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Anti-transfer learning for task invariance in convolutional neural networks for speech processing.

Neural networks : the official journal of the International Neural Network Society
We introduce the novel concept of anti-transfer learning for speech processing with convolutional neural networks. While transfer learning assumes that the learning process for a target task will benefit from re-using representations learned for anot...

Low-dimensional learned feature spaces quantify individual and group differences in vocal repertoires.

eLife
Increases in the scale and complexity of behavioral data pose an increasing challenge for data analysis. A common strategy involves replacing entire behaviors with small numbers of handpicked, domain-specific features, but this approach suffers from ...

Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits.

Nature neuroscience
Synaptic plasticity is believed to be a key physiological mechanism for learning. It is well established that it depends on pre- and postsynaptic activity. However, models that rely solely on pre- and postsynaptic activity for synaptic changes have, ...

A theory of capacity and sparse neural encoding.

Neural networks : the official journal of the International Neural Network Society
Motivated by biological considerations, we study sparse neural maps from an input layer to a target layer with sparse activity, and specifically the problem of storing K input-target associations (x,y), or memories, when the target vectors y are spar...

Efficient learning with augmented spikes: A case study with image classification.

Neural networks : the official journal of the International Neural Network Society
Efficient learning of spikes plays a valuable role in training spiking neural networks (SNNs) to have desired responses to input stimuli. However, current learning rules are limited to a binary form of spikes. The seemingly ubiquitous phenomenon of b...

Graph embedding clustering: Graph attention auto-encoder with cluster-specificity distribution.

Neural networks : the official journal of the International Neural Network Society
Towards exploring the topological structure of data, numerous graph embedding clustering methods have been developed in recent years, none of them takes into account the cluster-specificity distribution of the nodes representations, resulting in subo...

Against spatial-temporal discrepancy: contrastive learning-based network for surgical workflow recognition.

International journal of computer assisted radiology and surgery
PURPOSE: Automatic workflow recognition from surgical videos is fundamental and significant for developing context-aware systems in modern operating rooms. Although many approaches have been proposed to tackle challenges in this complex task, there a...

Classic Hebbian learning endows feed-forward networks with sufficient adaptability in challenging reinforcement learning tasks.

Journal of neurophysiology
A common pitfall of current reinforcement learning agents implemented in computational models is in their inadaptability postoptimization. Najarro and Risi [Najarro E, Risi S. . 2020: 20719-20731, 2020] demonstrate how such adaptability may be salvag...

Clone-structured graph representations enable flexible learning and vicarious evaluation of cognitive maps.

Nature communications
Cognitive maps are mental representations of spatial and conceptual relationships in an environment, and are critical for flexible behavior. To form these abstract maps, the hippocampus has to learn to separate or merge aliased observations appropria...

Augmented semantic feature based generative network for generalized zero-shot learning.

Neural networks : the official journal of the International Neural Network Society
Zero-shot learning (ZSL) aims to recognize objects in images when no training data is available for the object classes. Under generalized zero-shot learning (GZSL) setting, the test objects belong to seen or unseen categories. In many recent studies,...