Statistical Physics and Representations in Real and Artificial Neural Networks

This document presents the material of two lectures on statistical physics and neural representations, delivered by one of us (R.M.) at the Fundamental Problems in Statistical Physics XIV summer school in July 2017. In a first part, we consider the neural representations of space (maps) in the hippocampus. We introduce an extension of the Hopfield model, able to store multiple spatial maps as continuous, finite-dimensional attractors. The phase diagram and dynamical properties of the model are analyzed. We then show how spatial representations can be dynamically decoded using an effective Ising model capturing the correlation structure in the neural data, and compare applications to data obtained from hippocampal multi-electrode recordings and by (sub)sampling our attractor model. In a second part, we focus on the problem of learning data representations in machine learning, in particular with artificial neural networks. We start by introducing data representations through some illustrations. We then analyze two important algorithms, Principal Component Analysis and Restricted Boltzmann Machines, with tools from statistical physics

Caratterizzazione della misura di entropia di singolo nodo nell'ambito della teoria statistica dei network

In questo lavoro si è affrontata la definizione e la caratterizzazione di una misura di entropia di singolo nodo nell’ambito della teoria statistica dei network, per ottenere informazioni a livello di singolo nodo a fini di analisi e classificazione. Sono state introdotte e studiate alcune proprietà di questi osservabili in quanto la Network Entropy, precedentemente definita e utilizzata nello stesso contesto, fornisce un’informazione globale a livello dell’intero network. I risultati delle analisi svolte con questa definizione sono stati confrontati con una seconda definizione di entropia di singolo nodo proveniente dalla letteratura, applicando entrambe le misure allo stesso problema di caratterizzazione di due classi di nodi all’interno di un networ

Inférence et modélisation de réseaux biologiques par la physique statistique : des attracteurs neuronaux au paysage de fitness des protéines

The recent advent of high-throughput experimental procedures has opened a new era for the quantitative study of biological systems. Today, electrophysiology recordings and calcium imaging allow for the in vivo simultaneous recording of hundreds to thousands of neurons. In parallel, thanks to automated sequencing procedures, the libraries of known functional proteins expanded from thousands to millions in just a few years. This current abundance of biological data opens a new series of challenges for theoreticians. Accurate and transparent analysis methods are needed to process this massive amount of raw data into meaningful observables. Concurrently, the simultaneous observation of a large number of interacting units enables the development and validation of theoretical models aimed at the mechanistic understanding of the collective behavior of biological systems. In this manuscript, we propose an approach to both these challenges based on methods and models from statistical physics. We present an application of these methods to problems from neuroscience and bioinformatics, focusing on (1) the spatial memory and navigation task in the hippocampal loop and (2) the reconstruction of the fitness landscape of proteins from homologous sequence data.L'avènement récent des procédures expérimentales à haut débit a ouvert une nouvelle ère pour l'étude quantitative des systèmes biologiques. De nos jours, les enregistrements d'électrophysiologie et l'imagerie du calcium permettent l'enregistrement simultané in vivo de centaines à des milliers de neurones. Parallèlement, grâce à des procédures de séquençage automatisées, les bibliothèques de protéines fonctionnelles connues ont été étendues de milliers à des millions en quelques années seulement. L'abondance actuelle de données biologiques ouvre une nouvelle série de défis aux théoriciens. Des méthodes d’analyse précises et transparentes sont nécessaires pour traiter cette quantité massive de données brutes en observables significatifs. Parallèlement, l'observation simultanée d'un grand nombre d'unités en interaction permet de développer et de valider des modèles théoriques visant à la compréhension mécanistique du comportement collectif des systèmes biologiques. Dans ce manuscrit, nous proposons une approche de ces défis basée sur des méthodes et des modèles issus de la physique statistique, en développent et appliquant ces méthodes au problèmes issu de la neuroscience et de la bio-informatique : l’étude de la mémoire spatiale dans le réseau hippocampique, et la reconstruction du paysage adaptatif local d'une protéine

CS3 2021- Cloud Storage Synchronization and Sharing

In the scientific community, we have - at the same time - a strong need to **seamlessly store and share files** with colleagues all around the world, and a major constraint of **data sovereignty**. As many publicly-funded institutions are not allowed to use commercial cloud storage products - as they are run and hosted by foreign companies obeying their own local laws - a common solution is to host a **private cloud infrastructure** within their own premises. However, not every institution can afford the heavy hardware and connectivity infrastructure, along with the required IT workforce for maintenance, that is needed to run efficiently a private cloud solution. In this talk, we present Cubbit Hive: an innovative approach that **decouples the storage service from the need for dedicated infrastructure**, virtualizing a private cloud on a pre-existing network of connected devices. Cubbit Hive intelligently **collects spare storage and computing resources** within the premises of the institution (workstations, small servers, etc.) to enable a distributed storage service that is **encrypted**, **fast**, and **compliant-by-design** with the strongest needs of security and sovereignty

Integration and multiplexing of positional and contextual information by the hippocampal network

International audienceThe hippocampus is known to store cognitive representations, or maps, that encode both positional and contextual information, critical for episodic memories and functional behavior. How path integration and contextual cues are dynamically combined and processed by the hippocampus to maintain these representations accurate over time remains unclear. To answer this question, we propose a two-way data analysis and modeling approach to CA3 multi-electrode recordings of a moving rat submitted to rapid changes of contextual (light) cues, triggering back-and-forth instabitilies between two cognitive representations (“teleportation” experiment of Jezek et al). We develop a dual neural activity decoder, capable of independently identifying the recalled cognitive map at high temporal resolution (comparable to theta cycle) and the position of the rodent given a map. Remarkably, position can be reconstructed at any time with an accuracy comparable to fixed-context periods, even during highly unstable periods. These findings provide evidence for the capability of the hippocampal neural activity to maintain an accurate encoding of spatial and contextual variables, while one of these variables undergoes rapid changes independently of the other. To explain this result we introduce an attractor neural network model for the hippocampal activity that process inputs from external cues and the path integrator. Our model allows us to make predictions on the frequency of the cognitive map instability, its duration, and the detailed nature of the place-cell population activity, which are validated by a further analysis of the data. Our work therefore sheds light on the mechanisms by which the hippocampal network achieves and updates multi-dimensional neural representations from various input streams

The hippocampus as a perceptual map: neuronal and behavioral discrimination during memory encoding

Posté sur BioRxiv le 8 décembre 2019The hippocampus is thought to encode similar events as distinct memory representations that are used for behavioral decisions. Where and how this “pattern separation” function is accomplished in the hippocampal circuit, and how it relates to behavior, is still unclear. Here we perform in vivo 2-photon Ca 2+ imaging from hippocampal subregions of head-fixed mice performing a virtual-reality spatial discrimination task. We find that population activity in the input region of the hippocampus, the dentate gyrus, robustly discriminates small changes in environments, whereas spatial discrimination in CA1 reflects the behavioral performance of the animals and depends on the degree of differences between environments. Our results demonstrate that the dentate gyrus amplifies small differences in its inputs, while downstream hippocampal circuits will act as the final arbiter on this decorrelated information, thereby producing a “perceptual map” that will guide behaviour

Differential Relation between Neuronal and Behavioral Discrimination during Hippocampal Memory Encoding

International audienceHow are distinct memories formed and used for behavior? To relate neuronal and behavioral discrimination during memory formation, we use in vivo 2-photon Ca 2+ imaging and whole-cell recordings from hippocampal subregions in head-fixed mice performing a spatial virtual-reality task. We find that both subthreshold activity as well as population codes of dentate gyrus neurons robustly discriminate across different spatial environments, while neuronal remapping in CA1 depends on the degree of difference between visual cues. Moreover, neuronal discrimination in CA1, but not in the dentate gyrus, reflects behavioral performance. Our results suggest that CA1 weights the decorrelated information from the dentate gyrus according to its relevance, thereby producing a map of memory representations that can be used by downstream circuits to guide learning and behavior

A synaptic signal for novelty processing in the hippocampus

International audienceAbstract Episodic memory formation and recall are complementary processes that rely on opposing neuronal computations in the hippocampus. How this conflict is resolved in hippocampal circuits is unclear. To address this question, we obtained in vivo whole-cell patch-clamp recordings from dentate gyrus granule cells in head-fixed mice trained to explore and distinguish between familiar and novel virtual environments. We find that granule cells consistently show a small transient depolarisation upon transition to a novel environment. This synaptic novelty signal is sensitive to local application of atropine, indicating that it depends on metabotropic acetylcholine receptors. A computational model suggests that the synaptic response to novelty may bias granule cell population activity, which can drive downstream attractor networks to a new state, favouring the switch from recall to new memory formation when faced with novelty. Such a novelty-driven switch may enable flexible encoding of new memories while preserving stable retrieval of familiar ones