Most discriminative stimuli for functional cell type clustering

• URL: http://arxiv.org/abs/2401.05342v2
• Date: Thu, 14 Mar 2024 15:40:01 GMT
• Title: Most discriminative stimuli for functional cell type clustering
• Abstract summary: In the retina, functional types can be identified by carefully selected stimuli, but this requires expert domain knowledge. In the visual cortex, it is still unknown what functional types exist and how to identify them. Here we propose an optimization-based clustering approach using deep predictive models to obtain functional clusters of neurons.
• Score: 43.92016158113801
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types can be identified by carefully selected stimuli, but this requires expert domain knowledge and biases the procedure towards previously known cell types. In the visual cortex, it is still unknown what functional types exist and how to identify them. Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed. Here we propose an optimization-based clustering approach using deep predictive models to obtain functional clusters of neurons using Most Discriminative Stimuli (MDS). Our approach alternates between stimulus optimization with cluster reassignment akin to an expectation-maximization algorithm. The algorithm recovers functional clusters in mouse retina, marmoset retina and macaque visual area V4. This demonstrates that our approach can successfully find discriminative stimuli across species, stages of the visual system and recording techniques. The resulting most discriminative stimuli can be used to assign functional cell types fast and on the fly, without the need to train complex predictive models or show a large natural scene dataset, paving the way for experiments that were previously limited by experimental time. Crucially, MDS are interpretable: they visualize the distinctive stimulus patterns that most unambiguously identify a specific type of neuron.

Related papers

• Neuromorphic Online Clustering and Its Application to Spike Sorting [0.783218941317936]
  We develop neuromorphic dendrites as basic neural building blocks capable of dynamic online clustering.<n>Features and capabilities of neuromorphic dendrites are demonstrated via a benchmark drawn from experimental neuroscience: spike sorting.<n>The accuracy of the proposed dendrite is compared with the more compute-intensive, offline k-means clustering approach.
  arXiv  Detail & Related papers  (2025-06-14T15:53:55Z)
• Transformer brain encoders explain human high-level visual responses [0.0]
  We study how retinotopic visual features can be dynamically routed to category-selective areas in high-level visual processing.<n>We show that this computational motif is significantly more powerful than alternative methods in predicting brain activity during natural scene viewing.
  arXiv  Detail & Related papers  (2025-05-22T22:48:15Z)
• 3D Segmentation of Neuronal Nuclei and Cell-Type Identification using Multi-channel Information [2.6034750171634102]
  We present a method to improve the 3D reconstruction of neuronal nuclei that allows their segmentation. It was able to provide a good identification ratio of neuronal nuclei and a 3D segmentation.
  arXiv  Detail & Related papers  (2024-10-04T09:13:02Z)
• Finding Shared Decodable Concepts and their Negations in the Brain [4.111712524255376]
  We train a highly accurate contrastive model that maps brain responses during naturalistic image viewing to CLIP embeddings. We then use a novel adaptation of the DBSCAN clustering algorithm to cluster the parameters of participant-specific contrastive models. Examining the images most and least associated with each SDC cluster gives us additional insight into the semantic properties of each SDC.
  arXiv  Detail & Related papers  (2024-05-27T21:28:26Z)
• UniCell: Universal Cell Nucleus Classification via Prompt Learning [76.11864242047074]
  We propose a universal cell nucleus classification framework (UniCell) It employs a novel prompt learning mechanism to uniformly predict the corresponding categories of pathological images from different dataset domains. In particular, our framework adopts an end-to-end architecture for nuclei detection and classification, and utilizes flexible prediction heads for adapting various datasets.
  arXiv  Detail & Related papers  (2024-02-20T11:50:27Z)
• Single-Cell Deep Clustering Method Assisted by Exogenous Gene Information: A Novel Approach to Identifying Cell Types [50.55583697209676]
  We develop an attention-enhanced graph autoencoder, which is designed to efficiently capture the topological features between cells. During the clustering process, we integrated both sets of information and reconstructed the features of both cells and genes to generate a discriminative representation. This research offers enhanced insights into the characteristics and distribution of cells, thereby laying the groundwork for early diagnosis and treatment of diseases.
  arXiv  Detail & Related papers  (2023-11-28T09:14:55Z)
• Brain Diffusion for Visual Exploration: Cortical Discovery using Large Scale Generative Models [6.866437017874623]
  We introduce a data-driven approach in which we synthesize images predicted to activate a given brain region using paired natural images and fMRI recordings. Our approach builds on recent generative methods by combining large-scale diffusion models with brain-guided image synthesis. These results advance our understanding of the fine-grained functional organization of human visual cortex.
  arXiv  Detail & Related papers  (2023-06-05T17:59:05Z)
• A Neural Network Based Automated IFT-20 Sensory Neuron Classifier for Caenorhabditis elegans [0.0]
  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.
  arXiv  Detail & Related papers  (2022-10-24T00:17:26Z)
• Brain Cortical Functional Gradients Predict Cortical Folding Patterns via Attention Mesh Convolution [51.333918985340425]
  We develop a novel attention mesh convolution model to predict cortical gyro-sulcal segmentation maps on individual brains. Experiments show that the prediction performance via our model outperforms other state-of-the-art models.
  arXiv  Detail & Related papers  (2022-05-21T14:08:53Z)
• Self-Supervised Graph Representation Learning for Neuronal Morphologies [75.38832711445421]
  We present GraphDINO, a data-driven approach to learn low-dimensional representations of 3D neuronal morphologies from unlabeled datasets. We show, in two different species and across multiple brain areas, that this method yields morphological cell type clusterings on par with manual feature-based classification by experts. Our method could potentially enable data-driven discovery of novel morphological features and cell types in large-scale datasets.
  arXiv  Detail & Related papers  (2021-12-23T12:17:47Z)
• Towards Efficient Processing and Learning with Spikes: New Approaches for Multi-Spike Learning [59.249322621035056]
  We propose two new multi-spike learning rules which demonstrate better performance over other baselines on various tasks. In the feature detection task, we re-examine the ability of unsupervised STDP with its limitations being presented. Our proposed learning rules can reliably solve the task over a wide range of conditions without specific constraints being applied.
  arXiv  Detail & Related papers  (2020-05-02T06:41:20Z)

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.