A Neural Network Based Automated IFT-20 Sensory Neuron Classifier for Caenorhabditis elegans

• URL: http://arxiv.org/abs/2210.14961v1
• Date: Mon, 24 Oct 2022 00:17:26 GMT
• Title: A Neural Network Based Automated IFT-20 Sensory Neuron Classifier for Caenorhabditis elegans
• Authors: Arvind Seshan
• Abstract summary: Cross-organism comparison enables a wide variety of research including whole-brain analysis of functional networks. Recent development of pan-neuronal imaging with single-cell resolution within Caenorhabditis elegans has brought neuron identification, tracking, and activity monitoring all within reach. The principal barrier to high-accuracy neuron identification is that in adult C. elegans, the position of neuronal cell bodies is not stereotyped. I propose an alternative neuronal identification technique using only single-color fluorescent images.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Determining neuronal identity in imaging data is an essential task in neuroscience, facilitating the comparison of neural activity across organisms. Cross-organism comparison, in turn, enables a wide variety of research including whole-brain analysis of functional networks and linking the activity of specific neurons to behavior or environmental stimuli. The recent development of three-dimensional, pan-neuronal imaging with single-cell resolution within Caenorhabditis elegans has brought neuron identification, tracking, and activity monitoring all within reach. The nematode C. elegans is often used as a model organism to study neuronal activity due to factors such as its transparency and well-understood nervous system. The principal barrier to high-accuracy neuron identification is that in adult C. elegans, the position of neuronal cell bodies is not stereotyped. Existing approaches to address this issue use genetically encoded markers as an additional identifying feature. For example, the NeuroPAL strain uses multicolored fluorescent reporters. However, this approach has limited use due to the negative effects of excessive genetic modification. In this study, I propose an alternative neuronal identification technique using only single-color fluorescent images. I designed a novel neural network based classifier that automatically labels sensory neurons using an iterative, landmark-based neuron identification process inspired by the manual annotation procedures that humans employ. This design labels sensory neurons in C. elegans with 91.61% accuracy.

Related papers

• Error-margin Analysis for Hidden Neuron Activation Labels [1.1982127665424676]
  We argue that this is only the first-part of a two-part job, it is imperative to also investigate neuron responses to other stimuli, i.e., their precision. We call this the neuron labels error margin.
  arXiv  Detail & Related papers  (2024-05-14T19:13:50Z)
• Fast gradient-free activation maximization for neurons in spiking neural networks [5.805438104063613]
  We present a framework with an efficient design for such a loop. We track changes in the optimal stimuli for artificial neurons during training. This formation of refined optimal stimuli is associated with an increase in classification accuracy.
  arXiv  Detail & Related papers  (2023-12-28T18:30:13Z)
• Identifying Interpretable Visual Features in Artificial and Biological Neural Systems [3.604033202771937]
  Single neurons in neural networks are often interpretable in that they represent individual, intuitively meaningful features. Many neurons exhibit $textitmixed selectivity$, i.e., they represent multiple unrelated features. We propose an automated method for quantifying visual interpretability and an approach for finding meaningful directions in network activation space.
  arXiv  Detail & Related papers  (2023-10-17T17:41:28Z)
• Neuronal Cell Type Classification using Deep Learning [3.3517146652431378]
  Recent developments in machine learning have provided advanced abilities for classifying neurons. This paper aims to provide a robust and explainable deep-learning framework to classify neurons based on their electrophysiological activity.
  arXiv  Detail & Related papers  (2023-06-01T10:28:49Z)
• Constraints on the design of neuromorphic circuits set by the properties of neural population codes [61.15277741147157]
  In the brain, information is encoded, transmitted and used to inform behaviour. Neuromorphic circuits need to encode information in a way compatible to that used by populations of neuron in the brain.
  arXiv  Detail & Related papers  (2022-12-08T15:16:04Z)
• 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)
• Overcoming the Domain Gap in Contrastive Learning of Neural Action Representations [60.47807856873544]
  A fundamental goal in neuroscience is to understand the relationship between neural activity and behavior. We generated a new multimodal dataset consisting of the spontaneous behaviors generated by fruit flies. This dataset and our new set of augmentations promise to accelerate the application of self-supervised learning methods in neuroscience.
  arXiv  Detail & Related papers  (2021-11-29T15:27:51Z)
• Rapid detection and recognition of whole brain activity in a freely behaving Caenorhabditis elegans [18.788855494800238]
  We propose a cascade solution for long-term and rapid recognition of head ganglion neurons in a freely moving textitC. elegans. Under the constraint of a small number of training samples, our bottom-up approach is able to process each volume - $1024 times 1024 times 18$ in voxels - in less than 1 second. Our work represents an important development towards a rapid and fully automated algorithm for decoding whole brain activity underlying natural animal behaviors.
  arXiv  Detail & Related papers  (2021-09-22T01:33:54Z)
• The distribution of inhibitory neurons in the C. elegans connectome facilitates self-optimization of coordinated neural activity [78.15296214629433]
  The nervous system of the nematode Caenorhabditis elegans exhibits remarkable complexity despite the worm's small size. A general challenge is to better understand the relationship between neural organization and neural activity at the system level. We implemented an abstract simulation model of the C. elegans connectome that approximates the neurotransmitter identity of each neuron.
  arXiv  Detail & Related papers  (2020-10-28T23:11:37Z)
• Compositional Explanations of Neurons [52.71742655312625]
  We describe a procedure for explaining neurons in deep representations by identifying compositional logical concepts. We use this procedure to answer several questions on interpretability in models for vision and natural language processing.
  arXiv  Detail & Related papers  (2020-06-24T20:37:05Z)
• 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.