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Found 13 papers, 4 papers with code
Associative Memories in the Feature Space
This problem can be easily solved by computing \emph{similarities} in an embedding space instead of the pixel space.
Sequential Memory with Temporal Predictive Coding
Forming accurate memory of sequential stimuli is a fundamental function of biological agents.
A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks
Predictive coding networks are neuroscience-inspired models with roots in both Bayesian statistics and neuroscience.
A Theoretical Framework for Inference and Learning in Predictive Coding Networks
In this paper, we provide a comprehensive theoretical analysis of the properties of PCNs trained with prospective configuration.
Backpropagation at the Infinitesimal Inference Limit of Energy-Based Models: Unifying Predictive Coding, Equilibrium Propagation, and Contrastive Hebbian Learning
How the brain performs credit assignment is a fundamental unsolved problem in neuroscience.
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.
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.
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 the error of this prediction back through the network.
Associative Memories via Predictive Coding
We conclude by discussing the possible impact of this work in the neuroscience community, by showing that our model provides a plausible framework to study learning and retrieval of memories in the brain, as it closely mimics the behavior of the hippocampus as a memory index and generative model.
Embedding digital chronotherapy into medical devices -- A canine validation for controlling status epilepticus through multi-scale rhythmic brain stimulation
Such is the case of circadian and infradian seizure patterns observed in epilepsy.
Reverse Differentiation via Predictive Coding
Recent works prove that these methods can approximate BP up to a certain margin on multilayer perceptrons (MLPs), and asymptotically on any other complex model, and that zero-divergence inference learning (Z-IL), a variant of PC, is able to exactly implement BP on MLPs.
Predictive Coding Can Do Exact Backpropagation on Convolutional and Recurrent Neural Networks
Predictive coding networks (PCNs) are an influential model for information processing in the brain.
Can the Brain Do Backpropagation? --- Exact Implementation of Backpropagation in Predictive Coding Networks
However, there are several gaps between BP and learning in biologically plausible neuronal networks of the brain (learning in the brain, or simply BL, for short), in particular, (1) it has been unclear to date, if BP can be implemented exactly via BL, (2) there is a lack of local plasticity in BP, i. e., weight updates require information that is not locally available, while BL utilizes only locally available information, and (3)~there is a lack of autonomy in BP, i. e., some external control over the neural network is required (e. g., switching between prediction and learning stages requires changes to dynamics and synaptic plasticity rules), while BL works fully autonomously.
