The gating mechanism is commonly used to determine whether information should pass through a gate, thereby controlling the flow of information. Based on the gating concept, the Gated Recurrent Unit (GRU) has shown promising results in LSTM [47]. Inspired by the MogaNet [48] gated network, we...
3 Spatial Transformers 3.1 Localisation Network The localisation network takes the input feature map U ∈ R H×W×C with width W, height H and C channels and outputs θ, the parameters of the transformation Tθ to be applied to the feature map: θ = floc(U). The size of θ can vary...
SpaGCN adopts a graph convolutional network to integrate the transcript expression level data with their spatial location information to identify spatial domains31. STAGATE uses an adaptive graph attention auto-encoder model to learn the similarities between the neighboring spots to identify spatial ...
3.1 Model Structure Our network employs DINOv2 as a robust visual feature extraction module, utilizing a Hybrid Receptive Field Mod- ule (HRF) to capture information across various receptive fields. This diverse information is fed into a Spatial Gating Aggregation Module (SGA), where spatial ...
The network was built by selecting the connectivity edges (regions) that predicted the participants’ sustained attention performance. During neurofeedback, real-time functional connectivity in the whole brain was measured in 3 min blocks. Connectivity was evaluated within the predefined network to ...
This is consistent with the notion of the hippocampus as a generative recurrent neural network, that starts at a current state and runs forward, specifically toward the desired state50. The striatum is understood as part of an action gate that permits certain actions in specific contexts, ...
Compared with the original LSTM, the GRU network is not only simple and efficient, and easy to implement, but also has fewer parameters and faster training, which can effectively limit the risk of overfitting. The update gate zt and reset gate rt are two essential component unit in a GRU ...
gate probability mass; submitting the adaptive context vector and the current hidden state of the language decoder to a feed-forward neural network and causing the feed-forward neural network to emit a next caption word; and repeating the processing of words through the language decoder, the ...
Fig. 1: Overview of STAGATE. STAGATE first constructs a spatial neighbor network (SNN) based on a pre-defined radius, and another optional one in the dashed box for 10x Visium data by pruning it according to the pre-clustering of gene expressions to better characterize the spatial similarity...
Network tuning We employed brute-force search algorithms to find suitable values for the lateral inhibitory and excitatory weight parameters, the feedforward projection strengths from input to output layer, and for intrinsic properties of the AdEx equations of the SC neurons. Besides \({\overline{W...