Neuronal Cell Type Classification using Deep Learning
- URL: http://arxiv.org/abs/2306.00528v1
- Date: Thu, 1 Jun 2023 10:28:49 GMT
- Title: Neuronal Cell Type Classification using Deep Learning
- Authors: Ofek Ophir, Orit Shefi, Ofir Lindenbaum
- Abstract summary: 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.
- Score: 3.3517146652431378
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The brain is likely the most complex organ, given the variety of functions it
controls, the number of cells it comprises, and their corresponding diversity.
Studying and identifying neurons, the brain's primary building blocks, is a
crucial milestone and essential for understanding brain function in health and
disease. Recent developments in machine learning have provided advanced
abilities for classifying neurons. However, these methods remain black boxes
with no explainability and reasoning. This paper aims to provide a robust and
explainable deep-learning framework to classify neurons based on their
electrophysiological activity. Our analysis is performed on data provided by
the Allen Cell Types database containing a survey of biological features
derived from single-cell recordings of mice and humans. First, we classify
neuronal cell types of mice data to identify excitatory and inhibitory neurons.
Then, neurons are categorized to their broad types in humans using domain
adaptation from mice data. Lastly, neurons are classified into sub-types based
on transgenic mouse lines using deep neural networks in an explainable fashion.
We show state-of-the-art results in a dendrite-type classification of
excitatory vs. inhibitory neurons and transgenic mouse lines classification.
The model is also inherently interpretable, revealing the correlations between
neuronal types and their electrophysiological properties.
Related papers
- Joint Learning Neuronal Skeleton and Brain Circuit Topology with Permutation Invariant Encoders for Neuron Classification [33.47541392305739]
We propose NeuNet framework that combines morphological information of neurons obtained from skeleton and topological information between neurons obtained from neural circuit.
We reprocess and release two new datasets for neuron classification task from volume electron microscopy(VEM) images of human brain cortex and Drosophila brain.
arXiv Detail & Related papers (2023-12-22T08:31:11Z) - 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) - Single Biological Neurons as Temporally Precise Spatio-Temporal Pattern
Recognizers [0.0]
thesis is focused on the central idea that single neurons in the brain should be regarded as temporally highly complex-temporal pattern recognizers.
In chapter 2 we demonstrate that single neurons can generate temporally precise output patterns in response to specific-temporal input patterns.
In chapter 3, we use the differentiable deep network of a realistic cortical neuron as a tool to approximate the implications of the output of the neuron.
arXiv Detail & Related papers (2023-09-26T17:32:08Z) - 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) - 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) - Natural Language Descriptions of Deep Visual Features [50.270035018478666]
We introduce a procedure that automatically labels neurons with open-ended, compositional, natural language descriptions.
We use MILAN for analysis, characterizing the distribution and importance of neurons selective for attribute, category, and relational information in vision models.
We also use MILAN for auditing, surfacing neurons sensitive to protected categories like race and gender in models trained on datasets intended to obscure these features.
arXiv Detail & Related papers (2022-01-26T18:48:02Z) - 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) - 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)
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.