The Neurophysiology of Autonomic Dysfunction in SCI: Plasticity in the Input and Output Neurons

2014 ◽  
Author(s):  
Keith Tansey
Keyword(s):  
Autonomic Dysfunction ◽  
Input And Output ◽  
Output Neurons

Different classes of input and output neurons reveal new features in microglomeruli of the adult Drosophila mushroom body calyx

2012 ◽  
Vol 520 (10) ◽  
pp. 2185-2201 ◽  
Author(s):  
Nancy J. Butcher ◽  
Anja B. Friedrich ◽  
Zhiyuan Lu ◽  
Hiromu Tanimoto ◽  
Ian A. Meinertzhagen
Keyword(s):  
Mushroom Body ◽  
Input And Output ◽  
Output Neurons ◽  
Mushroom Body Calyx ◽  
Adult Drosophila

scholarly journals Convergence of monosynaptic and polysynaptic sensory paths onto common motor outputs in a Drosophila feeding connectome

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Anton Miroschnikow ◽  
Philipp Schlegel ◽  
Andreas Schoofs ◽  
Sebastian Hueckesfeld ◽  
Feng Li ◽  
...  
Keyword(s):  
Food Intake ◽  
Mushroom Body ◽  
Sensory Inputs ◽  
Input And Output ◽  
Drosophila Larvae ◽  
Output Neurons ◽  
Overlapping Domains ◽  
Motor Outputs ◽  
Neuroendocrine Neurons ◽  
Mushroom Body Calyx

We reconstructed, from a whole CNS EM volume, the synaptic map of input and output neurons that underlie food intake behavior of Drosophila larvae. Input neurons originate from enteric, pharyngeal and external sensory organs and converge onto seven distinct sensory synaptic compartments within the CNS. Output neurons consist of feeding motor, serotonergic modulatory and neuroendocrine neurons. Monosynaptic connections from a set of sensory synaptic compartments cover the motor, modulatory and neuroendocrine targets in overlapping domains. Polysynaptic routes are superimposed on top of monosynaptic connections, resulting in divergent sensory paths that converge on common outputs. A completely different set of sensory compartments is connected to the mushroom body calyx. The mushroom body output neurons are connected to interneurons that directly target the feeding output neurons. Our results illustrate a circuit architecture in which monosynaptic and multisynaptic connections from sensory inputs traverse onto output neurons via a series of converging paths.

scholarly journals Dynamics of Olfactory Bulb Input and Output Activity During Odor Stimulation in Zebrafish

2004 ◽  
Vol 91 (6) ◽  
pp. 2658-2669 ◽  
Author(s):  
Rainer W. Friedrich ◽  
Gilles Laurent
Keyword(s):  
Olfactory Bulb ◽  
Activity Patterns ◽  
Odor Identification ◽  
Population Activity ◽  
Excitatory Response ◽  
Input And Output ◽  
Response Onset ◽  
Output Neurons ◽  
Response Profiles ◽  
Over Time

The processing of odor-evoked activity in the olfactory bulb (OB) of zebrafish was studied by extracellular single unit recordings from the input and output neurons, i.e., olfactory receptor neurons (ORNs) and mitral cells (MCs), respectively. A panel of 16 natural amino acid odors was used as stimuli. Responses of MCs, but not ORNs, changed profoundly during the first few hundred milliseconds after response onset. In MCs, but not ORNs, the total evoked excitatory activity in the population was initially odordependent but subsequently converged to a common level. Hence, the overall population activity is regulated by network interactions in the OB. The tuning widths of both ORN and MC response profiles were similar and, on average, stable over time. However, when analyzed for individual neurons, MC response profiles could sharpen (excitatory response to fewer odors) or broaden (excitatory response to more odors), whereas ORN response profiles remained nearly unchanged. Several observations indicate that dynamic inhibition plays an important role in this remodeling. Finally, the reliability of odor identification based on MC population activity patterns improved over time, whereas odor identification based on ORN activity patterns was most reliable early in the odor response. These results demonstrate that several properties of MC, but not ORN, activity change during the initial phase of the odor response with important consequences for odor-encoding activity patterns. Furthermore, our data indicate that inhibitory interactions in the OB are important in dynamically shaping the activity of OB output neurons.

scholarly journals Radial and Directional Posteriors for Bayesian Deep Learning

2020 ◽  
Vol 34 (04) ◽  
pp. 5298-5305
Author(s):  
Changyong Oh ◽  
Kamil Adamczewski ◽  
Mijung Park
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Mean Field ◽  
Predictive Performance ◽  
Radial Component ◽  
Field Type ◽  
Input And Output ◽  
Output Neurons ◽  
Directional Density ◽  
Directional Component

We propose a new variational family for Bayesian neural networks. We decompose the variational posterior into two components, where the radial component captures the strength of each neuron in terms of its magnitude; while the directional component captures the statistical dependencies among the weight parameters. The dependencies learned via the directional density provide better modeling performance compared to the widely-used Gaussian mean-field-type variational family. In addition, the strength of input and output neurons learned via our posterior provides a structured way to compress neural networks. Indeed, experiments show that our variational family improves predictive performance and yields compressed networks simultaneously.

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