To sum up, all the hidden layers can be joined together into a single recurrent layer such that the weights and bias are the same for all the hidden layers. So a recurrent neural network will look something like the below: Now it’s time to deal with some of the equations for an RNN...
We used reinforcement learning to train a recurrent neural network model to learn a spatial working memory task. The model is composed of a decision network and a baseline network. The decision network is responsible for updating strategies to make action choices, while the baseline network ...
The experimental results on algorithm learning, language modeling, and question answering demonstrate that the proposed neural memory architecture is promising for practical applications. 展开 关键词: External addressable memory (EAM) long-term memory recurrent neural network (RNN) working memory....
a high rate of misclassification, a high rate of errors, and decreased efficiency. To address these concerns, the purpose of this research is to forecast the stress levels of working professionals using a sophisticated deep learning model called the Deep Recurrent Neural Network (DRNN). The model...
During working memory tasks, the firing rates of single neurons recorded in behaving monkeys remain elevated without external cues. Modeling studies have explored different mechanisms that could underlie this selective persistent activity, including recurrent excitation within cell assemblies, synfire chains ...
this definition can be interpreted as the effort of a brain region needed to steer the activity pattern of itself and its connected brain regions into the desired final activation state, for example by tuning their internal firing or activity patterns by recurrent inhibitory connections. Accordingly,...
Recent advances introduced new ideas regarding possible mechanisms of working memory, such as short-term synaptic facilitation, precise tuning of recurrent excitation and inhibition, and intrinsic network dynamics. These ideas are motivated by computational considerations and careful analysis of experimental ...
For optimal performance of a Recurrent Neural Network Transducer (RNNT), install the Numba package from Conda. Run the following code: conda remove numba pip uninstall numba conda install -c conda-forge numba Install LLMs and MMs Dependencies ...
recurrent-neural-networks spiking-neural-networks working-memory Updated Nov 21, 2021 MATLAB seeholza / seeholzer-deger-2018 Star 11 Code Issues Pull requests Spiking neuronal network simulations (Python, NEST Simulator) for continuous attractor working memory networks with short-term plasticity. ...
To assess whether the linearity of the SVM decoder posed limitations on decoding WM during the delay period, we used a nonlinear long short-term memory (LSTM) recurrent neural network decoder for analogous analyses. The performance of the LSTM decoder was very similar to the SVM and did not ...