Universal hopfield networks: A general framework for single-shot associative memory models
A large number of neural network models of associative memory have been proposed in the
literature. These include the classical Hopfield networks (HNs), sparse distributed memories …
literature. These include the classical Hopfield networks (HNs), sparse distributed memories …
[PDF][PDF] Brain-inspired computational intelligence via predictive coding
Artificial intelligence (AI) is rapidly becoming one of the key technologies of this century. The
majority of results in AI thus far have been achieved using deep neural networks trained with …
majority of results in AI thus far have been achieved using deep neural networks trained with …
Predictive coding: Towards a future of deep learning beyond backpropagation?
The backpropagation of error algorithm used to train deep neural networks has been
fundamental to the successes of deep learning. However, it requires sequential backward …
fundamental to the successes of deep learning. However, it requires sequential backward …
Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning
F Mumuni, A Mumuni - Cognitive Systems Research, 2024 - Elsevier
We review current and emerging knowledge-informed and brain-inspired cognitive systems
for realizing adversarial defenses, eXplainable Artificial Intelligence (XAI), and zero-shot or …
for realizing adversarial defenses, eXplainable Artificial Intelligence (XAI), and zero-shot or …
Recurrent predictive coding models for associative memory employing covariance learning
The computational principles adopted by the hippocampus in associative memory (AM)
tasks have been one of the most studied topics in computational and theoretical …
tasks have been one of the most studied topics in computational and theoretical …
Learning on arbitrary graph topologies via predictive coding
Training with backpropagation (BP) in standard deep learning consists of two main steps: a
forward pass that maps a data point to its prediction, and a backward pass that propagates …
forward pass that maps a data point to its prediction, and a backward pass that propagates …
Learning probability distributions of sensory inputs with Monte Carlo predictive coding
It has been suggested that the brain employs probabilistic generative models to optimally
interpret sensory information. This hypothesis has been formalised in distinct frameworks …
interpret sensory information. This hypothesis has been formalised in distinct frameworks …
A review of neuroscience-inspired machine learning
One major criticism of deep learning centers around the biological implausibility of the credit
assignment schema used for learning--backpropagation of errors. This implausibility …
assignment schema used for learning--backpropagation of errors. This implausibility …
The predictive forward-forward algorithm
We propose the predictive forward-forward (PFF) algorithm for conducting credit assignment
in neural systems. Specifically, we design a novel, dynamic recurrent neural system that …
in neural systems. Specifically, we design a novel, dynamic recurrent neural system that …
Sequential memory with temporal predictive coding
Forming accurate memory of sequential stimuli is a fundamental function of biological
agents. However, the computational mechanism underlying sequential memory in the brain …
agents. However, the computational mechanism underlying sequential memory in the brain …