[ { "id": "authors:5m31y-wfa08", "collection": "authors", "collection_id": "5m31y-wfa08", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20230628-257286000.41", "type": "monograph", "title": "Evolution of neuronal cell classes and types in the vertebrate retina", "author": [ { "family_name": "Hahn", "given_name": "Joshua", "orcid": "0000-0002-4776-2067", "clpid": "Hahn-Joshua" }, { "family_name": "Monavarfeshani", "given_name": "Aboozar", "orcid": "0000-0001-8906-5115", "clpid": "Monavarfeshani-Aboozar" }, { "family_name": "Qiao", "given_name": "Mu", "orcid": "0000-0001-7309-4237", "clpid": "Qiao-Mu" }, { "family_name": "Kao", "given_name": "Allison", "clpid": "Kao-Allison" }, { "family_name": "K\u00f6lsch", "given_name": "Yvonne", "orcid": "0000-0002-9953-7312", "clpid": "K\u00f6lsch-Yvonne" }, { "family_name": "Kumar", "given_name": "Ayush", "clpid": "Kumar-Ayush" }, { "family_name": "Kunze", "given_name": "Vincent P.", "orcid": "0000-0002-7869-9793", "clpid": "Kunze-Vincent-P" }, { "family_name": "Rasys", "given_name": "Ashley M.", "orcid": "0000-0002-4589-5456", "clpid": "Rasys-Ashley-M" }, { "family_name": "Richardson", "given_name": "Rose", "orcid": "0000-0001-7164-0337", "clpid": "Richardson-Rose" }, { "family_name": "Baier", "given_name": "Herwig", "orcid": "0000-0002-7268-0469", "clpid": "Baier-Herwig" }, { "family_name": "Lucas", "given_name": "Robert J.", "orcid": "0000-0002-1088-8029", "clpid": "Lucas-Robert-J" }, { "family_name": "Li", "given_name": "Wei", "orcid": "0000-0002-2897-649X", "clpid": "Li-Wei" }, { "family_name": "Meister", "given_name": "Markus", "orcid": "0000-0003-2136-6506", "clpid": "Meister-M" }, { "family_name": "Trachtenberg", "given_name": "Joshua T.", "orcid": "0000-0002-5041-774X", "clpid": "Trachtenberg-Joshua-T" }, { "family_name": "Yan", "given_name": "Wenjun", "orcid": "0000-0003-3568-4265", "clpid": "Yan-Wenjun" }, { "family_name": "Peng", "given_name": "Yi-Rong", "orcid": "0000-0002-5689-2779", "clpid": "Peng-Yi-Rong" }, { "family_name": "Sanes", "given_name": "Joshua R.", "orcid": "0000-0001-8926-8836", "clpid": "Sanes-Joshua-R" }, { "family_name": "Shekhar", "given_name": "Karthik", "orcid": "0000-0003-4349-6600", "clpid": "Shejkhar-Karthik" } ], "abstract": "The basic plan of the retina is conserved across vertebrates, yet species differ profoundly in their visual needs (Baden et al., 2020). One might expect that retinal cell types evolved to accommodate these varied needs, but this has not been systematically studied. Here, we generated and integrated single-cell transcriptomic atlases of the retina from 17 species: humans, two non-human primates, four rodents, three ungulates, opossum, ferret, tree shrew, a teleost fish, a bird, a reptile and a lamprey. Molecular conservation of the six retinal cell classes (photoreceptors, horizontal cells, bipolar cells, amacrine cells, retinal ganglion cells [RGCs] and M\u00fcller glia) is striking, with transcriptomic differences across species correlated with evolutionary distance. Major subclasses are also conserved, whereas variation among types within classes or subclasses is more pronounced. However, an integrative analysis revealed that numerous types are shared across species based on conserved gene expression programs that likely trace back to the common ancestor of jawed vertebrates. The degree of variation among types increases from the outer retina (photoreceptors) to the inner retina (RGCs), suggesting that evolution acts preferentially to shape the retinal output. Finally, we identified mammalian orthologs of midget RGCs, which comprise >80% of RGCs in the human retina, subserve high-acuity vision, and were believed to be primate-specific (Berson, 2008); in contrast, the mouse orthologs comprise <2% of mouse RGCs. Projections both primate and mouse orthologous types are overrepresented in the thalamus, which supplies the primary visual cortex. We suggest that midget RGCs are not primate innovations, but descendants of evolutionarily ancient types that decreased in size and increased in number as primates evolved, thereby facilitating high visual acuity and increased cortical processing of visual information.", "doi": "10.1101/2023.04.07.536039", "pmcid": "PMC10104162", "publication_date": "2023-04-08" }, { "id": "authors:gmn1a-mm051", "collection": "authors", "collection_id": "gmn1a-mm051", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20220406-729141742", "type": "monograph", "title": "Functional Cell Types in the Mouse Superior Colliculus", "author": [ { "family_name": "Li", "given_name": "Ya-tang", "orcid": "0000-0003-2763-1534", "clpid": "Li-Ya-tang" }, { "family_name": "Meister", "given_name": "Markus", "orcid": "0000-0003-2136-6506", "clpid": "Meister-M" } ], "abstract": "The superior colliculus (SC) represents a major visual processing station in the mammalian brain that receives input from many types of retinal ganglion cells (RGCs). How many parallel channels exist in the SC, and what information does each encode? Here we recorded from mouse superficial SC neurons under a battery of visual stimuli including those used for classification of RGCs. An unsupervised clustering algorithm identified 24 functional types based on their visual responses. They fall into two groups: one that responds similarly to RGCs, and another with more diverse and specialized stimulus selectivity. The second group is dominant at greater depths, consistent with a vertical progression of signal processing in the SC. Cells of the same functional type tend to cluster near each other in anatomical space. Compared to the retina, the visual representation in the SC has lower dimensionality, consistent with a sifting process along the visual pathway.", "doi": "10.1101/2022.04.01.486789", "publication_date": "2022-04-05" }, { "id": "authors:9ddh3-bfe46", "collection": "authors", "collection_id": "9ddh3-bfe46", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20210929-162922601", "type": "monograph", "title": "Endotaxis: A Universal Algorithm for Mapping, Goal-Learning, and Navigation", "author": [ { "family_name": "Zhang", "given_name": "Tony", "clpid": "Zhang-Tony" }, { "family_name": "Rosenberg", "given_name": "Matthew", "clpid": "Rosenberg-Matthew" }, { "family_name": "Perona", "given_name": "Pietro", "orcid": "0000-0002-7583-5809", "clpid": "Perona-P" }, { "family_name": "Meister", "given_name": "Markus", "orcid": "0000-0003-2136-6506", "clpid": "Meister-M" } ], "abstract": "An animal entering a new environment typically faces three challenges: explore the space for resources, memorize their locations, and navigate towards those targets as needed. Experimental work on exploration, mapping, and navigation has mostly focused on simple environments \u2013 such as an open arena, a pond [1], or a desert [2] \u2013 and much has been learned about neural signals in diverse brain areas under these conditions [3, 4]. However, many natural environments are highly constrained, such as a system of burrows, or of paths through the underbrush. More generally, many cognitive tasks are equally constrained, allowing only a small set of actions at any given stage in the process. Here we propose an algorithm that learns the structure of an arbitrary environment, discovers useful targets during exploration, and navigates back to those targets by the shortest path. It makes use of a behavioral module common to all motile animals, namely the ability to follow an odor to its source [5]. We show how the brain can learn to generate internal \"virtual odors\" that guide the animal to any location of interest. This endotaxis algorithm can be implemented with a simple 3-layer neural circuit using only biologically realistic structures and learning rules. Several neural components of this scheme are found in brains from insects to humans. Nature may have evolved a general mechanism for search and navigation on the ancient backbone of chemotaxis.", "doi": "10.1101/2021.09.24.461751", "publication_date": "2021-09-25" }, { "id": "authors:kc0zb-px694", "collection": "authors", "collection_id": "kc0zb-px694", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20211217-233144809", "type": "monograph", "title": "Curved Micro-Electrode Arrays", "author": [ { "family_name": "Meister", "given_name": "Markus", "orcid": "0000-0003-2136-6506", "clpid": "Meister-M" } ], "abstract": "Multi-electrode arrays serve to record electrical signals of many neurons in the brain simultaneously. For most of the past century, electrodes that penetrate brain tissue have had exactly one shape: a straight needle. Certainly this was a good starting choice at the time, but there is no reason to think that a straight line would be the optimal shape in all Neuroscience applications. Here I argue that, in fact, a wide variety of curved shapes is equally practical: all possible helices. I discuss the manufacture and manipulation of such devices, and illustrate a few use cases where they will likely outperform conventional needles. With some collective action from the research community, curved arrays could be manufactured and distributed at low cost.", "doi": "10.48550/arXiv.2107.13532", "publication_date": "2021-07-26" }, { "id": "authors:55fz4-f7f55", "collection": "authors", "collection_id": "55fz4-f7f55", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20210105-133427535", "type": "monograph", "title": "Factorized linear discriminant analysis and its application in computational biology", "author": [ { "family_name": "Qiao", "given_name": "Mu", "orcid": "0000-0001-7309-4237", "clpid": "Qiao-Mu" }, { "family_name": "Meister", "given_name": "Markus", "orcid": "0000-0003-2136-6506", "clpid": "Meister-M" } ], "abstract": "A fundamental problem in computational biology is to find a suitable representation of the high-dimensional gene expression data that is consistent with the structural and functional properties of cell types, collectively called their phenotypes. This representation is often sought from a linear transformation of the original data, for the reasons of model interpretability and computational simplicity. Here we propose a novel method of linear dimensionality reduction to address this problem. This method, which we call factorized linear discriminant analysis (FLDA), seeks a linear transformation of gene expressions that varies highly with only one phenotypic feature and minimally with others. We further leverage our approach with a sparsity-based regularization algorithm, which selects a few genes important to a specific phenotypic feature or feature combination. We illustrated this approach by applying it to a single-cell transcriptome dataset of Drosophila T4/T5 neurons. A representation from FLDA captured structures in the data aligned with phenotypic features and revealed critical genes for each phenotype.", "doi": "10.48550/arXiv.2010.02171", "publisher": "arXiv", "publication_date": "2020-10-05" }, { "id": "authors:t3df1-gzb61", "collection": "authors", "collection_id": "t3df1-gzb61", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20200526-134149667", "type": "monograph", "title": "PanDA: Panoptic Data Augmentation", "author": [ { "family_name": "Liu", "given_name": "Yang", "clpid": "Liu-Yang-Electrical-Engineering" }, { "family_name": "Perona", "given_name": "Pietro", "orcid": "0000-0002-7583-5809", "clpid": "Perona-P" }, { "family_name": "Meister", "given_name": "Markus", "orcid": "0000-0003-2136-6506", "clpid": "Meister-M" } ], "abstract": "The recently proposed panoptic segmentation task presents a significant challenge of image understanding with computer vision by unifying semantic segmentation and instance segmentation tasks. In this paper we present an efficient and novel panoptic data augmentation (PanDA) method which operates exclusively in pixel space, requires no additional data or training, and is computationally cheap to implement. By retraining original state-of-the-art models on PanDA augmented datasets generated with a single frozen set of parameters, we show robust performance gains in panoptic segmentation, instance segmentation, as well as detection across models, backbones, dataset domains, and scales. Finally, the effectiveness of unrealistic-looking training images synthesized by PanDA suggest that one should rethink the need for image realism for efficient data augmentation.", "doi": "10.48550/arXiv.1911.12317", "publisher": "arXiv", "publication_date": "2019-11-27" }, { "id": "authors:0e7hj-w0v41", "collection": "authors", "collection_id": "0e7hj-w0v41", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20181128-093526738", "type": "monograph", "title": "Mouse Academy: high-throughput automated training and trial-by-trial behavioral analysis during learning", "author": [ { "family_name": "Qiao", "given_name": "Mu", "orcid": "0000-0001-7309-4237", "clpid": "Qiao-Mu" }, { "family_name": "Zhang", "given_name": "Tony", "clpid": "Zhang-Tony" }, { "family_name": "Segalin", "given_name": "Cristina", "orcid": "0000-0001-7219-7074", "clpid": "Segalin-C" }, { "family_name": "Sam", "given_name": "Sarah", "clpid": "Sam-Sarah" }, { "family_name": "Perona", "given_name": "Pietro", "orcid": "0000-0002-7583-5809", "clpid": "Perona-P" }, { "family_name": "Meister", "given_name": "Markus", "orcid": "0000-0003-2136-6506", "clpid": "Meister-M" } ], "abstract": "Progress in understanding how individual animals learn will require high-throughput standardized methods for behavioral training but also advances in the analysis of the resulting behavioral data. In the course of training with multiple trials, an animal may change its behavior abruptly, and capturing such events calls for a trial-by-trial analysis of the animal's strategy. To address this challenge, we developed an integrated platform for automated animal training and analysis of behavioral data. A low-cost and space-efficient apparatus serves to train entire cohorts of mice on a decision-making task under identical conditions. A generalized linear model (GLM) analyzes each animal's performance at single-trial resolution. This model infers the momentary decision-making strategy and can predict the animal's choice on each trial with an accuracy of ~80%. We also introduce automated software to assess the animal's detailed trajectories and body poses within the apparatus. Unsupervised analysis of these features revealed unusual trajectories that represent hesitation in the response. This integrated hardware/software platform promises to accelerate the understanding of animal learning.", "doi": "10.1101/467878", "publication_date": "2018-11-16" }, { "id": "authors:egasq-d9c23", "collection": "authors", "collection_id": "egasq-d9c23", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20181030-105013410", "type": "monograph", "title": "Optimal Sensory Coding By Populations Of ON And OFF Neurons", "author": [ { "family_name": "Gjorgjieva", "given_name": "Julijana", "orcid": "0000-0001-7118-4079", "clpid": "Gjorgjieva-J" }, { "family_name": "Meister", "given_name": "Markus", "orcid": "0000-0003-2136-6506", "clpid": "Meister-M" }, { "family_name": "Sompolinsky", "given_name": "Haim", "clpid": "Sompolinsky-H" } ], "abstract": "In many sensory systems the neural signal is coded by multiple parallel pathways, suggesting an evolutionary fitness benefit of general nature. A common pathway splitting is that into ON and OFF cells, responding to stimulus increments and decrements, respectively. According to efficient coding theory, sensory neurons have evolved to an optimal configuration for maximizing information transfer given the structure of natural stimuli and circuit constraints. Using the efficient coding framework, we describe two aspects of neural coding: how to optimally split a population into ON and OFF pathways, and how to allocate the firing thresholds of individual neurons given realistic noise levels, stimulus distributions and optimality measures. We find that populations of ON and OFF neurons convey equal information about the stimulus regardless of the ON/OFF mixture, once the thresholds are chosen optimally, independent of stimulus statistics and noise. However, an equal ON/OFF mixture is the most efficient as it uses the fewest spikes to convey this information. The optimal thresholds and coding efficiency, however, depend on noise and stimulus statistics if information is decoded by an optimal linear readout. With non-negligible noise, mixed ON/OFF populations reap significant advantages compared to a homogeneous population. The best coding performance is achieved by a unique mixture of ON/OFF neurons tuned to stimulus asymmetries and noise. We provide a theory for how different cell types work together to encode the full stimulus range using a diversity of response thresholds. The optimal ON/OFF mixtures derived from the theory accord with certain biases observed experimentally.", "doi": "10.1101/131946", "publication_date": "2017-04-28" } ]