Modular Boundaries in Recurrent Neural Networks

• URL: http://arxiv.org/abs/2310.20601v2
• Date: Fri, 18 Oct 2024 17:07:01 GMT
• Title: Modular Boundaries in Recurrent Neural Networks
• Authors: Jacob Tanner, Sina Mansour L., Ludovico Coletta, Alessandro Gozzi, Richard F. Betzel,
• Abstract summary: We use a community detection method from network science known as modularity to partition neurons into distinct modules. These partitions allow us to ask the following question: do these modular boundaries matter to the system?
• Score: 39.626497874552555
• License:
• Abstract: Recent theoretical and experimental work in neuroscience has focused on the representational and dynamical character of neural manifolds --subspaces in neural activity space wherein many neurons coactivate. Importantly, neural populations studied under this "neural manifold hypothesis" are continuous and not cleanly divided into separate neural populations. This perspective clashes with the "modular hypothesis" of brain organization, wherein neural elements maintain an "all-or-nothing" affiliation with modules. In line with this modular hypothesis, recent research on recurrent neural networks suggests that multi-task networks become modular across training, such that different modules specialize for task-general dynamical motifs. If the modular hypothesis is true, then it would be important to use a dimensionality reduction technique that captures modular structure. Here, we investigate the features of such a method. We leverage RNNs as a model system to study the character of modular neural populations, using a community detection method from network science known as modularity maximization to partition neurons into distinct modules. These partitions allow us to ask the following question: do these modular boundaries matter to the system? ...

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)
• Growing Brains: Co-emergence of Anatomical and Functional Modularity in Recurrent Neural Networks [18.375521792153112]
  Recurrent neural networks (RNNs) trained on compositional tasks can exhibit functional modularity. We apply a recent machine learning method, brain-inspired modular training, to a network being trained to solve a set of compositional cognitive tasks. We find that functional and anatomical clustering emerge together, such that functionally similar neurons also become spatially localized and interconnected.
  arXiv  Detail & Related papers  (2023-10-11T17:58:25Z)
• Emergent Modularity in Pre-trained Transformers [127.08792763817496]
  We consider two main characteristics of modularity: functional specialization of neurons and function-based neuron grouping. We study how modularity emerges during pre-training, and find that the modular structure is stabilized at the early stage. It suggests that Transformers first construct the modular structure and then learn fine-grained neuron functions.
  arXiv  Detail & Related papers  (2023-05-28T11:02:32Z)
• Seeing is Believing: Brain-Inspired Modular Training for Mechanistic Interpretability [5.15188009671301]
  Brain-Inspired Modular Training is a method for making neural networks more modular and interpretable. BIMT embeds neurons in a geometric space and augments the loss function with a cost proportional to the length of each neuron connection.
  arXiv  Detail & Related papers  (2023-05-04T17:56:42Z)
• Ranking of Communities in Multiplex Spatiotemporal Models of Brain Dynamics [0.0]
  We propose an interpretation of neural HMMs as multiplex brain state graph models we term Hidden Markov Graph Models (HMs) This interpretation allows for dynamic brain activity to be analysed using the full repertoire of network analysis techniques. We produce a new tool for determining important communities of brain regions using a random walk-based procedure.
  arXiv  Detail & Related papers  (2022-03-17T12:14:09Z)
• POPPINS : A Population-Based Digital Spiking Neuromorphic Processor with Integer Quadratic Integrate-and-Fire Neurons [50.591267188664666]
  We propose a population-based digital spiking neuromorphic processor in 180nm process technology with two hierarchy populations. The proposed approach enables the developments of biomimetic neuromorphic system and various low-power, and low-latency inference processing applications.
  arXiv  Detail & Related papers  (2022-01-19T09:26:34Z)
• Detecting Modularity in Deep Neural Networks [8.967870619902211]
  We consider the problem of assessing the modularity exhibited by a partitioning of a network's neurons. We propose two proxies for this: importance, which reflects how crucial sets of neurons are to network performance; and coherence, which reflects how consistently their neurons associate with features of the inputs. We show that these partitionings, even ones based only on weights, reveal groups of neurons that are important and coherent.
  arXiv  Detail & Related papers  (2021-10-13T20:33:30Z)
• The Neural Coding Framework for Learning Generative Models [91.0357317238509]
  We propose a novel neural generative model inspired by the theory of predictive processing in the brain. In a similar way, artificial neurons in our generative model predict what neighboring neurons will do, and adjust their parameters based on how well the predictions matched reality.
  arXiv  Detail & Related papers  (2020-12-07T01:20:38Z)
• Non-linear Neurons with Human-like Apical Dendrite Activations [81.18416067005538]
  We show that a standard neuron followed by our novel apical dendrite activation (ADA) can learn the XOR logical function with 100% accuracy. We conduct experiments on six benchmark data sets from computer vision, signal processing and natural language processing.
  arXiv  Detail & Related papers  (2020-02-02T21:09:39Z)

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.