Activity

Copyright © 2020 Bidelman, Bush and Boudreaux.There is evidence to show that electro-acupuncture (EA) has a promotive effect on both lipolysis and thermogenesis, and that these mechanisms underlie the anti-obesity effect of EA. The sympathetic nervous system (SNS) is known to play a role in thermogenesis. Additionally, obesity is characterized by a chronic low-grade inflammatory state. Based on these findings, the aim of the present study is to investigate the potential neuro-immune mechanisms underlying the therapeutic effect of EA in obesity. In the experiment, we used a high fat diet (HFD) rats model to study the effect of EA in reducing body weight. EA increases the activity of sympathetic nerves in inguinal white adipose tissue (iWAT), especially in the HFD group. Compared to HFD rats, EA can decrease sympathetic associated macrophage (SAM) and the level of norepinephrine transporter protein (Slc6a2). The relative uncoupling protein 1 expression shows EA increases thermogenesis in iWAT, and increases β3 receptors. Interestingly, injecting β antagonist in iWAT increases Slc6a2 protein levels. Additionally, the SNS-macrophage cross-talk response to EA showed in iWAT but not in epididymis white adipose tissue. The results of the present study indicate that EA exerts its anti-obesity effect via three mechanisms (1) inhibition of SAMs and the norepinephrine transporter protein SlC6a2, (2) promoting SNS activity and thermogenesis, and (3) regulating immunologic balance. Copyright © 2020 Lu, He, Gong, Li, Tang, Wang, Wang, Yuan, Yu and Xu.Spiking neural networks are well-suited for spatiotemporal feature detection and learning, and naturally involve dynamic delay mechanisms in the synapses, dendrites, and axons. Dedicated delay neurons and axonal delay circuits have been considered when implementing such pattern recognition networks in dynamic neuromorphic processors. Inspired by an auditory feature detection circuit in crickets, featuring a delayed excitation by post-inhibitory rebound, we investigate disynaptic delay elements formed by inhibitory-excitatory pairs of dynamic synapses. We configured such disynaptic delay elements in the DYNAP-SE neuromorphic processor and characterized the distribution of delayed excitations resulting from device mismatch. Interestingly, we found that the disynaptic delay elements can be configured such that the timing and magnitude of the delayed excitation depend mainly on the efficacy of the inhibitory and excitatory synapses, respectively, and that a neuron with multiple delay elements can be tuned to respond selectively to a specific pattern. Furthermore, we present a network with one disynaptic delay element that mimics the auditory feature detection circuit of crickets, and we demonstrate how varying synaptic weights, input noise and processor temperature affect the circuit. Dynamic delay elements of this kind open up for synapse level temporal feature tuning with configurable delays of up to 100 ms. Copyright © 2020 Sandin and Nilsson.Spiking Neural Networks (SNNs) have recently emerged as a prominent neural computing paradigm. However, the typical shallow SNN architectures have limited capacity for expressing complex representations while training deep SNNs using input spikes has not been successful so far. Diverse methods have been proposed to get around this issue such as converting off-the-shelf trained deep Artificial Neural Networks (ANNs) to SNNs. However, the ANN-SNN conversion scheme fails to capture the temporal dynamics of a spiking system. On the other hand, it is still a difficult problem to directly train deep SNNs using input spike events due to the discontinuous, non-differentiable nature of the spike generation function. To overcome this problem, we propose an approximate derivative method that accounts for the leaky behavior of LIF neurons. This method enables training deep convolutional SNNs directly (with input spike events) using spike-based backpropagation. Our experiments show the effectiveness of the proposed spike-based learning on deep networks (VGG and Residual architectures) by achieving the best classification accuracies in MNIST, SVHN, and CIFAR-10 datasets compared to other SNNs trained with a spike-based learning. selleck kinase inhibitor Moreover, we analyze sparse event-based computations to demonstrate the efficacy of the proposed SNN training method for inference operation in the spiking domain. Copyright © 2020 Lee, Sarwar, Panda, Srinivasan and Roy.Hereditary spastic paraplegias (HSP) are a group of neurodegenerative diseases sharing spasticity in lower limbs as common symptom. There is a large clinical variability in the presentation of patients, partly underlined by the large genetic heterogeneity, with more than 60 genes responsible for HSP. Despite this large heterogeneity, the proteins with known function are supposed to be involved in a limited number of cellular compartments such as shaping of the endoplasmic reticulum or endolysosomal function. Yet, it is difficult to understand why alteration of such different cellular compartments can lead to degeneration of the axons of cortical motor neurons. A common feature that has emerged over the last decade is the alteration of lipid metabolism in this group of pathologies. This was first revealed by the identification of mutations in genes encoding proteins that have or are supposed to have enzymatic activities on lipid substrates. However, it also appears that mutations in genes affecting endoplasmic reticulum, mitochondria, or endolysosome function can lead to changes in lipid distribution or metabolism. The aim of this review is to discuss the role of lipid metabolism alterations in the physiopathology of HSP, to evaluate how such alterations contribute to neurodegenerative phenotypes, and to understand how this knowledge can help develop therapeutic strategy for HSP. Copyright © 2020 Darios, Mochel and Stevanin.High-fidelity measurements of neural activity can enable advancements in our understanding of the neural basis of complex behaviors such as speech, audition, and language, and are critical for developing neural prostheses that address impairments to these abilities due to disease or injury. We develop a novel high resolution, thin-film micro-electrocorticography (micro-ECoG) array that enables high-fidelity surface measurements of neural activity from songbirds, a well-established animal model for studying speech behavior. With this device, we provide the first demonstration of sensory-evoked modulation of surface-recorded single unit responses. We establish that single unit activity is consistently sensed from micro-ECoG electrodes over the surface of sensorimotor nucleus HVC (used as a proper name) in anesthetized European starlings, and validate responses with correlated firing in single units recorded simultaneously at surface and depth. The results establish a platform for high-fidelity recording from the surface of subcortical structures that will accelerate neurophysiological studies, and development of novel electrode arrays and neural prostheses.