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Towards the role of humanoid robots as squad mates in urban operations and other domains, we identified doors as a major area lacking capability development. In this paper, we focus on the ability of humanoid robots to navigate and deal with doors. Human-sized doors are ubiquitous in many environment domains and the humanoid form factor is uniquely suited to operate and traverse them. We present an architecture which incorporates GPU accelerated perception and a tree based interactive behavior coordination system with a whole body motion and walking controller. Our system is capable of performing door traversals on a variety of door types. It supports rapid authoring of behaviors for unseen door types and techniques to achieve re-usability of those authored behaviors. The behaviors are modelled using trees and feature logical reactivity and action sequences that can be executed with layered concurrency to increase speed. Primitive actions are built on top of our existing whole body controller which supports manipulation while walking. We include a perception system using both neural networks and classical computer vision for door mechanism detection outside of the lab environment. We present operator-robot interdependence analysis charts to explore how human cognition is combined with artificial intelligence to produce complex robot behavior. Finally, we present and discuss real robot performances of fast door traversals on our Nadia humanoid robot. Videos online at https://www.youtube.com/playlist?list=PLXuyT8w3JVgMPaB5nWNRNHtqzRK8i68dy., Comment: 15 pages, 23 figure, for submission to Elsevier RAS

Perceptions and actions, thoughts and memories result from coordinated activity in hundreds or even thousands of neurons in the brain. It is an old dream of the physics community to provide a statistical mechanics description for these and other emergent phenomena of life. These aspirations appear in a new light because of developments in our ability to measure the electrical activity of the brain, sampling thousands of individual neurons simultaneously over hours or days. We review the progress that has been made in bringing theory and experiment together, focusing on maximum entropy methods and a phenomenological renormalization group. These approaches have uncovered new, quantitatively reproducible collective behaviors in networks of real neurons, and provide examples of rich parameter--free predictions that agree in detail with experiment.

New experimental methods make it possible to measure the expression levels of many genes, simultaneously, in snapshots from thousands or even millions of individual cells. Current approaches to analyze these experiments involve clustering or low-dimensional projections. Here we use the principle of maximum entropy to obtain a probabilistic description that captures the observed presence or absence of mRNAs from hundreds of genes in cells from the mammalian brain. We construct the Ising model compatible with experimental means and pairwise correlations, and validate it by showing that it gives good predictions for higher-order statistics. We notice that the probability distribution of cell states has many local maxima. By labeling cell states according to the associated maximum, we obtain a cell classification that agrees well with previous results that use traditional clustering techniques. Our results provide quantitative descriptions of gene expression statistics and interpretable criteria for defining cell classes, supporting the hypothesis that cell classes emerge from the collective interaction of gene expression levels., Comment: 14 pages, 21 figures

The SNO+ detector operated initially as a water Cherenkov detector. The implementation of a sealed covergas system midway through water data taking resulted in a significant reduction in the activity of $^{222}$Rn daughters in the detector and allowed the lowest background to the solar electron scattering signal above 5 MeV achieved to date. This paper reports an updated SNO+ water phase $^8$B solar neutrino analysis with a total livetime of 282.4 days and an analysis threshold of 3.5 MeV. The $^8$B solar neutrino flux is found to be $\left(2.32^{+0.18}_{-0.17}\text{(stat.)}^{+0.07}_{-0.05}\text{(syst.)}\right)\times10^{6}$ cm$^{-2}$s$^{-1}$ assuming no neutrino oscillations, or $\left(5.36^{+0.41}_{-0.39}\text{(stat.)}^{+0.17}_{-0.16}\text{(syst.)} \right)\times10^{6}$ cm$^{-2}$s$^{-1}$ assuming standard neutrino oscillation parameters, in good agreement with both previous measurements and Standard Solar Model Calculations. The electron recoil spectrum is presented above 3.5 MeV.

The interaction between light and matter in condensed matter excitations and electromagnetic resonators serves as a rich playground for fundamental research and lies at the core of photonic and quantum technologies. Herein, we present comprehensive experimental and theoretical studies of the photon-mediated hybridization of magnons and phonons in the terahertz (THz) range. We demonstrate the intriguing concept of composite states formed by distinct electric and magnetic quasiparticles strongly coupled to the same optical cavity modes. Specifically, we explore magnons excited in a slab of an antiferromagnetic crystal and phonons excited in a distinct specimen of an insulating material. The crystal slabs form an optical cavity with Fabry-P\'erot oscillations in the THz range. We demonstrate hybridized phonon-magnon polariton modes and their tunability by adjusting the distance between the slabs, showing that hybridization persists even at separations up to several millimeters. The experimental results are interpreted using both classical and quantum electrodynamical models. The quantum description allows us to quantify the degree of hybridization that is linked to a topological behavior of the electric field phasor, in agreement with the classical electrodynamics expectations. Importantly, the presented results refer to temperature conditions and cavities of millimeter size, paving the way for engineering realistic, frequency-tunable THz devices through the hybridization of electric (phononics) and magnetic (spintronics) elementary excitations of matter.

After a machine learning model has been deployed into production, its predictive performance needs to be monitored. Ideally, such monitoring can be carried out by comparing the model's predictions against ground truth labels. For this to be possible, the ground truth labels must be available relatively soon after inference. However, there are many use cases where ground truth labels are available only after a significant delay, or in the worst case, not at all. In such cases, directly monitoring the model's predictive performance is impossible. Recently, novel methods for estimating the predictive performance of a model when ground truth is unavailable have been developed. Many of these methods leverage model confidence or other uncertainty estimates and are experimentally compared against a naive baseline method, namely Average Confidence (AC), which estimates model accuracy as the average of confidence scores for a given set of predictions. However, until now the theoretical properties of the AC method have not been properly explored. In this paper, we try to fill this gap by reviewing the AC method and show that under certain general assumptions, it is an unbiased and consistent estimator of model accuracy with many desirable properties. We also compare this baseline estimator against some more complex estimators empirically and show that in many cases the AC method is able to beat the others, although the comparative quality of the different estimators is heavily case-dependent.

The SNO+ collaboration reports its first spectral analysis of long-baseline reactor antineutrino oscillation using 114 tonne-years of data. Fitting the neutrino oscillation probability to the observed energy spectrum yields constraints on the neutrino mass-squared difference $\Delta m^2_{21}$. In the ranges allowed by previous measurements, the best-fit $\Delta m^2_{21}$ is (8.85$^{+1.10}_{-1.33}$) $\times$ 10$^{-5}$ eV$^2$. This measurement is continuing in the next phases of SNO+ and is expected to surpass the present global precision on $\Delta m^2_{21}$ with about three years of data.

We introduce a novel technique to mitigate the adverse effects of atmospheric turbulence on astronomical imaging. Utilizing a video-to-image neural network trained on simulated data, our method processes a sliding sequence of short-exposure ($\sim$0.2s) stellar field images to reconstruct an image devoid of both turbulence and noise. We demonstrate the method with simulated and observed stellar fields, and show that the brief exposure sequence allows the network to accurately associate speckles to their originating stars and effectively disentangle light from adjacent sources across a range of seeing conditions, all while preserving flux to a lower signal-to-noise ratio than an average stack. This approach results in a marked improvement in angular resolution without compromising the astrometric stability of the final image., Comment: Accepted for publication in MNRAS (advance copy available at https://doi.org/10.1093/mnras/stae1018). Project website available at https://dancecam.info/ . 20 pages, 17 figures

At the 2023 Les Houches Summer School on Theoretical Biological Physics, several students asked for some background on information theory, and so we added a tutorial to the scheduled lectures. This is largely a transcript of that tutorial, lightly edited. It covers basic definitions and context rather than detailed calculations. We hope to have maintained the informality of the presentation, including exchanges with the students, while still being useful., Comment: See also 2401.15538

Theoretical physicists have been fascinated by the phenomena of life for more than a century. As we engage with more realistic descriptions of living systems, however, things get complicated. After reviewing different reactions to this complexity, I explore the optimization of information flow as a potentially general theoretical principle. The primary example is a genetic network guiding development of the fly embryo, but each idea also is illustrated by examples from neural systems. In each case, optimization makes detailed, largely parameter-free predictions that connect quantitatively with experiment, Comment: Lectures at the 2023 Les Houches Summer School, Theoretical Biophysics. Revisions include some replacement figures with permissions, small clarifications in the text

Machine learning models often experience performance degradation post-deployment due to shifts in data distribution. It is challenging to assess model's performance accurately when labels are missing or delayed. Existing proxy methods, such as drift detection, fail to measure the effects of these shifts adequately. To address this, we introduce a new method, Probabilistic Adaptive Performance Estimation (PAPE), for evaluating classification models on unlabeled data that accurately quantifies the impact of covariate shift on model performance. It is model and data-type agnostic and works for various performance metrics. Crucially, PAPE operates independently of the original model, relying only on its predictions and probability estimates, and does not need any assumptions about the nature of the covariate shift, learning directly from data instead. We tested PAPE on tabular data using over 900 dataset-model combinations created from US census data, assessing its performance against multiple benchmarks. Overall, PAPE provided more accurate performance estimates than other evaluated methodologies., Comment: 9 content pages, 3 figures

The body plan of the fruit fly is determined by the expression of just a handful of genes. We show that the spatial patterns of expression for several of these genes scale precisely with the size of the embryo. Concretely, discrete positional markers such as the peaks in striped patterns have absolute positions along the anterior-posterior axis that are proportional to embryo length, with better than 1% accuracy. Further, the information (in bits) that graded patterns of expression provide about position can be decomposed into information about fractional or scaled position and information about absolute position or embryo length; all of the available information is about scaled position, again with ~1% accuracy. These observations suggest that the underlying genetic network exhibits scale invariance in a deeper mathematical sense. Taking this mathematical statement seriously requires that the network dynamics have a zero mode, which connects to many other observations on this system.

In the brain, fine-scale correlations combine to produce macroscopic patterns of activity. However, as experiments record from larger and larger populations, we approach a fundamental bottleneck: the number of correlations one would like to include in a model grows larger than the available data. In this undersampled regime, one must focus on a sparse subset of correlations; the optimal choice contains the maximum information about patterns of activity or, equivalently, minimizes the entropy of the inferred maximum entropy model. Applying this ``minimax entropy" principle is generally intractable, but here we present an exact and scalable solution for pairwise correlations that combine to form a tree (a network without loops). Applying our method to over one thousand neurons in the mouse hippocampus, we find that the optimal tree of correlations reduces our uncertainty about the population activity by 14% (over 50 times more than a random tree). Despite containing only 0.1% of all pairwise correlations, this minimax entropy model accurately predicts the observed large-scale synchrony in neural activity and becomes even more accurate as the population grows. The inferred Ising model is almost entirely ferromagnetic (with positive interactions) and exhibits signatures of thermodynamic criticality. These results suggest that a sparse backbone of excitatory interactions may play an important role in driving collective neuronal activity., Comment: 15 pages, 11 figures

In a developing embryo, information about the position of cells is encoded in the concentrations of "morphogen" molecules. In the fruit fly, the local concentrations of just a handful of proteins encoded by the gap genes are sufficient to specify position with a precision comparable to the spacing between cells along the anterior--posterior axis. This matches the precision of downstream events such as the striped patterns of expression in the pair-rule genes, but is not quite sufficient to define unique identities for individual cells. We demonstrate theoretically that this information gap can be bridged if positional errors are spatially correlated, with relatively long correlation lengths. We then show experimentally that these correlations are present, with the required strength, in the fluctuating positions of the pair-rule stripes, and this can be traced back to the gap genes. Taking account of these correlations, the available information matches the information needed for unique cellular specification, within error bars of ~2%. These observation support a precisionist view of information flow through the underlying genetic networks, in which accurate signals are available from the start and preserved as they are transformed into the final spatial patterns.

Maximum entropy methods provide a principled path connecting measurements of neural activity directly to statistical physics models, and this approach has been successful for populations of $N\sim 100$ neurons. As $N$ increases in new experiments, we enter an undersampled regime where we have to choose which observables should be constrained in the maximum entropy construction. The best choice is the one that provides the greatest reduction in entropy, defining a "minimax entropy" principle. This principle becomes tractable if we restrict attention to correlations among pairs of neurons that link together into a tree; we can find the best tree efficiently, and the underlying statistical physics models are exactly solved. We use this approach to analyze experiments on $N\sim 1500$ neurons in the mouse hippocampus, and show that the resulting model captures the distribution of synchronous activity in the network., Comment: 6 pages, 5 figures

Magneto-spectroscopy methods have been employed to study the zero-wavevector magnon excitations in MnPSe$_3$. Experiments carried out as a function of temperature and the applied magnetic field show that two low-energy magnon branches of MnPSe$_3$ in its antiferromagnetic phase are gapped. The observation of two low-energy magnon gaps (at 14 and 0.7 cm$^{-1}$) implies that MnPSe$_3$ is a biaxial antiferromagnet. A relatively strong out-of-plane anisotropy imposes the spin alignment to be in-plane whereas the spin directionality within the plane is governed by a factor of 2.5 $\times$ 10$^{-3}$ weaker in-plane anisotropy., Comment: 9 pages, 3 figures

The direction of individual $^8$B solar neutrinos has been reconstructed using the SNO+ liquid scintillator detector. Prompt, directional Cherenkov light was separated from the slower, isotropic scintillation light using time information, and a maximum likelihood method was used to reconstruct the direction of individual scattered electrons. A clear directional signal was observed, correlated with the solar angle. The observation was aided by a period of low primary fluor concentration that resulted in a slower scintillator decay time. This is the first time that event-by-event direction reconstruction in high light-yield liquid scintillator has been demonstrated in a large-scale detector., Comment: 6 pages, 6 figures. Accepted manuscript by PRD

Keith Brueckner was a theoretical physicist of considerable technical power who came of age as the mysteries of the atomic nucleus were coming into focus. His fundamental contributions to the "many-body problem" had a lasting impact on our understanding of how the macroscopic behavior of matter emerges from the underlying microscopic rules. A passionate and accomplished mountain climber, he listed the American Alpine Club below the National Academy of Sciences on his vitae. During decades of complex interactions between the physics community and the United States government, he helped build structures that allowed him and many others to provide advice on classified matters, but also actively raised funds to support opposition to the war in Vietnam. At the peak of his career, he left the Ivy League to help found and build a new university in a small village filled with Marines and retirees - La Jolla, California.

Humanoid robots have the potential to perform useful tasks in a world built for humans. However, communicating intention and teaming with a humanoid robot is a multi-faceted and complex problem. In this paper, we tackle the problems associated with quickly and interactively authoring new robot behavior that works on real hardware. We bring the powerful concepts of Affordance Templates and Coactive Design methodology to this problem to attempt to solve and explain it. In our approach we use interactive stance and hand pose goals along with other types of actions to author humanoid robot behavior on the fly. We then describe how our operator interface works to author behaviors on the fly and provide interdependence analysis charts for task approach and door opening. We present timings from real robot performances for traversing a push door and doing a pick and place task on our Nadia humanoid robot., Comment: 8 pages, 12 figures, for Humanoids 2023

A relatively new concern for the forthcoming massive spectroscopic sky surveys is the impact of contamination from low earth orbit satellites. Several hundred thousand of these satellites are licensed for launch in the next few years and it has been estimated that, in some cases, up to a few percent of spectra could be contaminated when using wide field, multi-fiber spectrographs. In this paper, a multi-staged approach is used to assess the practicality and limitations of identifying and minimizing the impact of satellite contamination in a WEAVE-like stellar spectral survey. We develop a series of convolutional-network based architectures to attempt identification, stellar parameter and chemical abundances recovery, and source separation of stellar spectra that we artificially contaminate with satellite (i.e. solar-like) spectra. Our results show that we are able to flag 67% of all contaminated sources at a precision level of 80% for low-resolution spectra and 96% for high-resolution spectra. Additionally, we are able to remove the contamination from the spectra and recover the clean spectra with a $<$1% reconstruction error. The errors in stellar parameter predictions reduce by up to a factor of 2-3 when either including contamination as an augmentation to a training set or by removing the contamination from the spectra, with overall better performance in the former case. The presented methods illustrate several machine learning mitigation strategies that can be implemented to improve stellar parameters for contaminated spectra in the WEAVE stellar spectroscopic survey and others like it., Comment: 15 pages. To be published in MNRAS

The explosion of data on animal behavior in more natural contexts highlights the fact that these behaviors exhibit correlations across many time scales. But there are major challenges in analyzing these data: records of behavior in single animals have fewer independent samples than one might expect; in pooling data from multiple animals, individual differences can mimic long-ranged temporal correlations; conversely long-ranged correlations can lead to an over-estimate of individual differences. We suggest an analysis scheme that addresses these problems directly, apply this approach to data on the spontaneous behavior of walking flies, and find evidence for scale invariant correlations over nearly three decades in time, from seconds to one hour. Three different measures of correlation are consistent with a single underlying scaling field of dimension $\Delta = 0.180\pm 0.005$.

Many biological systems approach physical limits to their performance, motivating the idea that their behavior and underlying mechanisms could be determined by such optimality. Nevertheless, optimization as a predictive principle has only been applied in very simplified setups. Here, in contrast, we explore a mechanistically-detailed class of models for the gap gene network of the Drosophila embryo, and determine its 50+ parameters by optimizing the information that gene expression levels convey about nuclear positions, subject to physical constraints on the number of available molecules. Optimal networks recapitulate the architecture and spatial gene expression profiles of the real organism. Our framework makes precise the many tradeoffs involved in maximizing functional performance, and allows us to explore alternative networks to address the questions of necessity vs contingency. Multiple solutions to the optimization problem may be realized in closely related organisms., Comment: Main text: 13 pages, Supplementary Info: 46 pages, 10 figures

Information of relevance to the organism often is represented by the concentrations of particular molecules inside a cell. As outside observers we can now measure these concentrations precisely, but the cell's own mechanisms must be noisier. We show that, in certain limits, there is a universal tradeoff between the information capacity of these concentration measurements and the amount of relevant information that is captured, a version of the information bottleneck problem. This universal tradeoff is confirmed, quantitatively, in an analysis of the positional information encoded by the gap genes in the developing fly embryo.

Biological and artificial neural networks routinely make reliable distinctions between similar inputs, and the rules for making these distinctions are learned. In some ways, self/nonself discrimination in the immune system is similar, being both reliable and (partly) learned through thymic selection. In contrast to other examples, we show that the distributions of self and nonself peptides are nearly identical but strongly inhomogeneous. Reliable discrimination is possible only because self peptides are a particular finite sample drawn out of this distribution, and the immune system can target the ``spaces'' in between these samples. In conventional learning problems, this would constitute overfitting and lead to disaster. Here, the strong inhomogeneities imply instead that the immune system gains by targeting peptides which are very similar to self, with maximum sensitivity for sequences just one substitution away. This prediction from the structure of the underlying distribution in sequence space agrees, for example, with the observed responses to cancer neoantigens.

Coupling of space-separated oscillators is interesting for quantum and communication technologies. In this work, it is shown that two antiferromagnetic oscillators placed inside an electromagnetic cavity couple cooperatively to its terahertz modes and, in effect, hybridized magnon-polariton modes are formed. This is supported by a systematic study of reflection spectra from two parallel-plane slabs of hematite ($\alpha$-Fe$_2$O$_3$), measured as a function of their temperatures and separation distance, and modeled theoretically. The mediating cavity was formed by the crystals themselves and the experiment was performed in a practical distance range of a few millimetres and above room temperature. Cavity-mediated coupling allows for engineering of complex resonators controlled by their geometry and by sharing properties of their components.

The SNO+ Collaboration reports the first evidence of reactor antineutrinos in a Cherenkov detector. The nearest nuclear reactors are located 240~km away in Ontario, Canada. This analysis uses events with energies lower than in any previous analysis with a large water Cherenkov detector. Two analytical methods are used to distinguish reactor antineutrinos from background events in 190 days of data and yield consistent evidence for antineutrinos with a combined significance of 3.5$\sigma$., Comment: v2: add missing author, add link to supplemental material v3: minor updates to match PRL publication

Machine-learning based classifiers have become indispensable in the field of astrophysics, allowing separation of astronomical sources into various classes, with computational efficiency suitable for application to the enormous data volumes that wide-area surveys now typically produce. In the standard supervised classification paradigm, a model is typically trained and validated using data from relatively small areas of sky, before being used to classify sources in other areas of the sky. However, population shifts between the training examples and the sources to be classified can lead to `silent' degradation in model performance, which can be challenging to identify when the ground-truth is not available. In this Letter, we present a novel methodology using the NannyML Confidence-Based Performance Estimation (CBPE) method to predict classifier F1-score in the presence of population shifts, but without ground-truth labels. We apply CBPE to the selection of quasars with decision-tree ensemble models, using broad-band photometry, and show that the F1-scores are predicted remarkably well (MAPE ~ 10%; R^2 = 0.74-0.92). We discuss potential use-cases in the domain of astronomy, including machine-learning model and/or hyperparameter selection, and evaluation of the suitability of training datasets for a particular classification problem., Comment: 5.5 pages, 2 figures, accepted for publication in MNRAS Letters

Hematite ($\alpha$-Fe$_2$O$_3$) is an antiferromagnetic material with a very low spin damping and high N\'eel temperature. The temperature dependence of the antiferromagnetic resonance in a bulk single crystal of hematite was characterized from room temperature up to the N\'eel temperature in the frequency range of 0.19-0.5 THz. From these data, the N\'eel temperature was estimated as 966 K.

This paper reports results from a search for single and multi-nucleon disappearance from the $^{16}$O nucleus in water within the \snoplus{} detector using all of the available data. These so-called "invisible" decays do not directly deposit energy within the detector but are instead detected through their subsequent nuclear de-excitation and gamma-ray emission. New limits are given for the partial lifetimes: $\tau(n\rightarrow inv) > 9.0\times10^{29}$ years, $\tau(p\rightarrow inv) > 9.6\times10^{29}$ years, $\tau(nn\rightarrow inv) > 1.5\times10^{28}$ years, $\tau(np\rightarrow inv) > 6.0\times10^{28}$ years, and $\tau(pp\rightarrow inv) > 1.1\times10^{29}$ years at 90\% Bayesian credibility level (with a prior uniform in rate). All but the ($nn\rightarrow inv$) results improve on existing limits by a factor of about 3.

Living systems are fundamentally irreversible, breaking detailed balance and establishing an arrow of time. But how does the evident arrow of time for a whole system arise from the interactions among its multiple elements? We show that the local evidence for the arrow of time, which is the entropy production for thermodynamic systems, can be decomposed. First, it can be split into two components: an independent term reflecting the dynamics of individual elements and an interaction term driven by the dependencies among elements. Adapting tools from non--equilibrium physics, we further decompose the interaction term into contributions from pairs of elements, triplets, and higher--order terms. We illustrate our methods on models of cellular sensing and logical computations, as well as on patterns of neural activity in the retina as it responds to visual inputs. We find that neural activity can define the arrow of time even when the visual inputs do not, and that the dominant contribution to this breaking of detailed balance comes from interactions among pairs of neurons., Comment: 16 pages, 10 figures

Distribution systems will require new cost-effective solutions to provide network capacity and increased flexibility to accommodate Low Carbon Technologies. To address this need, we propose the Hybrid Multi-Terminal Soft Open Point (Hybrid MT-SOP) to efficiently provide distribution system interconnection capacity. Each leg of the Hybrid MT-SOP has an AC/DC converter connected in series with a bank of AC switches (Feeder Selector Switches) to allow the converter to connect to any of the feeders at a node. Asymmetric converter sizing is shown to increase feasible power transfers by up to 50% in the three-terminal case, whilst a conic mixed-integer program is formulated to optimally select the device configuration and power transfers. A case study shows the Hybrid MT-SOP increasing utilization of the converters by more than one third, with a 13% increase in system loss reduction as compared to an equally-sized MT-SOP., Comment: Preprint submitted to Electric Power Systems Research (Power Systems Computation Conference 2022)

Biological networks exhibit complex, coordinated patterns of activity. Can these patterns be captured precisely in simple models? Here we use measurements of simultaneous activity in 1000+ neurons in the mouse brain to test the validity of models grounded in statistical physics. When cells are dense samples from a small region, we find extremely detailed quantitative agreement between theory and experiment; sparse samples from larger regions lead to model failures. These results show we can aspire to more than qualitative agreement between simplifying theoretical ideas and the detailed behavior of a complex biological system.

We show that the evidence for a local arrow of time, which is equivalent to the entropy production in thermodynamic systems, can be decomposed. In a system with many degrees of freedom, there is a term that arises from the irreversible dynamics of the individual variables, and then a series of non--negative terms contributed by correlations among pairs, triplets, and higher--order combinations of variables. We illustrate this decomposition on simple models of noisy logical computations, and then apply it to the analysis of patterns of neural activity in the retina as it responds to complex dynamic visual scenes. We find that neural activity breaks detailed balance even when the visual inputs do not, and that this irreversibility arises primarily from interactions between pairs of neurons., Comment: 6 pages, 3 figures

Patterns of activity in networks of neurons are a prototypical complex system. Here we analyze data on the retina to show that information shared between a single neuron and the rest of the network is compressible, through a combination of the information bottleneck and an iteration scheme inspired by the renormalization group. The result is that the number of parameters needed to describe the distribution of joint activity scales with the square of the number of neurons, even though the interactions are not well approximated as pairwise. Our results also show that the shared information is essentially equal to the information that individual neurons carry about natural visual inputs, which has implications for the structure of the neural code.