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Results 1 through 20 out of 10872

in category neuroscience

 

1: Deep image reconstruction from human brain activity

Guohua Shen, Tomoyasu Horikawa et al.

122,275 downloads (posted 28 Dec 2017)

Machine learning-based analysis of human functional magnetic resonance imaging (fMRI) patterns has enabled the visualization of perceptual content. However, it has been limited to the reconstruction with low-level image bases or to the matching to exemplars. Recent work showed that visual cortical activity can be decoded (translated) into hierarchical features of a deep neural network (DNN) for the same input image, providing a way to make use of the information from hierarchical visual features. Here, we present a novel image reconstruction method, in which the pixel values of an image are optimized to make its DNN features similar to those decoded from human brain activity at multiple layers. We found that the generated images resembled the stimulus images (both natural images and artificial shapes) and the subjective visual content during imagery. While our model was solely trained with natural images, our method successfully generalized the reconstruction to artificial shapes, indicating that our model indeed reconstructs or generates images from brain activity, not simply matches to exemplars. A natural image prior introduced by another deep neural network effectively rendered semantically meaningful details to reconstructions by constraining reconstructed images to be similar to natural images. Furthermore, human judgment of reconstructions suggests the effectiveness of combining multiple DNN layers to enhance visual quality of generated images. The results suggest that hierarchical visual information in the brain can be effectively combined to reconstruct perceptual and subjective images.

https://rxivist.org/papers/14036
https://doi.org/10.1101/240317

2: Could a neuroscientist understand a microprocessor?

Eric Jonas, Konrad P Kording

101,025 downloads (posted 26 May 2016)

There is a popular belief in neuroscience that we are primarily data limited, and that producing large, multimodal, and complex datasets will, with the help of advanced data analysis algorithms, lead to fundamental insights into the way the brain processes information. These datasets do not yet exist, and if they did we would have no way of evaluating whether or not the algorithmically-generated insights were sufficient or even correct. To address this, here we take a classical microprocessor as a model organism, and us...

https://rxivist.org/papers/16066
https://doi.org/10.1101/055624

3: An integrated brain-machine interface platform with thousands of channels

Elon Musk, Neuralink

82,021 downloads (posted 17 Jul 2019)

Brain-machine interfaces (BMIs) hold promise for the restoration of sensory and motor function and the treatment of neurological disorders, but clinical BMIs have not yet been widely adopted, in part because modest channel counts have limited their potential. In this white paper, we describe Neuralink’s first steps toward a scalable high-bandwidth BMI system. We have built arrays of small and flexible electrode “threads”, with as many as 3,072 electrodes per array distributed across 96 threads. We have also built a neur...

https://rxivist.org/papers/55953
https://doi.org/10.1101/703801

4: Prefrontal cortex as a meta-reinforcement learning system

Jane X Wang, Zeb Kurth-Nelson et al.

27,701 downloads (posted 06 Apr 2018)

Over the past twenty years, neuroscience research on reward-based learning has converged on a canonical model, under which the neurotransmitter dopamine 'stamps in' associations between situations, actions and rewards by modulating the strength of synaptic connections between neurons. However, a growing number of recent findings have placed this standard model under strain. In the present work, we draw on recent advances in artificial intelligence to introduce a new theory of reward-based learning. Here, the dopamine sy...

https://rxivist.org/papers/13080
https://doi.org/10.1101/295964

5: Towards an integration of deep learning and neuroscience

Adam H Marblestone, Greg Wayne et al.

26,946 downloads (posted 13 Jun 2016)

Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits. In machine learning, however, artificial neural networks tend to eschew precisely designed codes, dynamics or circuits in favor of brute force optimization of a cost function, often using simple and relatively uniform initial architectures. Two recent developments have emerged within machine learning that create an opportunity to connect these seemingly divergent perspectives. First, structured architect...

https://rxivist.org/papers/16272
https://doi.org/10.1101/058545

6: Sex Differences In The Adult Human Brain: Evidence From 5,216 UK Biobank Participants

Stuart J Ritchie, Simon R Cox et al.

22,433 downloads (posted 04 Apr 2017)

Sex differences in the human brain are of interest, for example because of sex differences in the observed prevalence of psychiatric disorders and in some psychological traits. We report the largest single-sample study of structural and functional sex differences in the human brain (2,750 female, 2,466 male participants; 44-77 years). Males had higher volumes, surface areas, and white matter fractional anisotropy; females had thicker cortices and higher white matter tract complexity. There was considerable distributiona...

https://rxivist.org/papers/13873
https://doi.org/10.1101/123729

7: Why Does the Neocortex Have Columns, A Theory of Learning the Structure of the World

Jeff Hawkins, Subutai Ahmad et al.

20,515 downloads (posted 12 Jul 2017)

Neocortical regions are organized into columns and layers. Connections between layers run mostly perpendicular to the surface suggesting a columnar functional organization. Some layers have long-range excitatory lateral connections suggesting interactions between columns. Similar patterns of connectivity exist in all regions but their exact role remain a mystery. In this paper, we propose a network model composed of columns and layers that performs robust object learning and recognition. Each column integrates its chang...

https://rxivist.org/papers/14736
https://doi.org/10.1101/162263

8: The hippocampus as a predictive map

Kimberly Lauren Stachenfeld, Matthew M. Botvinick et al.

14,768 downloads (posted 28 Dec 2016)

A cognitive map has long been the dominant metaphor for hippocampal function, embracing the idea that place cells encode a geometric representation of space. However, evidence for predictive coding, reward sensitivity, and policy dependence in place cells suggests that the representation is not purely spatial. We approach this puzzle from a reinforcement learning perspective: what kind of spatial representation is most useful for maximizing future reward? We show that the answer takes the form of a predictive representa...

https://rxivist.org/papers/15114
https://doi.org/10.1101/097170

9: Using DeepLabCut for 3D markerless pose estimation across species and behaviors

Tanmay Nath, Mackenzie W. Mathis et al.

14,513 downloads (posted 24 Nov 2018)

Noninvasive behavioral tracking of animals during experiments is crucial to many scientific pursuits. Extracting the poses of animals without using markers is often essential for measuring behavioral effects in biomechanics, genetics, ethology & neuroscience. Yet, extracting detailed poses without markers in dynamically changing backgrounds has been challenging. We recently introduced an open source toolbox called DeepLabCut that builds on a state-of-the-art human pose estimation algorithm to allow a user to train a dee...

https://rxivist.org/papers/37145
https://doi.org/10.1101/476531

10: A Framework for Intelligence and Cortical Function Based on Grid Cells in the Neocortex

Jeff Hawkins, Marcus Lewis et al.

13,691 downloads (posted 13 Oct 2018)

How the neocortex works is a mystery. In this paper we propose a novel framework for understanding its function. Grid cells are neurons in the entorhinal cortex that represent the location of an animal in its environment. Recent evidence suggests that grid cell-like neurons may also be present in the neocortex. We propose that grid cells exist throughout the neocortex, in every region and in every cortical column. They define a location-based framework for how the neocortex functions. Whereas grid cells in the entorhina...

https://rxivist.org/papers/34593
https://doi.org/10.1101/442418

11: Panoptic vDISCO imaging reveals neuronal connectivity, remote trauma effects and meningeal vessels in intact transparent mice

Ruiyao Cai, Chenchen Pan et al.

11,943 downloads (posted 23 Jul 2018)

Analysis of entire transparent rodent bodies could provide holistic information on biological systems in health and disease. However, it has been challenging to reliably image and quantify signal from endogenously expressed fluorescent proteins in large cleared mouse bodies due to the low signal contrast. Here, we devised a pressure driven, nanobody based whole-body immunolabeling technology to enhance the signal of fluorescent proteins by up to two orders of magnitude. This allowed us to image subcellular details in tr...

https://rxivist.org/papers/11935
https://doi.org/10.1101/374785

12: The successor representation in human reinforcement learning

Ida Momennejad, Evan M. Russek et al.

11,875 downloads (posted 27 Oct 2016)

Theories of reward learning in neuroscience have focused on two families of algorithms, thought to capture deliberative vs. habitual choice. Model-based algorithms compute the value of candidate actions from scratch, whereas model-free algorithms make choice more efficient but less flexible by storing pre-computed action values. We examine an intermediate algorithmic family, the successor representation (SR), which balances flexibility and efficiency by storing partially computed action values: predictions about future ...

https://rxivist.org/papers/15241
https://doi.org/10.1101/083824

13: Deep neural networks: a new framework for modelling biological vision and brain information processing

Nikolaus Kriegeskorte

11,103 downloads (posted 26 Oct 2015)

Recent advances in neural network modelling have enabled major strides in computer vision and other artificial intelligence applications. Human-level visual recognition abilities are coming within reach of artificial systems. Artificial neural networks are inspired by the brain and their computations could be implemented in biological neurons. Convolutional feedforward networks, which now dominate computer vision, take further inspiration from the architecture of the primate visual hierarchy. However, the current models...

https://rxivist.org/papers/16568
https://doi.org/10.1101/029876

14: Deep Neural Networks In Computational Neuroscience

Tim Christian Kietzmann, Patrick McClure et al.

10,721 downloads (posted 04 May 2017)

The goal of computational neuroscience is to find mechanistic explanations of how the nervous system processes information to give rise to cognitive function and behaviour. At the heart of the field are its models, i.e. mathematical and computational descriptions of the system being studied, which map sensory stimuli to neural responses and/or neural to behavioural responses. These models range from simple to complex. Recently, deep neural networks (DNNs) have come to dominate several domains of artificial intelligence ...

https://rxivist.org/papers/12525
https://doi.org/10.1101/133504

15: Spontaneous behaviors drive multidimensional, brain-wide population activity

Carsen Stringer, Marius Pachitariu et al.

10,551 downloads (posted 22 Apr 2018)

Cortical responses to sensory stimuli are highly variable, and sensory cortex exhibits intricate spontaneous activity even without external sensory input. Cortical variability and spontaneous activity have been variously proposed to represent random noise, recall of prior experience, or encoding of ongoing behavioral and cognitive variables. Here, by recording over 10,000 neurons in mouse visual cortex, we show that spontaneous activity reliably encodes a high-dimensional latent state, which is partially related to the ...

https://rxivist.org/papers/13001
https://doi.org/10.1101/306019

16: A suite of transgenic driver and reporter mouse lines with enhanced brain cell type targeting and functionality

Tanya L Daigle, Linda Madisen et al.

9,635 downloads (posted 25 Nov 2017)

Modern genetic approaches are powerful in providing access to diverse types of neurons within the mammalian brain and greatly facilitating the study of their function. We here report a large set of driver and reporter transgenic mouse lines, including 23 new driver lines targeting a variety of cortical and subcortical cell populations and 26 new reporter lines expressing an array of molecular tools. In particular, we describe the TIGRE2.0 transgenic platform and introduce Cre-dependent reporter lines that enable optical...

https://rxivist.org/papers/14303
https://doi.org/10.1101/224881

17: Suite2p: beyond 10,000 neurons with standard two-photon microscopy

Marius Pachitariu, Carsen Stringer et al.

9,500 downloads (posted 30 Jun 2016)

Two-photon microscopy of calcium-dependent sensors has enabled unprecedented recordings from vast populations of neurons. While the sensors and microscopes have matured over several generations of development, computational methods to process the resulting movies remain inefficient and can give results that are hard to interpret. Here we introduce Suite2p: a fast, accurate and complete pipeline that registers raw movies, detects active cells, extracts their calcium traces and infers their spike times. Suite2p runs on st...

https://rxivist.org/papers/15156
https://doi.org/10.1101/061507

18: Bright and photostable chemigenetic indicators for extended in vivo voltage imaging

Ahmed S. Abdelfattah, Takashi Kawashima et al.

9,328 downloads (posted 06 Oct 2018)

Imaging changes in membrane potential using genetically encoded fluorescent voltage indicators (GEVIs) has great potential for monitoring neuronal activity with high spatial and temporal resolution. Brightness and photostability of fluorescent proteins and rhodopsins have limited the utility of existing GEVIs. We engineered a novel GEVI, Voltron, that utilizes bright and photostable synthetic dyes instead of protein-based fluorophores, extending the combined duration of imaging and number of neurons imaged simultaneousl...

https://rxivist.org/papers/34156
https://doi.org/10.1101/436840

19: Molecular architecture of the mouse nervous system

Amit Zeisel, Hannah Hochgerner et al.

9,280 downloads (posted 05 Apr 2018)

The mammalian nervous system executes complex behaviors controlled by specialised, precisely positioned and interacting cell types. Here, we used RNA sequencing of half a million single cells to create a detailed census of cell types in the mouse nervous system. We mapped cell types spatially and derived a hierarchical, data-driven taxonomy. Neurons were the most diverse, and were grouped by developmental anatomical units, and by the expression of neurotransmitters and neuropeptides. Neuronal diversity was driven by gen...

https://rxivist.org/papers/13168
https://doi.org/10.1101/294918

20: High-performance GFP-based calcium indicators for imaging activity in neuronal populations and microcompartments

Hod Dana, Yi Sun et al.

9,182 downloads (posted 03 Oct 2018)

Calcium imaging with genetically encoded calcium indicators (GECIs) is routinely used to measure neural activity in intact nervous systems. GECIs are frequently used in one of two different modes: to track activity in large populations of neuronal cell bodies, or to follow dynamics in subcellular compartments such as axons, dendrites and individual synaptic compartments. Despite major advances, calcium imaging is still limited by the biophysical properties of existing GECIs, including affinity, signal-to-noise ratio, ri...

https://rxivist.org/papers/33940
https://doi.org/10.1101/434589