Search Results for author:
Found 6 papers, 2 papers with code
Cortex Inspired Learning to Recover Damaged Signal Modality with ReD-SOM Model
One of such modern tasks is recovering lost data of one modality by using the data from another one.
A unified software/hardware scalable architecture for brain-inspired computing based on self-organizing neural models
This work also demonstrates the distributed and scalable nature of the model through both simulation results and hardware execution on a dedicated FPGA-based platform named SCALP (Self-configurable 3D Cellular Adaptive Platform).
GPU-based Self-Organizing Maps for Post-Labeled Few-Shot Unsupervised Learning
In this work, we consider the problem of post-labeled few-shot unsupervised learning, a classification task where representations are learned in an unsupervised fashion, to be later labeled using very few annotated examples.
Improving Self-Organizing Maps with Unsupervised Feature Extraction
We conduct a comparative study on the SOM classification accuracy with unsupervised feature extraction using two different approaches: a machine learning approach with Sparse Convolutional Auto-Encoders using gradient-based learning, and a neuroscience approach with Spiking Neural Networks using Spike Timing Dependant Plasticity learning.
Ranked #5 on
Unsupervised MNIST
on MNIST
Brain-inspired self-organization with cellular neuromorphic computing for multimodal unsupervised learning
The divergence mechanism is used to label one modality based on the other, while the convergence mechanism is used to improve the overall accuracy of the system.
Neuromorphic hardware as a self-organizing computing system
This paper presents the self-organized neuromorphic architecture named SOMA.
