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Found 7 papers, 1 papers with code
Synaptic Modulation using Interspike Intervals Increases Energy Efficiency of Spiking Neural Networks
A higher ISI implies fewer spikes and vice-versa.
Towards Improved Imbalance Robustness in Continual Multi-Label Learning with Dual Output Spiking Architecture (DOSA)
A novel imbalance-aware loss function is also proposed, improving the multi-label classification performance of the model by making it more robust to data imbalance.
Deep Predictive Coding with Bi-directional Propagation for Classification and Reconstruction
Each layer in the networks trained using DBPC learn to predict the activities of neurons in the previous and next layer which allows the network to simultaneously perform classification and reconstruction tasks using feedforward and feedback propagation, respectively.
A Decentralized Spike-based Learning Framework for Sequential Capture in Discrete Perimeter Defense Problem
The output of MLC-SEFRON contains the labels of segments that a defender has to visit in order to protect the perimeter.
Sharing Lifelong Reinforcement Learning Knowledge via Modulating Masks
The key idea is that the isolation of specific task knowledge to specific masks allows agents to transfer only specific knowledge on-demand, resulting in robust and effective distributed lifelong learning.
MuPNet: Multi-modal Predictive Coding Network for Place Recognition by Unsupervised Learning of Joint Visuo-Tactile Latent Representations
Extracting and binding salient information from different sensory modalities to determine common features in the environment is a significant challenge in robotics.
A Deep Predictive Coding Network for Learning Latent Representations
In this paper, we describe an algorithm to build a deep generative model using predictive coding that can be used to infer latent representations about the stimuli received from external environment.
