AIMC Topic: Neurons

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What is the Key Conceptual or Methodological Bottleneck to Controlling Neural Biology?

Cell systems
Neurostimulation techniques allow us to manipulate the activity of nervous systems, and even that of single neurons. In this piece, researchers discuss what they see as the current key bottlenecks to controlling neural biology.

Sparse Scanning Electron Microscopy Data Acquisition and Deep Neural Networks for Automated Segmentation in Connectomics.

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada
With the growing importance of three-dimensional and very large field of view imaging, acquisition time becomes a serious bottleneck. Additionally, dose reduction is of importance when imaging material like biological tissue that is sensitive to elec...

Distinct subtypes of inhibitory interneurons differentially promote the propagation of rate and temporal codes in the feedforward neural network.

Chaos (Woodbury, N.Y.)
Sensory information is believed to be encoded in neuronal spikes using two different neural codes, the rate code (spike firing rate) and the temporal code (precisely-timed spikes). Since the sensory cortex has a highly hierarchical feedforward struct...

A simple locally active memristor and its application in HR neurons.

Chaos (Woodbury, N.Y.)
This paper proposes a simple locally active memristor whose state equation only consists of linear terms and an easily implementable function and design for its circuit emulator. The effectiveness of the circuit emulator is validated using breadboard...

Extreme value theory of evolving phenomena in complex dynamical systems: Firing cascades in a model of a neural network.

Chaos (Woodbury, N.Y.)
We extend the scope of the dynamical theory of extreme values to include phenomena that do not happen instantaneously but evolve over a finite, albeit unknown at the onset, time interval. We consider complex dynamical systems composed of many individ...

Computational cannula microscopy of neurons using neural networks.

Optics letters
Computational cannula microscopy is a minimally invasive imaging technique that can enable high-resolution imaging deep inside tissue. Here, we apply artificial neural networks to enable real-time, power-efficient image reconstructions that are more ...

3D high resolution generative deep-learning network for fluorescence microscopy imaging.

Optics letters
Microscopic fluorescence imaging serves as a basic tool in many research areas including biology, medicine, and chemistry. With the help of optical clearing, large volume imaging of a mouse brain and even a whole body has been enabled. However, const...

STDP Forms Associations between Memory Traces in Networks of Spiking Neurons.

Cerebral cortex (New York, N.Y. : 1991)
Memory traces and associations between them are fundamental for cognitive brain function. Neuron recordings suggest that distributed assemblies of neurons in the brain serve as memory traces for spatial information, real-world items, and concepts. Ho...

An Effective Encoder-Decoder Network for Neural Cell Bodies and Cell Nucleus Segmentation of EM Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Neural systems are complicated networks connected by a large number of neurons through gap junctions and synapse. At present, for electron microscopy connectomics research, neuron structure recognition algorithms mostly focus on synapses, dendrites, ...