Planarian Neural Networks: Evolutionary Patterns from Basic Bilateria Shaping Modern Artificial Neural Network Architectures

• URL: http://arxiv.org/abs/2501.04700v1
• Date: Wed, 08 Jan 2025 18:59:36 GMT
• Title: Planarian Neural Networks: Evolutionary Patterns from Basic Bilateria Shaping Modern Artificial Neural Network Architectures
• Authors: Ziyuan Huang, Mark Newman, Maria Vaida, Srikar Bellur, Roozbeh Sadeghian, Andrew Siu, Hui Wang, Kevin Huggins,
• Abstract summary: The aim of this study is to improve the image classification performance of ANNs via a novel approach inspired by the biological nervous system architecture of planarians. The proposed planarian neural architecture-based neural network was evaluated on the CIFAR-10 and CIFAR-100 datasets.
• Score: 7.054776300100835
• License:
• Abstract: This study examined the viability of enhancing the prediction accuracy of artificial neural networks (ANNs) in image classification tasks by developing ANNs with evolution patterns similar to those of biological neural networks. ResNet is a widely used family of neural networks with both deep and wide variants; therefore, it was selected as the base model for our investigation. The aim of this study is to improve the image classification performance of ANNs via a novel approach inspired by the biological nervous system architecture of planarians, which comprises a brain and two nerve cords. We believe that the unique neural architecture of planarians offers valuable insights into the performance enhancement of ANNs. The proposed planarian neural architecture-based neural network was evaluated on the CIFAR-10 and CIFAR-100 datasets. Our results indicate that the proposed method exhibits higher prediction accuracy than the baseline neural network models in image classification tasks. These findings demonstrate the significant potential of biologically inspired neural network architectures in improving the performance of ANNs in a wide range of applications.

Related papers

• Retinal Vessel Segmentation via Neuron Programming [17.609169389489633]
  This paper introduces a novel approach to neural network design, termed neuron programming'', to enhance a network's representation ability at the neuronal level. Comprehensive experiments validate that neuron programming can achieve competitive performance in retinal blood segmentation.
  arXiv  Detail & Related papers  (2024-11-17T16:03:30Z)
• Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling [52.06722364186432]
  We propose a biologically-informed framework for enhancing artificial neural networks (ANNs) Our proposed dual-framework approach highlights the potential of spiking neural networks (SNNs) for emulating diverse spiking behaviors. We outline how the proposed approach integrates brain-inspired compartmental models and task-driven SNNs, bioinspiration and complexity.
  arXiv  Detail & Related papers  (2024-07-05T14:11:28Z)
• Graph Neural Networks for Learning Equivariant Representations of Neural Networks [55.04145324152541]
  We propose to represent neural networks as computational graphs of parameters. Our approach enables a single model to encode neural computational graphs with diverse architectures. We showcase the effectiveness of our method on a wide range of tasks, including classification and editing of implicit neural representations.
  arXiv  Detail & Related papers  (2024-03-18T18:01:01Z)
• Reusability report: Prostate cancer stratification with diverse biologically-informed neural architectures [7.417447233454902]
  A feedforward neural network with biologically informed, sparse connections (P-NET) was presented to model the state of prostate cancer. We quantified the contribution of network sparsification by Reactome biological pathways, and confirmed its importance to P-NET's superior performance. We experimented with three types of graph neural networks on the same training data, and investigated the clinical prediction agreement between different models.
  arXiv  Detail & Related papers  (2023-09-28T17:51:02Z)
• Brain-inspired Evolutionary Architectures for Spiking Neural Networks [6.607406750195899]
  We explore efficient architectural optimization for Spiking Neural Networks (SNNs) This paper evolves SNNs architecture by incorporating brain-inspired local modular structure and global cross- module connectivity. We introduce an efficient multi-objective evolutionary algorithm based on a few-shot performance predictor, endowing SNNs with high performance, efficiency and low energy consumption.
  arXiv  Detail & Related papers  (2023-09-11T06:39:11Z)
• A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks [53.31941519245432]
  Brain-inspired spiking neural networks (SNNs) have demonstrated promising capabilities in solving pattern recognition tasks. These SNNs are grounded on homogeneous neurons that utilize a uniform neural coding for information representation. In this study, we argue that SNN architectures should be holistically designed to incorporate heterogeneous coding schemes.
  arXiv  Detail & Related papers  (2023-05-26T02:52:12Z)
• Spiking neural network for nonlinear regression [68.8204255655161]
  Spiking neural networks carry the potential for a massive reduction in memory and energy consumption. They introduce temporal and neuronal sparsity, which can be exploited by next-generation neuromorphic hardware. A framework for regression using spiking neural networks is proposed.
  arXiv  Detail & Related papers  (2022-10-06T13:04:45Z)
• 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)
• Max and Coincidence Neurons in Neural Networks [0.07614628596146598]
  We optimize networks containing models of the max and coincidence neurons using neural architecture search. We analyze the structure, operations, and neurons of optimized networks to develop a signal-processing ResNet. The developed network achieves an average of 2% improvement in accuracy and a 25% improvement in network size across a variety of datasets.
  arXiv  Detail & Related papers  (2021-10-04T07:13:50Z)
• Differentiable Neural Architecture Learning for Efficient Neural Network Design [31.23038136038325]
  We introduce a novel emph architecture parameterisation based on scaled sigmoid function. We then propose a general emphiable Neural Architecture Learning (DNAL) method to optimize the neural architecture without the need to evaluate candidate neural networks.
  arXiv  Detail & Related papers  (2021-03-03T02:03:08Z)
• Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks [50.684661759340145]
  Firefly neural architecture descent is a general framework for progressively and dynamically growing neural networks. We show that firefly descent can flexibly grow networks both wider and deeper, and can be applied to learn accurate but resource-efficient neural architectures. In particular, it learns networks that are smaller in size but have higher average accuracy than those learned by the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-02-17T04:47:18Z)

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.