Role of homeostasis in learning sparse representations

Neurons in the input layer of primary visual cortex in primates develop edge-like receptive fields. One approach to understanding the emergence of this response is to state that neural activity has to efficiently represent sensory data with respect to the statistics of natural scenes. Furthermore, it is believed that such an efficient coding is achieved using a competition across neurons so as to generate a sparse representation, that is, where a relatively small number of neurons are simultaneously active. Indeed, different models of sparse coding, coupled with Hebbian learning and homeostasis, have been proposed that successfully match the observed emergent response. However, the specific role of homeostasis in learning such sparse representations is still largely unknown. By quantitatively assessing the efficiency of the neural representation during learning, we derive a cooperative homeostasis mechanism that optimally tunes the competition between neurons within the sparse coding algorithm. We apply this homeostasis while learning small patches taken from natural images and compare its efficiency with state-of-the-art algorithms. Results show that while different sparse coding algorithms give similar coding results, the homeostasis provides an optimal balance for the representation of natural images within the population of neurons. Competition in sparse coding is optimized when it is fair. By contributing to optimizing statistical competition across neurons, homeostasis is crucial in providing a more efficient solution to the emergence of independent components

Motion clouds: model-based stimulus synthesis of natural-like random textures for the study of motion perception

Choosing an appropriate set of stimuli is essential to characterize the response of a sensory system to a particular functional dimension, such as the eye movement following the motion of a visual scene. Here, we describe a framework to generate random texture movies with controlled information content, i.e., Motion Clouds. These stimuli are defined using a generative model that is based on controlled experimental parametrization. We show that Motion Clouds correspond to dense mixing of localized moving gratings with random positions. Their global envelope is similar to natural-like stimulation with an approximate full-field translation corresponding to a retinal slip. We describe the construction of these stimuli mathematically and propose an open-source Python-based implementation. Examples of the use of this framework are shown. We also propose extensions to other modalities such as color vision, touch, and audition

Infection‐driven activation of transglutaminase 2 boosts glucose uptake and hexosamine biosynthesis in epithelial cells

DATA AVAILABILITYThe mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD017117.International audienceTransglutaminase 2 (TG2) is a ubiquitously expressed enzyme with transamidating activity. We report here that both expression and activity of TG2 are enhanced in mammalian epithelial cells infected with the obligate intracellular bacteria Chlamydia trachomatis. Genetic or pharmacological inhibition of TG2 impairs bacterial development. We show that TG2 increases glucose import by up-regulating the transcription of the glucose transporter genes GLUT-1 and GLUT-3. Furthermore, TG2 activation drives one specific glucose-dependent pathway in the host, i.e., hexosamine biosynthesis. Mechanistically, we identify the glucosamine:fructose-6-phosphate amidotransferase (GFPT) among the substrates of TG2. GFPT modification by TG2 increases its enzymatic activity, resulting in higher levels of UDP-N-acetylglucosamine biosynthesis and protein O-GlcNAcylation. The correlation between TG2 transamidating activity and O-GlcNAcylation is disrupted in infected cells because host hexosamine biosynthesis is being exploited by the bacteria, in particular to assist their division. In conclusion, our work establishes TG2 as a key player in controlling glucose-derived metabolic pathways in mammalian cells, themselves hijacked by C. trachomatis to sustain their own metabolic needs

A view of Neural Networks as dynamical systems

We consider neural networks from the point of view of dynamical systems theory. In this spirit we review recent results dealing with the following questions, adressed in the context of specific models. 1. Characterizing the collective dynamics; 2. Statistical analysis of spikes trains; 3. Interplay between dynamics and network structure; 4. Effects of synaptic plasticity.Comment: Review paper, 51 pages, 10 figures. submitte

Histone Methylation by NUE, a Novel Nuclear Effector of the Intracellular Pathogen Chlamydia trachomatis

Sequence analysis of the genome of the strict intracellular pathogen Chlamydia trachomatis revealed the presence of a SET domain containing protein, proteins that primarily function as histone methyltransferases. In these studies, we demonstrated secretion of this protein via a type III secretion mechanism. During infection, the protein is translocated to the host cell nucleus and associates with chromatin. We therefore named the protein nuclear effector (NUE). Expression of NUE in mammalian cells by transfection reconstituted nuclear targeting and chromatin association. In vitro methylation assays confirmed NUE is a histone methyltransferase that targets histones H2B, H3 and H4 and itself (automethylation). Mutants deficient in automethylation demonstrated diminished activity towards histones suggesting automethylation functions to enhance enzymatic activity. Thus, NUE is secreted by Chlamydia, translocates to the host cell nucleus and has enzymatic activity towards eukaryotic substrates. This work is the first description of a bacterial effector that directly targets mammalian histones

SNARE Protein Mimicry by an Intracellular Bacterium

Many intracellular pathogens rely on host cell membrane compartments for their survival. The strategies they have developed to subvert intracellular trafficking are often unknown, and SNARE proteins, which are essential for membrane fusion, are possible targets. The obligate intracellular bacteria Chlamydia replicate within an intracellular vacuole, termed an inclusion. A large family of bacterial proteins is inserted in the inclusion membrane, and the role of these inclusion proteins is mostly unknown. Here we identify SNARE-like motifs in the inclusion protein IncA, which are conserved among most Chlamydia species. We show that IncA can bind directly to several host SNARE proteins. A subset of SNAREs is specifically recruited to the immediate vicinity of the inclusion membrane, and their accumulation is reduced around inclusions that lack IncA, demonstrating that IncA plays a predominant role in SNARE recruitment. However, interaction with the SNARE machinery is probably not restricted to IncA as at least another inclusion protein shows similarities with SNARE motifs and can interact with SNAREs. We modelled IncA's association with host SNAREs. The analysis of intermolecular contacts showed that the IncA SNARE-like motif can make specific interactions with host SNARE motifs similar to those found in a bona fide SNARE complex. Moreover, point mutations in the central layer of IncA SNARE-like motifs resulted in the loss of binding to host SNAREs. Altogether, our data demonstrate for the first time mimicry of the SNARE motif by a bacterium

Balancing Feed-Forward Excitation and Inhibition via Hebbian Inhibitory Synaptic Plasticity

It has been suggested that excitatory and inhibitory inputs to cortical cells are balanced, and that this balance is important for the highly irregular firing observed in the cortex. There are two hypotheses as to the origin of this balance. One assumes that it results from a stable solution of the recurrent neuronal dynamics. This model can account for a balance of steady state excitation and inhibition without fine tuning of parameters, but not for transient inputs. The second hypothesis suggests that the feed forward excitatory and inhibitory inputs to a postsynaptic cell are already balanced. This latter hypothesis thus does account for the balance of transient inputs. However, it remains unclear what mechanism underlies the fine tuning required for balancing feed forward excitatory and inhibitory inputs. Here we investigated whether inhibitory synaptic plasticity is responsible for the balance of transient feed forward excitation and inhibition. We address this issue in the framework of a model characterizing the stochastic dynamics of temporally anti-symmetric Hebbian spike timing dependent plasticity of feed forward excitatory and inhibitory synaptic inputs to a single post-synaptic cell. Our analysis shows that inhibitory Hebbian plasticity generates ‘negative feedback’ that balances excitation and inhibition, which contrasts with the ‘positive feedback’ of excitatory Hebbian synaptic plasticity. As a result, this balance may increase the sensitivity of the learning dynamics to the correlation structure of the excitatory inputs

Computational modeling with spiking neural networks

This chapter reviews recent developments in the area of spiking neural networks (SNN) and summarizes the main contributions to this research field. We give background information about the functioning of biological neurons, discuss the most important mathematical neural models along with neural encoding techniques, learning algorithms, and applications of spiking neurons. As a specific application, the functioning of the evolving spiking neural network (eSNN) classification method is presented in detail and the principles of numerous eSNN based applications are highlighted and discussed

HIBISCUS: Hydroxychloroquine for the secondary prevention of thrombotic and obstetrical events in primary antiphospholipid syndrome

The relapse rate in antiphospholipid syndrome (APS) remains high, i.e. around 20%-21% at 5 years in thrombotic APS and 20-28% in obstetrical APS [2, 3]. Hydroxychloroquine (HCQ) appears as an additional therapy, as it possesses immunomodulatory and anti-thrombotic various effects [4-16]. Our group recently obtained the orphan designation of HCQ in antiphospholipid syndrome by the European Medicine Agency. Furthermore, the leaders of the project made the proposal of an international project, HIBISCUS, about the use of Hydroxychloroquine in secondary prevention of obstetrical and thrombotic events in primary APS. This study has been launched in several countries and at now, 53 centers from 16 countries participate to this international trial. This trial consists in two parts: a retrospective and a prospective study. The French part of the trial in thrombosis has been granted by the French Minister of Health in December 2015 (the academic trial independent of the pharmaceutical industry PHRC N PAPIRUS) and is coordinated by one of the members of the leading consortium of HIBISCUS

Dynamical neural networks: Modeling low-level vision at short latencies

Our goal is to understand the dynamics of neural computations in low-level vision. We study how the substrate of this system, that is local biochemical neural processes, could combine to give rise to an efficient and global perception. We will study these neural computations at different scales from the single-cell to the whole visual system to infer generic aspects of the under- lying neural code which may help to understand this cognitive ability. In fact, the architecture of cortical areas, such as the Primary Visual Cortex (V1), is massively parallel and we will focus on cortical columns as generic adaptive micro-circuits. To stress on the dynamical aspect of the processing, we will also focus on the transient response, that is during the first milliseconds after the presentation of a stimulus. In a generic model of a visual area, we propose to study the neural code as implementing visual pattern matching, that is as efficiently inverting a known model of image synthesis. A possible solution offered by the architecture of the visual pathways could be to represent at first on the surface of the cortical area how well the prototypical visual features are matched by a combination of inferential mechanisms as ideal observers. We studied the efficiency of this representation by rating the statistics of the output using natural scenes, that is scenes occurring frequently. We show that this may be finally used to provide a behavioral output such as an eye movement. However, constraints specific to the visual system imply that the set of prototypical features is not independent and that the cortical columns should communicate to produce an efficient, sparse solution. We will present efficient algorithms and representations based on the event-based nature of neural computations. By explicitely defining this efficiency, we propose then a simple implementation of Sparse Spike Coding using greedy inference mechanisms but also how the system may adapt in a unsupervised fashion. These computations may be implemented in simple models of neural networks by explicitly setting the lateral connectivity between populations of columns. Using natural scenes, this algorithm provides a model of V1 which exhibit prototypical properties of neural activities in that area. We show simple applications in the field of image processing as a quantitative method to evaluate these different cortical models