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Found 7 papers, 3 papers with code
Deep Phasor Networks: Connecting Conventional and Spiking Neural Networks
In this work, we extend standard neural networks by building upon an assumption that neuronal activations correspond to the angle of a complex number lying on the unit circle, or 'phasor.'
Bridge Networks: Relating Inputs through Vector-Symbolic Manipulations
These include high energy consumption, catastrophic forgetting, dependance on global losses, and an inability to reason symbolically.
Replay in Deep Learning: Current Approaches and Missing Biological Elements
Replay is the reactivation of one or more neural patterns, which are similar to the activation patterns experienced during past waking experiences.
A Dual-Memory Architecture for Reinforcement Learning on Neuromorphic Platforms
Reinforcement learning (RL) is a foundation of learning in biological systems and provides a framework to address numerous challenges with real-world artificial intelligence applications.
Biologically inspired sleep algorithm for increased generalization and adversarial robustness in deep neural networks
In this work, we utilize a biologically inspired sleep phase in ANNs and demonstrate the benefit of sleep on defending against adversarial attacks as well as in increasing ANN classification robustness.
Biologically inspired sleep algorithm for artificial neural networks
First, in an incremental learning framework, sleep is able to recover older tasks that were otherwise forgotten in the ANN without sleep phase due to catastrophic forgetting.
Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings
In all of our simulated data, the differential covariance-based methods achieved better or similar performance to the GLM method and required fewer data samples.
