Fatal cardiac arrest in an adult patient with euthyroid anti-SSA/Ro-positive connective tissue disease: a case report

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Nonlinear computations in spiking neural networks through multiplicative synapses

The brain efficiently performs nonlinear computations through its intricate networks of spiking neurons, but how this is done remains elusive. While nonlinear computations can be implemented successfully in spiking neural networks, this requires supervised training and the resulting connectivity can be hard to interpret. In contrast, the required connectivity for any computation in the form of a linear dynamical system can be directly derived and understood with the spike coding network (SCN) framework. These networks also have biologically realistic activity patterns and are highly robust to cell death. Here we extend the SCN framework to directly implement any polynomial dynamical system, without the need for training. This results in networks requiring a mix of synapse types (fast, slow, and multiplicative), which we term multiplicative spike coding networks (mSCNs). Using mSCNs, we demonstrate how to directly derive the required connectivity for several nonlinear dynamical systems. We also show how to carry out higher-order polynomials with coupled networks that use only pair-wise multiplicative synapses, and provide expected numbers of connections for each synapse type. Overall, our work demonstrates a novel method for implementing nonlinear computations in spiking neural networks, while keeping the attractive features of standard SCNs (robustness, realistic activity patterns, and interpretable connectivity). Finally, we discuss the biological plausibility of our approach, and how the high accuracy and robustness of the approach may be of interest for neuromorphic computing.Comment: This article has been peer-reviewed and recommended by Peer Community In Neuroscienc

Nonlinear computations in spiking neural networks through multiplicative synapses

The brain efficiently performs nonlinear computations through its intricate networks of spiking neurons, but how this is done remains elusive. While nonlinear computations can be implemented successfully in spiking neural networks, this requires supervised training and the resulting connectivity can be hard to interpret. In contrast, the required connectivity for any computation in the form of a linear dynamical system can be directly derived and understood with the spike coding network (SCN) framework. These networks also have biologically realistic activity patterns and are highly robust to cell death. Here we extend the SCN framework to directly implement any polynomial dynamical system, without the need for training. This results in networks requiring a mix of synapse types (fast, slow, and multiplicative), which we term multiplicative spike coding networks (mSCNs). Using mSCNs, we demonstrate how to directly derive the required connectivity for several nonlinear dynamical systems. We also show how to carry out higher-order polynomials with coupled networks that use only pair-wise multiplicative synapses, and provide expected numbers of connections for each synapse type. Overall, our work demonstrates a novel method for implementing nonlinear computations in spiking neural networks, while keeping the attractive features of standard SCNs (robustness, realistic activity patterns, and interpretable connectivity). Finally, we discuss the biological plausibility of our approach, and how the high accuracy and robustness of the approach may be of interest for neuromorphic computing

The entorhinal cognitive map is attracted to goals

Grid cells with their rigid hexagonal firing fields are thought to provide an invariant metric to the hippocampal cognitive map, yet environmental geometrical features have recently been shown to distort the grid structure. Given that the hippocampal role goes beyond space, we tested the influence of nonspatial information on the grid organization. We trained rats to daily learn three new reward locations on a cheeseboard maze while recording from the medial entorhinal cortex and the hippocampal CA1 region. Many grid fields moved toward goal location, leading to long-lasting deformations of the entorhinal map. Therefore, distortions in the grid structure contribute to goal representation during both learning and recall, which demonstrates that grid cells participate in mnemonic coding and do not merely provide a simple metric of space

Adenovirus-5-Vectored P. falciparum Vaccine Expressing CSP and AMA1. Part B: Safety, Immunogenicity and Protective Efficacy of the CSP Component

Background: A protective malaria vaccine will likely need to elicit both cell-mediated and antibody responses. As adenovirus vaccine vectors induce both these responses in humans, a Phase 1/2a clinical trial was conducted to evaluate the efficacy of an adenovirus serotype 5-vectored malaria vaccine against sporozoite challenge.\ud \ud Methodology/Principal Findings: NMRC-MV-Ad-PfC is an adenovirus vector encoding the Plasmodium falciparum 3D7 circumsporozoite protein (CSP). It is one component of a two-component vaccine NMRC-M3V-Ad-PfCA consisting of one adenovector encoding CSP and one encoding apical membrane antigen-1 (AMA1) that was evaluated for safety and immunogenicity in an earlier study (see companion paper, Sedegah et al). Fourteen Ad5 seropositive or negative adults received two doses of NMRC-MV-Ad-PfC sixteen weeks apart, at 1x1010 particle units per dose. The vaccine was safe and well tolerated. All volunteers developed positive ELISpot responses by 28 days after the first immunization (geometric mean 272 spot forming cells/million[sfc/m]) that declined during the following 16 weeks and increased after the second dose to levels that in most cases were less than the initial peak (geometric mean 119 sfc/m). CD8+ predominated over CD4+ responses, as in the first clinical trial. Antibody responses were poor and like ELISpot responses increased after the second immunization but did not exceed the initial peak. Pre-existing neutralizing antibodies (NAb) to Ad5 did not affect the immunogenicity of the first dose, but the fold increase in NAb induced by the first dose was significantly associated with poorer antibody responses after the second dose, while ELISpot responses remained unaffected. When challenged by the bite of P. falciparum-infected mosquitoes, two of 11 volunteers showed a delay in the time to patency compared to infectivity controls, but no volunteers were sterilely protected.\ud \ud Significance: The NMRC-MV-Ad-PfC vaccine expressing CSP was safe and well tolerated given as two doses, but did not provide sterile protection

Supplementary Code and Data for the paper "The Entorhinal Cognitive Map is Attracted to Goals"

Open the files in Jupyter Notebook (reccomended https://www.anaconda.com/distribution/#download-section with Python 3.7)

ISTA Thesis

The ability to form and retrieve memories is central to survival. In mammals, the hippocampus is a brain region essential to the acquisition and consolidation of new memories. It is also involved in keeping track of one’s position in space and aids navigation. Although this space-memory has been a source of contradiction, evidence supports the view that the role of the hippocampus in navigation is memory, thanks to the formation of cognitive maps. First introduced by Tolman in 1948, cognitive maps are generally used to organize experiences in memory; however, the detailed mechanisms by which these maps are formed and stored are not yet agreed upon. Some influential theories describe this process as involving three fundamental steps: initial encoding by the hippocampus, interactions between the hippocampus and other cortical areas, and long-term extra-hippocampal consolidation. In this thesis, I will show how the investigation of cognitive maps of space helped to shed light on each of these three memory processes. The first study included in this thesis deals with the initial encoding of spatial memories in the hippocampus. Much is known about encoding at the level of single cells, but less about their co-activity or joint contribution to the encoding of novel spatial information. I will describe the structure of an interaction network that allows for efficient encoding of noisy spatial information during the first exploration of a novel environment. The second study describes the interactions between the hippocampus and the prefrontal cortex (PFC), two areas directly and indirectly connected. It is known that the PFC, in concert with the hippocampus, is involved in various processes, including memory storage and spatial navigation. Nonetheless, the detailed mechanisms by which PFC receives information from the hippocampus are not clear. I will show how a transient improvement in theta phase locking of PFC cells enables interactions of cell pairs across the two regions. The third study describes the learning of behaviorally-relevant spatial locations in the hippocampus and the medial entorhinal cortex. I will show how the accumulation of firing around goal locations, a correlate of learning, can shed light on the transition from short- to long-term spatial memories and the speed of consolidation in different brain areas. The studies included in this thesis represent the main scientific contributions of my Ph.D. They involve statistical analyses and models of neural responses of cells in different brain areas of rats executing spatial tasks. I will conclude the thesis by discussing the impact of the findings on principles of memory formation and retention, including the mechanisms, the speed, and the duration of these processes

The generalized spatial representation in the prefrontal cortex is inherited from the hippocampus

Hippocampal and neocortical neural activity is modulated by the position of the individual in space. While hippocampal neurons provide the basis for a spatial map, prefrontal cortical neurons generalize over environmental features. Whether these generalized representations result from a bidirectional interaction with, or are mainly derived from hippocampal spatial representations is not known. By examining simultaneously recorded hippocampal and medial prefrontal neurons, we observed that prefrontal spatial representations show a delayed coherence with hippocampal ones. We also identified subpopulations of cells in the hippocampus and medial prefrontal cortex that formed functional cross-area couplings; these resembled the optimal connections predicted by a probabilistic model of spatial information transfer and generalization. Moreover, cross-area couplings were strongest and had the shortest delay preceding spatial decision-making. Our results suggest that generalized spatial coding in the medial prefrontal cortex is inherited from spatial representations in the hippocampus, and that the routing of information can change dynamically with behavioral demands

Theta oscillations as a substrate for medial prefrontal-hippocampal assembly interactions

The execution of cognitive functions requires coordinated circuit activity across different brain areas that involves the associated firing of neuronal assemblies. Here, we tested the circuit mechanism behind assembly interactions between the hippocampus and the medial prefrontal cortex (mPFC) of adult rats by recording neuronal populations during a rule-switching task. We identified functionally coupled CA1-mPFC cells that synchronized their activity beyond that expected from common spatial coding or oscillatory firing. When such cell pairs fired together, the mPFC cell strongly phase locked to CA1 theta oscillations and maintained consistent theta firing phases, independent of the theta timing of their CA1 counterpart. These functionally connected CA1-mPFC cells formed interconnected assemblies. While firing together with their CA1 assembly partners, mPFC cells fired along specific theta sequences. Our results suggest that upregulated theta oscillatory firing of mPFC cells can signal transient interactions with specific CA1 assemblies, thus enabling distributed computations

The structure of hippocampal CA1 interactions optimizes spatial coding across experience

Although much is known about how single neurons in the hippocampus represent an animal’s position, how cell-cell interactions contribute to spatial coding remains poorly understood. Using a novel statistical estimator and theoretical modeling, both developed in the framework of maximum entropy models, we reveal highly structured cell-to-cell interactions whose statistics depend on familiar vs. novel environment. In both conditions the circuit interactions optimize the encoding of spatial information, but for regimes that differ in the signal-to-noise ratio of their spatial inputs. Moreover, the topology of the interactions facilitates linear decodability, making the information easy to read out by downstream circuits. These findings suggest that the efficient coding hypothesis is not applicable only to individual neuron properties in the sensory periphery, but also to neural interactions in the central brain