The effect of noise correlations in populations of diversely tuned neurons

The amount of information encoded by networks of neurons critically depends on the correlation structure of their activity. Neurons with similar stimulus preferences tend to have higher noise correlations than others. In homogeneous populations of neurons this limited range correlation structure is highly detrimental to the accuracy of a population code. Therefore, reduced spike count correlations under attention, after adaptation or after learning have been interpreted as evidence for a more efficient population code. Here we analyze the role of limited range correlations in more realistic, heterogeneous population models. We use Fisher information and maximum likelihood decoding to show that reduced correlations do not necessarily improve encoding accuracy. In fact, in populations with more than a few hundred neurons, increasing the level of limited range correlations can substantially improve encoding accuracy. We found that this improvement results from a decrease in noise entropy that is associated with increasing correlations if the marginal distributions are unchanged. Surprisingly, for constant noise entropy and in the limit of large populations the encoding accuracy is independent of both structure and magnitude of noise correlations

Signatures of criticality arise in simple neural population models with correlations

Large-scale recordings of neuronal activity make it possible to gain insights into the collective activity of neural ensembles. It has been hypothesized that neural populations might be optimized to operate at a 'thermodynamic critical point', and that this property has implications for information processing. Support for this notion has come from a series of studies which identified statistical signatures of criticality in the ensemble activity of retinal ganglion cells. What are the underlying mechanisms that give rise to these observations? Here we show that signatures of criticality arise even in simple feed-forward models of retinal population activity. In particular, they occur whenever neural population data exhibits correlations, and is randomly sub-sampled during data analysis. These results show that signatures of criticality are not necessarily indicative of an optimized coding strategy, and challenge the utility of analysis approaches based on equilibrium thermodynamics for understanding partially observed biological systems.Comment: 36 pages, LaTeX; added journal reference on page 1, added link to code repositor

Développement d'un microscope STED à excitation deux photons et son application aux neurosciences

The advent of STED microscopy has created a lot of excitement in the field of neuroscience becausemany important neuronal structures, such as dendritic spines, axonal shafts or astroglial processes,cannot be properly resolved by regular light microscopy techniques. Two-photon fluorescence microscopy is a widely used imaging technique in neuroscience because it permits imaging dynamic events deep inside light-scattering brain tissue, providing high optical sectioning and depth penetration. However, the spatial resolution of this approach is limited to around half a micron, and hence is inadequate for revealing many morphological details of neurons and synapses. The aim of my PhD work was to A) develop a microscope that improves on two-photon imaging by combining it with STED microscopy and to B) demonstrate its potential for nanoscale imaging of dynamic neural processes in acute brain slices and in vivo. The new microscope achieves a lateral spatial resolution of ~50 nm at imaging depths of ~50 μm in living brain slices. It works with green fluorophores, including common fluorescent proteins like GFP and YFP, offering two-color contrast based on spectral detection and linear unmixing. Because of its upright design using a long working distance water-immersion objective, it was possible to incorporate electrophysiological techniques like patch-clamping or to add a stage for in vivo imaging. I have used the new microscope to image fine neural processes and their nanoscale dynamics in different experimental preparations and brain regions, revealing new and interesting morphological features of dendrites and spines. In addition, I have explored different labeling strategies to be able to use STED microscopy for visualizing protein trafficking and dynamics at the nanoscale in brain slices.L’avènement de la microscopie STED (Stimulated Emission Depletion) a bouleversé le domaine desneurosciences du au fait que beaucoup de structures neuronale, tels que les épines dendritiques, lesaxones ou les processus astrocytaires, ne peuvent pas être correctement résolu en microscopiephotonique classique. La microscopie 2-photon est une technique d’imagerie photonique très largement utilisée dans le domaine des neurosciences car elle permet d’imager les événements dynamique en profondeur dans le tissu cérébral, offrant un excellent sectionnement optique et une meilleure profondeur de pénétration. Cependant, la résolution spatiale de cette approche est limitée autour de 0.5 μm, la rendant inappropriée pour étudier les détails morphologiques des neurones et synapses. Le but de mon travail de thèse était à A) développer un microscope qui permet d'améliorer l'imagerie 2-photon en la combinant avec la microscopie STED et B) démontrer son potentiel pour l'imagerie à l'échelle nanométrique de processus neuronaux dynamiques dans des tranches de cerveau aigus et in vivo. Le nouveau microscope permet d'obtenir une résolution spatiale latérale de ~ 50 nm à des profondeurs d'imagerie de ~ 50 μm dans du tissu cérébral vivant. Il fonctionne avec des fluorophores verts, y compris les protéines fluorescentes communes telles que la GFP et YFP, offrant le contraste de deux couleurs basé sur la détection spectrale et linéaire ‘unmixing’. S’agissant d’un microscope droit, utilisant un objectif à immersion ayant une grande distance de travail, nous avons pu incorporer des techniques électrophysiologiques comme patch-clamp et ajouter une plateforme pour l'imagerie in vivo. J’ai utilise ce nouveau microscope pour imager des processus neuronaux fins et leur dynamique à l’échelle nanométrique dans différent types de préparations et des régions différentes du cerveau. J’ai pu révéler des nouvelles caractéristiques morphologique des dendrites et épines. En outre, j'ai exploré différentes stratégies de marquage pour pouvoir utiliser la microscopie STED pour imager le trafic des protéines et de leur dynamique à l'échelle nanométrique dans des tranches de cerveau

Statistical Analysis of Multi-Cell Recordings: Linking Population Coding Models to Experimental Data

Modern recording techniques such as multi-electrode arrays and two-photon imaging methods are capable of simultaneously monitoring the activity of large neuronal ensembles at single cell resolution. These methods finally give us the means to address some of the most crucial questions in systems neuroscience: what are the dynamics of neural population activity? How do populations of neurons perform computations? What is the functional organization of neural ensembles? While the wealth of new experimental data generated by these techniques provides exciting opportunities to test ideas about how neural ensembles operate, it also provides major challenges: multi-cell recordings necessarily yield data which is high-dimensional in nature. Understanding this kind of data requires powerful statistical techniques for capturing the structure of the neural population responses, as well as their relationship with external stimuli or behavioral observations. Furthermore, linking recorded neural population activity to the predictions of theoretical models of population coding has turned out not to be straightforward. These challenges motivated us to organize a workshop at the 2009 Computational Neuroscience Meeting in Berlin to discuss these issues. In order to collect some of the recent progress in this field, and to foster discussion on the most important directions and most pressing questions, we issued a call for papers for this Research Topic. We asked authors to address the following four questions: 1. What classes of statistical methods are most useful for modeling population activity? 2. What are the main limitations of current approaches, and what can be done to overcome them? 3. How can statistical methods be used to empirically test existing models of (probabilistic) population coding? 4. What role can statistical methods play in formulating novel hypotheses about the principles of information processing in neural populations? A total of 15 papers addressing questions related to these themes are now collected in this Research Topic. Three of these articles have resulted in “Focused reviews” in Frontiers in Neuroscience (Crumiller et al., 2011; Rosenbaum et al., 2011; Tchumatchenko et al., 2011), illustrating the great interest in the topic. Many of the articles are devoted to a better understanding of how correlations arise in neural circuits, and how they can be detected, modeled, and interpreted. For example, by modeling how pairwise correlations are transformed by spiking non-linearities in simple neural circuits, Tchumatchenko et al. (2010) show that pairwise correlation coefficients have to be interpreted with care, since their magnitude can depend strongly on the temporal statistics of their input-correlations. In a similar spirit, Rosenbaum et al. (2010) study how correlations can arise and accumulate in feed-forward circuits as a result of pooling of correlated inputs. Lyamzin et al. (2010) and Krumin et al. (2010) present methods for simulating correlated population activity and extend previous work to more general settings. The method of Lyamzin et al. (2010) allows one to generate synthetic spike trains which match commonly reported statistical properties, such as time varying firing rates as well signal and noise correlations. The Hawkes framework presented by Krumin et al. (2010) allows one to fit models of recurrent population activity to the correlation-structure of experimental data. Louis et al. (2010) present a novel method for generating surrogate spike trains which can be useful when trying to assess the significance and time-scale of correlations in neural spike trains. Finally, Pipa and Munk (2011) study spike synchronization in prefrontal cortex during working memory. A number of studies are also devoted to advancing our methodological toolkit for analyzing various aspects of population activity (Gerwinn et al., 2010; Machens, 2010; Staude et al., 2010; Yu et al., 2010). For example, Gerwinn et al. (2010) explain how full probabilistic inference can be performed in the popular model class of generalized linear models (GLMs), and study the effect of using prior distributions on the parameters of the stimulus and coupling filters. Staude et al. (2010) extend a method for detecting higher-order correlations between neurons via population spike counts to non-stationary settings. Yu et al. (2010) describe a new technique for estimating the information rate of a population of neurons using frequency-domain methods. Machens (2010) introduces a novel extension of principal component analysis for separating the variability of a neural response into different sources. Focusing less on the spike responses of neural populations but on aggregate signals of population activity, Boatman-Reich et al. (2010) and Hoerzer et al. (2010) describe methods for a quantitative analysis of field potential recordings. While Boatman-Reich et al. (2010) discuss a number of existing techniques in a unified framework and highlight the potential pitfalls associated with such approaches, Hoerzer et al. (2010) demonstrate how multivariate autoregressive models and the concept of Granger causality can be used to infer local functional connectivity in area V4 of behaving macaques. A final group of studies is devoted to understanding experimental data in light of computational models (Galán et al., 2010; Pandarinath et al., 2010; Shteingart et al., 2010). Pandarinath et al. (2010) present a novel mechanism that may explain how neural networks in the retina switch from one state to another by a change in gap junction coupling, and conjecture that this mechanism might also be found in other neural circuits. Galán et al. (2010) present a model of how hypoxia may change the network structure in the respiratory networks in the brainstem, and analyze neural correlations in multi-electrode recordings in light of this model. Finally, Shteingart et al. (2010) show that the spontaneous activation sequences they find in cultured networks cannot be explained by Zipf’s law, but rather require a wrestling model. The papers of this Research Topic thus span a wide range of topics in the statistical modeling of multi-cell recordings. Together with other recent advances, they provide us with a useful toolkit to tackle the challenges presented by the vast amount of data collected with modern recording techniques. The impact of novel statistical methods on the field and their potential to generate scientific progress, however, depends critically on how readily they can be adopted and applied by laboratories and researchers working with experimental data. An important step toward this goal is to also publish computer code along with the articles (Barnes, 2010) as a successful implementation of advanced methods also relies on many details which are hard to communicate in the article itself. In this way it becomes much more likely that other researchers can actually use the methods, and unnecessary re-implementations can be avoided. Some of the papers in this Research Topic already follow this goal (Gerwinn et al., 2010; Louis et al., 2010; Lyamzin et al., 2010). We hope that this practice becomes more and more common in the future and encourage authors and editors of Research Topics to make as much code available as possible, ideally in a format that can be easily integrated with existing software sharing initiatives (Herz et al., 2008; Goldberg et al., 2009)

Optimal Population Coding, Revisited

Cortical circuits perform the computations underlying rapid perceptual decisions within a few dozen milliseconds with each neuron emitting only a few spikes. Under these conditions, the theoretical analysis of neural population codes is challenging, as the most commonly used theoretical tool – Fisher information – can lead to erroneous conclusions about the optimality of different coding schemes. Here we revisit the effect of tuning function width and correlation structure on neural population codes based on ideal observer analysis in both a discrimination and reconstruction task. We show that the optimal tuning function width and the optimal correlation structure in both paradigms strongly depend on the available decoding time in a very similar way. In contrast, population codes optimized for Fisher information do not depend on decoding time and are severely suboptimal when only few spikes are available. In addition, we use the neurometric functions of the ideal observer in the classification task to investigate the differential coding properties of these Fisher-optimal codes for fine and coarse discrimination. We find that the discrimination error for these codes does not decrease to zero with increasing population size, even in simple coarse discrimination tasks. Our results suggest that quite different population codes may be optimal for rapid decoding in cortical computations than those inferred from the optimization of Fisher information

iDISCO+ for the Study of Neuroimmune Architecture of the Rat Auditory Brainstem

The lower stations of the auditory system display a complex anatomy. The inner ear labyrinth is composed of several interconnecting membranous structures encased in cavities of the temporal bone, and the cerebellopontine angle contains fragile structures such as meningeal folds, the choroid plexus (CP), and highly variable vascular formations. For this reason, most histological studies of the auditory system have either focused on the inner ear or the CNS by physically detaching the temporal bone from the brainstem. However, several studies of neuroimmune interactions have pinpointed the importance of structures such as meninges and CP; in the auditory system, an immune function has also been suggested for inner ear structures such as the endolymphatic duct (ED) and sac. All these structures are thin, fragile, and have complex 3D shapes. In order to study the immune cell populations located on these structures and their relevance to the inner ear and auditory brainstem in health and disease, we obtained a clarified-decalcified preparation of the rat hindbrain still attached to the intact temporal bone. This preparation may be immunolabeled using a clearing protocol (based on iDISCO+) to show location and functional state of immune cells. The observed macrophage distribution suggests the presence of CP-mediated communication pathways between the inner ear and the cochlear nuclei

Transport Coefficients from Large Deviation Functions

We describe a method for computing transport coefficients from the direct evaluation of large deviation function. This method is general, relying on only equilibrium fluctuations, and is statistically efficient, employing trajectory based importance sampling. Equilibrium fluctuations of molecular currents are characterized by their large deviation functions, which is a scaled cumulant generating function analogous to the free energy. A diffusion Monte Carlo algorithm is used to evaluate the large deviation functions, from which arbitrary transport coefficients are derivable. We find significant statistical improvement over traditional Green-Kubo based calculations. The systematic and statistical errors of this method are analyzed in the context of specific transport coefficient calculations, including the shear viscosity, interfacial friction coefficient, and thermal conductivity.Comment: 11 pages, 5 figure

Benchmarking spike rate inference in population calcium imaging

A fundamental challenge in calcium imaging has been to infer spike rates of neurons from the measured noisy fluorescence traces. We systematically evaluate different spike inference algorithms on a large benchmark dataset (>100,000 spikes) recorded from varying neural tissue (V1 and retina) using different calcium indicators (OGB-1 and GCaMP6). In addition, we introduce a new algorithm based on supervised learning in flexible probabilistic models and find that it performs better than other published techniques. Importantly, it outperforms other algorithms even when applied to entirely new datasets for which no simultaneously recorded data is available. Future data acquired in new experimental conditions can be used to further improve the spike prediction accuracy and generalization performance of the model. Finally, we show that comparing algorithms on artificial data is not informative about performance on real data, suggesting that benchmarking different methods with real-world datasets may greatly facilitate future algorithmic developments in neuroscience