163,628 research outputs found
Attractor neural networks storing multiple space representations: a model for hippocampal place fields
A recurrent neural network model storing multiple spatial maps, or
``charts'', is analyzed. A network of this type has been suggested as a model
for the origin of place cells in the hippocampus of rodents. The extremely
diluted and fully connected limits are studied, and the storage capacity and
the information capacity are found. The important parameters determining the
performance of the network are the sparsity of the spatial representations and
the degree of connectivity, as found already for the storage of individual
memory patterns in the general theory of auto-associative networks. Such
results suggest a quantitative parallel between theories of hippocampal
function in different animal species, such as primates (episodic memory) and
rodents (memory for space).Comment: 19 RevTeX pages, 8 pes figure
Long Short-Term Memory Spatial Transformer Network
Spatial transformer network has been used in a layered form in conjunction
with a convolutional network to enable the model to transform data spatially.
In this paper, we propose a combined spatial transformer network (STN) and a
Long Short-Term Memory network (LSTM) to classify digits in sequences formed by
MINST elements. This LSTM-STN model has a top-down attention mechanism profit
from LSTM layer, so that the STN layer can perform short-term independent
elements for the statement in the process of spatial transformation, thus
avoiding the distortion that may be caused when the entire sequence is
spatially transformed. It also avoids the influence of this distortion on the
subsequent classification process using convolutional neural networks and
achieves a single digit error of 1.6\% compared with 2.2\% of Convolutional
Neural Network with STN layer
Dynamical model of sequential spatial memory: winnerless competition of patterns
We introduce a new biologically-motivated model of sequential spatial memory
which is based on the principle of winnerless competition (WLC). We implement
this mechanism in a two-layer neural network structure and present the learning
dynamics which leads to the formation of a WLC network. After learning, the
system is capable of associative retrieval of pre-recorded sequences of spatial
patterns.Comment: 4 pages, submitted to PR
A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Prediction
In spite of its importance, passenger demand prediction is a highly
challenging problem, because the demand is simultaneously influenced by the
complex interactions among many spatial and temporal factors and other external
factors such as weather. To address this problem, we propose a Spatio-TEmporal
Fuzzy neural Network (STEF-Net) to accurately predict passenger demands
incorporating the complex interactions of all known important factors. We
design an end-to-end learning framework with different neural networks modeling
different factors. Specifically, we propose to capture spatio-temporal feature
interactions via a convolutional long short-term memory network and model
external factors via a fuzzy neural network that handles data uncertainty
significantly better than deterministic methods. To keep the temporal relations
when fusing two networks and emphasize discriminative spatio-temporal feature
interactions, we employ a novel feature fusion method with a convolution
operation and an attention layer. As far as we know, our work is the first to
fuse a deep recurrent neural network and a fuzzy neural network to model
complex spatial-temporal feature interactions with additional uncertain input
features for predictive learning. Experiments on a large-scale real-world
dataset show that our model achieves more than 10% improvement over the
state-of-the-art approaches.Comment: https://epubs.siam.org/doi/abs/10.1137/1.9781611975673.1
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