Periaxonal and nodal plasticities modulate action potential conduction in the adult mouse brain

Central nervous system myelination increases action potential conduction velocity. However, it is unclearhow myelination is coordinated to ensure the temporally precise arrival of action potentials and facilitate information processing within cortical and associative circuits. Here, we show that myelin sheaths, supportedby mature oligodendrocytes, remain plastic in the adult mouse brain and undergo subtle structural modifications to influence action potential conduction velocity. Repetitive transcranial magnetic stimulation andspatial learning, two stimuli that modify neuronal activity, alter the length of the nodes of Ranvier and thesize of the periaxonal space within active brain regions. This change in the axon-glial configuration is independent of oligodendrogenesis and robustly alters action potential conduction velocity. Because aptitudein the spatial learning task was found to correlate with action potential conduction velocity in the fimbriafornix pathway, modifying the axon-glial configuration may be a mechanism that facilitates learning in theadult mouse brain

Inhibition of rhythmic neural spiking by noise: the occurrence of a minimum in activity with increasing noise

The effects of noise on neuronal dynamical systems are of much current interest. Here, we investigate noise-induced changes in the rhythmic firing activity of single Hodgkin–Huxley neurons. With additive input current, there is, in the absence of noise, a critical mean value µ = µc above which sustained periodic firing occurs. With initial conditions as resting values, for a range of values of the mean µ near the critical value, we have found that the firing rate is greatly reduced by noise, even of quite small amplitudes. Furthermore, the firing rate may undergo a pronounced minimum as the noise increases. This behavior has the opposite character to stochastic resonance and coherence resonance. We found that these phenomena occurred even when the initial conditions were chosen randomly or when the noise was switched on at a random time, indicating the robustness of the results. We also examined the effects of conductance-based noise on Hodgkin–Huxley neurons and obtained similar results, leading to the conclusion that the phenomena occur across a wide range of neuronal dynamical systems. Further, these phenomena will occur in diverse applications where a stable limit cycle coexists with a stable focus

Engineering Yarrowia lipolytica to Produce Glycoproteins Homogeneously Modified with the Universal Man3GlcNAc2 N-Glycan Core

Yarrowia lipolytica is a dimorphic yeast that efficiently secretes various heterologous proteins and is classified as “generally recognized as safe.” Therefore, it is an attractive protein production host. However, yeasts modify glycoproteins with non-human high mannose-type N-glycans. These structures reduce the protein half-life in vivo and can be immunogenic in man. Here, we describe how we genetically engineered N-glycan biosynthesis in Yarrowia lipolytica so that it produces Man3GlcNAc2 structures on its glycoproteins. We obtained unprecedented levels of homogeneity of this glycanstructure. This is the ideal starting point for building human-like sugars. Disruption of the ALG3 gene resulted in modification of proteins mainly with Man5GlcNAc2 and GlcMan5GlcNAc2 glycans, and to a lesser extent with Glc2Man5GlcNAc2 glycans. To avoid underoccupancy of glycosylation sites, we concomitantly overexpressed ALG6. We also explored several approaches to remove the terminal glucose residues, which hamper further humanization of N-glycosylation; overexpression of the heterodimeric Apergillus niger glucosidase II proved to be the most effective approach. Finally, we overexpressed an α-1,2-mannosidase to obtain Man3GlcNAc2 structures, which are substrates for the synthesis of complex-type glycans. The final Yarrowia lipolytica strain produces proteins glycosylated with the trimannosyl core N-glycan (Man3GlcNAc2), which is the common core of all complex-type N-glycans. All these glycans can be constructed on the obtained trimannosyl N-glycan using either in vivo or in vitro modification with the appropriate glycosyltransferases. The results demonstrate the high potential of Yarrowia lipolytica to be developed as an efficient expression system for the production of glycoproteins with humanized glycans

Spike-Based Reinforcement Learning in Continuous State and Action Space: When Policy Gradient Methods Fail

Changes of synaptic connections between neurons are thought to be the physiological basis of learning. These changes can be gated by neuromodulators that encode the presence of reward. We study a family of reward-modulated synaptic learning rules for spiking neurons on a learning task in continuous space inspired by the Morris Water maze. The synaptic update rule modifies the release probability of synaptic transmission and depends on the timing of presynaptic spike arrival, postsynaptic action potentials, as well as the membrane potential of the postsynaptic neuron. The family of learning rules includes an optimal rule derived from policy gradient methods as well as reward modulated Hebbian learning. The synaptic update rule is implemented in a population of spiking neurons using a network architecture that combines feedforward input with lateral connections. Actions are represented by a population of hypothetical action cells with strong mexican-hat connectivity and are read out at theta frequency. We show that in this architecture, a standard policy gradient rule fails to solve the Morris watermaze task, whereas a variant with a Hebbian bias can learn the task within 20 trials, consistent with experiments. This result does not depend on implementation details such as the size of the neuronal populations. Our theoretical approach shows how learning new behaviors can be linked to reward-modulated plasticity at the level of single synapses and makes predictions about the voltage and spike-timing dependence of synaptic plasticity and the influence of neuromodulators such as dopamine. It is an important step towards connecting formal theories of reinforcement learning with neuronal and synaptic properties

Spike patterning in oxytocin neurons:Capturing physiological behaviour with Hodgkin-Huxley and integrate-and-fire models

Integrate-and-fire (IF) models can provide close matches to the discharge activity of neurons, but do they oversimplify the biophysical properties of the neurons? A single compartment Hodgkin-Huxley (HH) model of the oxytocin neuron has previously been developed, incorporating biophysical measurements of channel properties obtained in vitro. A simpler modified integrate-and-fire model has also been developed, which can match well the characteristic spike patterning of oxytocin neurons as observed in vivo. Here, we extended the HH model to incorporate synaptic input, to enable us to compare spike activity in the model with experimental data obtained in vivo. We refined the HH model parameters to closely match the data, and then matched the same experimental data with a modified IF model, using an evolutionary algorithm to optimise parameter matching. Finally we compared the properties of the modified HH model with those of the IF model to seek an explanation for differences between spike patterning in vitro and in vivo. We show that, with slight modifications, the original HH model, like the IF model, is able to closely match both the interspike interval (ISI) distributions of oxytocin neurons and the observed variability of spike firing rates in vivo and in vitro. This close match of both models to data depends on the presence of a slow activity-dependent hyperpolarisation (AHP); this is represented in both models and the parameters used in the HH model representation match well with optimal parameters of the IF model found by an evolutionary algorithm. The ability of both models to fit data closely also depends on a shorter hyperpolarising after potential (HAP); this is explicitly represented in the IF model, but in the HH model, it emerges from a combination of several components. The critical elements of this combination are identified

Origins and diversification of subsociality in leaf beetles (Coleoptera: Chrysomelidae: Cassidinae: Chrysomelinae)

Leaf beetles (Chrysomelidae; ~40,000 species) are commonly solitary animals but subsociality, maternal care of broods, is known in Cassidinae and Chrysomelinae. We report 11 novel records from Brazil and Peru, bringing the number of subsocial chrysomelids to 35 species in 10 genera. Two evolutionary models of chrysomelid subsociality have been proposed. One proposed three independent origins within Chrysomelinae, based on the potential phylogenetic positions of subsocial genera. The other hypothesised that an evolutionary arms race between chrysomelid prey and their predators, parasites, and parasitoids has led to an escalation of defences. Using our phylogenies, we propose that subsociality originated independently in Cassidinae and Chrysomelinae, and several times within each subfamily. Subsociality was preceded by particular behaviours. In Cassidinae, exophagous larvae with chemically offensive faecal weaponry preceded aggregated living, group defences (e.g. cycloalexy), and maternal guarding. In Chrysomelinae, offensive glandular compounds preceded ovi- and viviparity before subsociality. © 2014 © 2014 Taylor & Francis.This study was supported by a NSF-EPSCoR grant #66928 (USA; CSC), by the Institut de Biologia Evolutiva (CSIC-UPF, Spain; JGZ), by the Centro Universitário de Lavras (Brazil; FFC), and by Stichting Bevordering van Natuurwetenschappelijk Onderzoek (Netherlands; RW).Peer Reviewe