Deep Continuous Networks

• URL: http://arxiv.org/abs/2402.01557v1
• Date: Fri, 2 Feb 2024 16:50:18 GMT
• Title: Deep Continuous Networks
• Authors: Nergis Tomen, Silvia L. Pintea, Jan C. van Gemert
• Abstract summary: We propose deep continuous networks (DCNs), which combine spatially continuous filters, with the continuous depth framework of neural ODEs. This allows us to learn the spatial support of the filters during training, as well as model the continuous evolution of feature maps, linking DCNs closely to biological models. We show that DCNs are versatile and highly applicable to standard image classification and reconstruction problems, where they improve parameter and data efficiency, and allow for meta-parametrization.
• Score: 21.849285945717632
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: CNNs and computational models of biological vision share some fundamental principles, which opened new avenues of research. However, fruitful cross-field research is hampered by conventional CNN architectures being based on spatially and depthwise discrete representations, which cannot accommodate certain aspects of biological complexity such as continuously varying receptive field sizes and dynamics of neuronal responses. Here we propose deep continuous networks (DCNs), which combine spatially continuous filters, with the continuous depth framework of neural ODEs. This allows us to learn the spatial support of the filters during training, as well as model the continuous evolution of feature maps, linking DCNs closely to biological models. We show that DCNs are versatile and highly applicable to standard image classification and reconstruction problems, where they improve parameter and data efficiency, and allow for meta-parametrization. We illustrate the biological plausibility of the scale distributions learned by DCNs and explore their performance in a neuroscientifically inspired pattern completion task. Finally, we investigate an efficient implementation of DCNs by changing input contrast.

Related papers

• The Dynamic Net Architecture: Learning Robust and Holistic Visual Representations Through Self-Organizing Networks [3.9848584845601014]
  We present a novel intelligent-system architecture called "Dynamic Net Architecture" (DNA) DNA relies on recurrence-stabilized networks and discuss it in application to vision.
  arXiv  Detail & Related papers  (2024-07-08T06:22:10Z)
• Unveiling the Unseen: Identifiable Clusters in Trained Depthwise Convolutional Kernels [56.69755544814834]
  Recent advances in depthwise-separable convolutional neural networks (DS-CNNs) have led to novel architectures. This paper reveals another striking property of DS-CNN architectures: discernible and explainable patterns emerge in their trained depthwise convolutional kernels in all layers.
  arXiv  Detail & Related papers  (2024-01-25T19:05:53Z)
• Neural Echos: Depthwise Convolutional Filters Replicate Biological Receptive Fields [56.69755544814834]
  We present evidence suggesting that depthwise convolutional kernels are effectively replicating the biological receptive fields observed in the mammalian retina. We propose a scheme that draws inspiration from the biological receptive fields.
  arXiv  Detail & Related papers  (2024-01-18T18:06:22Z)
• How neural networks learn to classify chaotic time series [77.34726150561087]
  We study the inner workings of neural networks trained to classify regular-versus-chaotic time series. We find that the relation between input periodicity and activation periodicity is key for the performance of LKCNN models.
  arXiv  Detail & Related papers  (2023-06-04T08:53:27Z)
• Multi-scale Evolutionary Neural Architecture Search for Deep Spiking Neural Networks [7.271032282434803]
  We propose a Multi-Scale Evolutionary Neural Architecture Search (MSE-NAS) for Spiking Neural Networks (SNNs) MSE-NAS evolves individual neuron operation, self-organized integration of multiple circuit motifs, and global connectivity across motifs through a brain-inspired indirect evaluation function, Representational Dissimilarity Matrices (RDMs) The proposed algorithm achieves state-of-the-art (SOTA) performance with shorter simulation steps on static datasets and neuromorphic datasets.
  arXiv  Detail & Related papers  (2023-04-21T05:36:37Z)
• Improving Neural Predictivity in the Visual Cortex with Gated Recurrent Connections [0.0]
  We aim to shift the focus on architectures that take into account lateral recurrent connections, a ubiquitous feature of the ventral visual stream, to devise adaptive receptive fields. In order to increase the robustness of our approach and the biological fidelity of the activations, we employ specific data augmentation techniques.
  arXiv  Detail & Related papers  (2022-03-22T17:27:22Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• Finite Meta-Dynamic Neurons in Spiking Neural Networks for Spatio-temporal Learning [13.037452551907657]
  Spiking Neural Networks (SNNs) have incorporated more biologically-plausible structures and learning principles. We propose Meta-Dynamic Neurons (MDNs) to improve SNNs for a better network generalization during-temporal learning. The MDNs generated from a spatial (MNIST) and a temporal (TIts) datasets first and then extended to various other different-temporal tasks.
  arXiv  Detail & Related papers  (2020-10-07T03:49:28Z)
• An Ode to an ODE [78.97367880223254]
  We present a new paradigm for Neural ODE algorithms, called ODEtoODE, where time-dependent parameters of the main flow evolve according to a matrix flow on the group O(d) This nested system of two flows provides stability and effectiveness of training and provably solves the gradient vanishing-explosion problem.
  arXiv  Detail & Related papers  (2020-06-19T22:05:19Z)
• Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks [55.0627904986664]
  Spiking Neural Networks (SNNs) usetemporal spike patterns to represent and transmit information, which is not only biologically realistic but also suitable for ultra-low-power event-driven neuromorphic implementation. This paper investigates the contribution of spike timing dynamics to information encoding, synaptic plasticity and decision making, providing a new perspective to design of future DeepSNNs and neuromorphic hardware systems.
  arXiv  Detail & Related papers  (2020-03-26T11:13:07Z)
• Exploiting Neuron and Synapse Filter Dynamics in Spatial Temporal Learning of Deep Spiking Neural Network [7.503685643036081]
  A bio-plausible SNN model with spatial-temporal property is a complex dynamic system. We formulate SNN as a network of infinite impulse response (IIR) filters with neuron nonlinearity. We propose a training algorithm that is capable to learn spatial-temporal patterns by searching for the optimal synapse filter kernels and weights.
  arXiv  Detail & Related papers  (2020-02-19T01:27:39Z)

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.