Know Thyself by Knowing Others: Learning Neuron Identity from Population Context

• URL: http://arxiv.org/abs/2512.01199v1
• Date: Mon, 01 Dec 2025 02:28:04 GMT
• Title: Know Thyself by Knowing Others: Learning Neuron Identity from Population Context
• Authors: Vinam Arora, Divyansha Lachi, Ian J. Knight, Mehdi Azabou, Blake Richards, Cole L. Hurwitz, Josh Siegle, Eva L. Dyer,
• Abstract summary: We present the first systematic scaling analysis for neuron-level representation learning.<n>We show that increasing the number of animals used during pretraining consistently improves downstream performance.<n>Results highlight how large, diverse neural datasets enable models to recover information about neuron identity that generalize across animals.
• Score: 9.798773806523114
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Neurons process information in ways that depend on their cell type, connectivity, and the brain region in which they are embedded. However, inferring these factors from neural activity remains a significant challenge. To build general-purpose representations that allow for resolving information about a neuron's identity, we introduce NuCLR, a self-supervised framework that aims to learn representations of neural activity that allow for differentiating one neuron from the rest. NuCLR brings together views of the same neuron observed at different times and across different stimuli and uses a contrastive objective to pull these representations together. To capture population context without assuming any fixed neuron ordering, we build a spatiotemporal transformer that integrates activity in a permutation-equivariant manner. Across multiple electrophysiology and calcium imaging datasets, a linear decoding evaluation on top of NuCLR representations achieves a new state-of-the-art for both cell type and brain region decoding tasks, and demonstrates strong zero-shot generalization to unseen animals. We present the first systematic scaling analysis for neuron-level representation learning, showing that increasing the number of animals used during pretraining consistently improves downstream performance. The learned representations are also label-efficient, requiring only a small fraction of labeled samples to achieve competitive performance. These results highlight how large, diverse neural datasets enable models to recover information about neuron identity that generalize across animals. Code is available at https://github.com/nerdslab/nuclr.

Related papers

• Neuron Platonic Intrinsic Representation From Dynamics Using Contrastive Learning [4.538151592901714]
  We propose the NeurPIR (Neuron Platonic Intrinsic Representation) framework.<n>It uses contrastive learning, with segments from the same neuron as positive pairs and those from different neurons as negative pairs.<n>We tested our method on Izhikevich model-simulated neuronal population dynamics data.
  arXiv  Detail & Related papers  (2025-02-06T02:22:23Z)
• Integration of Calcium Imaging Traces via Deep Generative Modeling [0.0]
  We show how to learn single-neuron representations from calcium imaging fluorescence traces without relying on spike inference algorithms.<n>We find that this approach outperforms state-of-the-art models, preserving biological variability while mitigating batch effects.<n>This framework enables robust visualization, clustering, and interpretation of single-neuron dynamics.
  arXiv  Detail & Related papers  (2025-01-24T16:33:52Z)
• Interpreting the Second-Order Effects of Neurons in CLIP [73.54377859089801]
  We interpret the function of individual neurons in CLIP by automatically describing them using text.<n>We present the "second-order lens", analyzing the effect flowing from a neuron through the later attention heads, directly to the output.<n>Our results indicate that an automated interpretation of neurons can be used for model deception and for introducing new model capabilities.
  arXiv  Detail & Related papers  (2024-06-06T17:59:52Z)
• Neuron to Graph: Interpreting Language Model Neurons at Scale [8.32093320910416]
  This paper introduces a novel automated approach designed to scale interpretability techniques across a vast array of neurons within Large Language Models. We propose Neuron to Graph (N2G), an innovative tool that automatically extracts a neuron's behaviour from the dataset it was trained on and translates it into an interpretable graph.
  arXiv  Detail & Related papers  (2023-05-31T14:44:33Z)
• 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)
• Overcoming the Domain Gap in Neural Action Representations [60.47807856873544]
  3D pose data can now be reliably extracted from multi-view video sequences without manual intervention. We propose to use it to guide the encoding of neural action representations together with a set of neural and behavioral augmentations. To reduce the domain gap, during training, we swap neural and behavioral data across animals that seem to be performing similar actions.
  arXiv  Detail & Related papers  (2021-12-02T12:45:46Z)
• 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)
• Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity [33.06823702945747]
  We introduce a novel unsupervised approach for learning disentangled representations of neural activity called Swap-VAE. Our approach combines a generative modeling framework with an instance-specific alignment loss. We show that it is possible to build representations that disentangle neural datasets along relevant latent dimensions linked to behavior.
  arXiv  Detail & Related papers  (2021-11-03T16:39:43Z)
• And/or trade-off in artificial neurons: impact on adversarial robustness [91.3755431537592]
  Presence of sufficient number of OR-like neurons in a network can lead to classification brittleness and increased vulnerability to adversarial attacks. We define AND-like neurons and propose measures to increase their proportion in the network. Experimental results on the MNIST dataset suggest that our approach holds promise as a direction for further exploration.
  arXiv  Detail & Related papers  (2021-02-15T08:19:05Z)
• 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)
• Learning Neural Activations [2.842794675894731]
  We explore what happens when the activation function of each neuron in an artificial neural network is learned from data alone. This is achieved by modelling the activation function of each neuron as a small neural network whose weights are shared by all neurons in the original network.
  arXiv  Detail & Related papers  (2019-12-27T15:52:07Z)

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.