The James Webb Space Telescope Mission

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit.Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figure

Tensor Analysis Reveals Distinct Population Structure that Parallels the Different Computational Roles of Areas M1 and V1.

Cortical firing rates frequently display elaborate and heterogeneous temporal structure. One often wishes to compute quantitative summaries of such structure-a basic example is the frequency spectrum-and compare with model-based predictions. The advent of large-scale population recordings affords the opportunity to do so in new ways, with the hope of distinguishing between potential explanations for why responses vary with time. We introduce a method that assesses a basic but previously unexplored form of population-level structure: when data contain responses across multiple neurons, conditions, and times, they are naturally expressed as a third-order tensor. We examined tensor structure for multiple datasets from primary visual cortex (V1) and primary motor cortex (M1). All V1 datasets were 'simplest' (there were relatively few degrees of freedom) along the neuron mode, while all M1 datasets were simplest along the condition mode. These differences could not be inferred from surface-level response features. Formal considerations suggest why tensor structure might differ across modes. For idealized linear models, structure is simplest across the neuron mode when responses reflect external variables, and simplest across the condition mode when responses reflect population dynamics. This same pattern was present for existing models that seek to explain motor cortex responses. Critically, only dynamical models displayed tensor structure that agreed with the empirical M1 data. These results illustrate that tensor structure is a basic feature of the data. For M1 the tensor structure was compatible with only a subset of existing models

Data from: Tensor analysis reveals distinct population structure that parallels the different computational roles of areas M1 and V1

Cortical firing rates frequently display elaborate and heterogeneous temporal structure. One often wishes to compute quantitative summaries of such structure—a basic example is the frequency spectrum—and compare with model-based predictions. The advent of large-scale population recordings affords the opportunity to do so in new ways, with the hope of distinguishing between potential explanations for why responses vary with time. We introduce a method that assesses a basic but previously unexplored form of population-level structure: when data contain responses across multiple neurons, conditions, and times, they are naturally expressed as a third-order tensor. We examined tensor structure for multiple datasets from primary visual cortex (V1) and primary motor cortex (M1). All V1 datasets were ‘simplest’ (there were relatively few degrees of freedom) along the neuron mode, while all M1 datasets were simplest along the condition mode. These differences could not be inferred from surface-level response features. Formal considerations suggest why tensor structure might differ across modes. For idealized linear models, structure is simplest across the neuron mode when responses reflect external variables, and simplest across the condition mode when responses reflect population dynamics. This same pattern was present for existing models that seek to explain motor cortex responses. Critically, only dynamical models displayed tensor structure that agreed with the empirical M1 data. These results illustrate that tensor structure is a basic feature of the data. For M1 the tensor structure was compatible with only a subset of existing models

Tensor data

MATLAB data structure containing all analyzed data

Illustration of the stimuli/task and neural responses for one V1 dataset and one M1 dataset.

<p>(<b>a</b>) Responses of four example neurons for a V1 dataset recorded via an implanted electrode array during presentation of movies of natural scenes. Each colored trace plots the trial-averaged firing rate for one condition (one of 25 movies). For visualization, traces are colored red to blue based on the firing rate early in the stimulus. (<b>b</b>) Responses of four example neurons for an M1 dataset recorded via two implanted electrode arrays during a delayed-reach task (monkey J). Example neurons were chosen to illustrate the variety of observed responses. Each colored trace plots the trial-averaged firing rate for one condition; <i>i</i>.<i>e</i>., one of 72 straight and curved reach trajectories. For visualization, traces are colored based on the firing rate during the delay period between target onset and the go cue. Insets show the reach trajectories (which are the same for each neuron) using the color-coding for that neuron. M1 responses were time-locked separately to the three key events: target onset, the go cue, and reach onset. For presentation, the resulting average traces were spliced together to create a continuous firing rate as a function of time. However, the analysis window included primarily movement-related activity. Gray boxes indicate the analysis windows (for V1, <i>T</i> = 91 time points spanning 910 ms; for M1, <i>T</i> = 71 time points spanning 710 ms). Horizontal bars: 200 ms; vertical bars: 20 spikes per second.</p

Preferred mode analysis of two control datasets.

<p>The preferred mode is not determined by surface-level features. (<b>a</b>) Analysis for the empirical V1 dataset from <b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005164#pcbi.1005164.g003" target="_blank">Fig 3C</a></b> and <b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005164#pcbi.1005164.g004" target="_blank">Fig 4A</a></b>. Shown are three example neurons (left panels) and reconstruction error versus timespan (right panel, reproduced from <b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005164#pcbi.1005164.g003" target="_blank">Fig 3C</a></b>). (<b>b</b>) Same as in <b>a</b> but the V1 dataset was intentionally manipulated to have structure that was simplest across conditions. (<b>c</b>) Analysis for the empirical M1 dataset from <b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005164#pcbi.1005164.g003" target="_blank">Fig 3E</a></b>. Shown are three example neurons (left panels) and reconstruction error versus timespan (right panel, reproduced from <b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005164#pcbi.1005164.g003" target="_blank">Fig 3E</a></b>). (<b>d</b>) Same as in <b>c</b> but the M1 dataset was intentionally manipulated to have structure that was simplest across conditions.</p

Reconstruction error as a function of the number of basis elements.

<p>Each panel plots the difference in reconstruction errors (reconstruction error using <i>k</i> basis-conditions minus reconstruction error using <i>k</i> basis-neurons). The full timespan is considered. Positive values indicate neuron-preferred structure while negative values indicate condition-preferred structure (colored backgrounds for reference). All values in each panel are normalized by a constant, chosen as the smaller of the two reconstruction errors (for the full timespan) plotted in corresponding panels of <b>Figs <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005164#pcbi.1005164.g004" target="_blank">4</a> </b>and <b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005164#pcbi.1005164.g006" target="_blank">6</a></b>. For most datasets we considered <i>k</i> from 1–20 (mode preference did not flip for higher <i>k</i> in any dataset). For datasets with fewer than 20 neurons (or muscles) values are plotted up to the maximum possible <i>k</i>: the number of neurons (or muscles) in the dataset.</p