome...
Attention mechanism is first proposed to pay attention to different parts of the source sentence at every decoding step in neural machine translation [16]. In seq2seq learning, we use a guider to distinguish the importance of tokens. Suppose we have a sentence, which has n tokens, represented...
decoding system may be able to losslessly regenerate the content of the input document in its entirety from the output document. A networked computing system may be able to process encoded documents more efficiently and more rapidly than non-encoded documents. Further, encoded documents may use ...
To solve the aforementioned problems, in this study, a deep learning model of sleep EEG signal was developed using bidirectional recurrent neural network (BiRNN) encoding and decoding. First, the input signal was denoised using the wavelet threshold method. Next, feature extraction in the time ...
Due to the inherent auto-regressive nature of texts, an auto-regressive decoder such as a recurrent neural network (RNN) and a long short-term memory (LSTM) [4] are also widely used, and a few integrated models with RNN and VAE have also been proposed [2]. Previous VAE-driven language...
Attention-based networks emphasize the most relevant parts of the source sequence during each decoding time step. In doing so, the encoder sequence is treated as a soft-addressable memory whose positions are weighted based on the state of the decoder RNN. Bidirectional RNNs learn past and future...