Competitive learning to generate sparse representations for associative memory

• URL: http://arxiv.org/abs/2301.02196v1
• Date: Thu, 5 Jan 2023 17:57:52 GMT
• Title: Competitive learning to generate sparse representations for associative memory
• Authors: Luis Sacouto and Andreas Wichert
• Abstract summary: We propose a biologically plausible network that encodes images into codes that are suitable for associative memory. It is organized into groups of neurons that specialize on local receptive fields, and learn through a competitive scheme.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One of the most well established brain principles, hebbian learning, has led to the theoretical concept of neural assemblies. Based on it, many interesting brain theories have spawned. Palm's work implements this concept through binary associative memory, in a model that not only has a wide cognitive explanatory power but also makes neuroscientific predictions. Yet, associative memory can only work with logarithmic sparse representations, which makes it extremely difficult to apply the model to real data. We propose a biologically plausible network that encodes images into codes that are suitable for associative memory. It is organized into groups of neurons that specialize on local receptive fields, and learn through a competitive scheme. After conducting auto- and hetero-association experiments on two visual data sets, we can conclude that our network not only beats sparse coding baselines, but also that it comes close to the performance achieved using optimal random codes.

Related papers

• Don't Cut Corners: Exact Conditions for Modularity in Biologically Inspired Representations [52.48094670415497]
  We develop a theory of when biologically inspired representations modularise with respect to source variables (sources) We derive necessary and sufficient conditions on a sample of sources that determine whether the neurons in an optimal biologically-inspired linear autoencoder modularise. Our theory applies to any dataset, extending far beyond the case of statistical independence studied in previous work.
  arXiv  Detail & Related papers  (2024-10-08T17:41:37Z)
• Hierarchical Working Memory and a New Magic Number [1.024113475677323]
  We propose a recurrent neural network model for chunking within the framework of the synaptic theory of working memory. Our work provides a novel conceptual and analytical framework for understanding the on-the-fly organization of information in the brain that is crucial for cognition.
  arXiv  Detail & Related papers  (2024-08-14T16:03:47Z)
• Unsupervised representation learning with Hebbian synaptic and structural plasticity in brain-like feedforward neural networks [0.0]
  We introduce and evaluate a brain-like neural network model capable of unsupervised representation learning. The model was tested on a diverse set of popular machine learning benchmarks.
  arXiv  Detail & Related papers  (2024-06-07T08:32:30Z)
• Language Reconstruction with Brain Predictive Coding from fMRI Data [28.217967547268216]
  Theory of predictive coding suggests that human brain naturally engages in continuously predicting future word representations. textscPredFT achieves current state-of-the-art decoding performance with a maximum BLEU-1 score of $27.8%$.
  arXiv  Detail & Related papers  (2024-05-19T16:06:02Z)
• MindBridge: A Cross-Subject Brain Decoding Framework [60.58552697067837]
  Brain decoding aims to reconstruct stimuli from acquired brain signals. Currently, brain decoding is confined to a per-subject-per-model paradigm. We present MindBridge, that achieves cross-subject brain decoding by employing only one model.
  arXiv  Detail & Related papers  (2024-04-11T15:46:42Z)
• Implementing engrams from a machine learning perspective: matching for prediction [0.0]
  We propose how we might design a computer system to implement engrams using neural networks. Building on autoencoders, we propose latent neural spaces as indexes for storing and retrieving information in a compressed format. We consider how different states in latent neural spaces corresponding to different types of sensory input could be linked by synchronous activation.
  arXiv  Detail & Related papers  (2023-03-01T10:05:40Z)
• Measures of Information Reflect Memorization Patterns [53.71420125627608]
  We show that the diversity in the activation patterns of different neurons is reflective of model generalization and memorization. Importantly, we discover that information organization points to the two forms of memorization, even for neural activations computed on unlabelled in-distribution examples.
  arXiv  Detail & Related papers  (2022-10-17T20:15:24Z)
• Associative Memories via Predictive Coding [37.59398215921529]
  Associative memories in the brain receive and store patterns of activity registered by the sensory neurons. We present a novel neural model for realizing associative memories based on a hierarchical generative network that receives external stimuli via sensory neurons.
  arXiv  Detail & Related papers  (2021-09-16T15:46:26Z)
• Reservoir Memory Machines as Neural Computers [70.5993855765376]
  Differentiable neural computers extend artificial neural networks with an explicit memory without interference. We achieve some of the computational capabilities of differentiable neural computers with a model that can be trained very efficiently.
  arXiv  Detail & Related papers  (2020-09-14T12:01:30Z)
• Self-Attentive Associative Memory [69.40038844695917]
  We propose to separate the storage of individual experiences (item memory) and their occurring relationships (relational memory) We achieve competitive results with our proposed two-memory model in a diversity of machine learning tasks.
  arXiv  Detail & Related papers  (2020-02-10T03:27:48Z)
• Encoding-based Memory Modules for Recurrent Neural Networks [79.42778415729475]
  We study the memorization subtask from the point of view of the design and training of recurrent neural networks. We propose a new model, the Linear Memory Network, which features an encoding-based memorization component built with a linear autoencoder for sequences.
  arXiv  Detail & Related papers  (2020-01-31T11:14:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.