Recent studies in neuroscience have begun to independently address the two components of information processing: the representation of stimuli in neural activity and the transmission of information in networks that model neural interactions. Yet only recently are studies seeking to link these two types ...
This paper presents a biologically inspired neural network model which uses a population cell coding mechanism for visual context representation and target searching. Experimental results show that the population-cell-coding generally performs better than the single-cell-coding system. 展开 关键词: neural...
H. Kirchhoffer, et al."Overview of the Neural Network Compression and Representation (NNR) Standard", IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-14, July 2021, doi: 10.1109/TCSVT.2021.3095970, Open Access P. Haase et al.,"Encoder Optimizations For The NNR Standar...
(10 vs. 20), input representation (pixel information only versus pixel information plus direction of motion), and regularization strategy (L1 versus L2 loss term). Consistent with the goal of the M-Pong task, we trained all networks using a standard performance-optimizing cost function to find...
which provide the conceptual framework for information representation appropriate to machine-based communication. Neural-network systems (biological or artificial) do not store information or process it in the way that conventional digital computers do. Specifically, the basic unit of neural-network operati...
The model consists of three parts: feature representation, output coding, and network architecture. B. Network Input The network input comprises the real and imaginary parts of the time-frequency domain signal. In contrast to high- level features extraction, such a representation retains all the in...
How are discrete views of a panoramic environment knit together into a broad, unified memory representation? Regions of the brain’s “scene network” are well poised to integrate retinal input and memory [1]: they are visually driven [2, 3] but also densely interconnected with memory ...
The mathematical representation of the feedforward network with the tansig activation function is given by the following system: (2.28)nk,t=ωk,o+∑i=1i*ωk,ixi,t (2.29)Nk,t=T(nk,t) (2.30)=enk,t−e−nk,tenk,t+e−nk,t (2.31)yt=γ0+∑k=1k*γkNk,t where T(nk,t) is ...
Neural Network Representation Compute the output of the neural network for a single training example: Forward Propagation For A Single Training Example Compute the output of the neural network for m training examples (vectorization): Forward Propagation For m Training Exmaples ...
Recently, a deep neural network representation of density functional theory (DFT) Hamiltonian (named DeepH) was developed by employing the locality of electronic matter, localized basis, and local coordinate transformation25. By the DeepH approach, the computationally demanding self-consistent field iter...