AIMC Topic: Neurons

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Figure-Ground Organization in Natural Scenes: Performance of a Recurrent Neural Model Compared with Neurons of Area V2.

eNeuro
A crucial step in understanding visual input is its organization into meaningful components, in particular object contours and partially occluded background structures. This requires that all contours are assigned to either the foreground or the back...

Prototyping a memristive-based device to analyze neuronal excitability.

Biophysical chemistry
Many efforts have been spent in the last decade for the development of nanoscale synaptic devices integrated into neuromorphic circuits, trying to emulate the behavior of natural synapses. The study of brain properties with the standard approaches ba...

Training Spiking Neural Networks for Cognitive Tasks: A Versatile Framework Compatible With Various Temporal Codes.

IEEE transactions on neural networks and learning systems
Recent studies have demonstrated the effectiveness of supervised learning in spiking neural networks (SNNs). A trainable SNN provides a valuable tool not only for engineering applications but also for theoretical neuroscience studies. Here, we propos...

Learning cellular morphology with neural networks.

Nature communications
Reconstruction and annotation of volume electron microscopy data sets of brain tissue is challenging but can reveal invaluable information about neuronal circuits. Significant progress has recently been made in automated neuron reconstruction as well...

Biologically plausible deep learning - But how far can we go with shallow networks?

Neural networks : the official journal of the International Neural Network Society
Training deep neural networks with the error backpropagation algorithm is considered implausible from a biological perspective. Numerous recent publications suggest elaborate models for biologically plausible variants of deep learning, typically defi...

On Ev-Degree and Ve-Degree Topological Properties of Tickysim Spiking Neural Network.

Computational intelligence and neuroscience
Topological indices are indispensable tools for analyzing networks to understand the underlying topology of these networks. Spiking neural network architecture (SpiNNaker or TSNN) is a million-core calculating engine which aims at simulating the beha...

DeTerm: Software for automatic detection of neuronal dendritic branch terminals via an artificial neural network.

Genes to cells : devoted to molecular & cellular mechanisms
Dendrites of neurons receive and process synaptic or sensory inputs. The Drosophila class IV dendritic arborization (da) neuron is an established model system to explore molecular mechanisms of dendrite morphogenesis. The total number of dendritic br...

Spatiotemporal discrimination in attractor networks with short-term synaptic plasticity.

Journal of computational neuroscience
We demonstrate that a randomly connected attractor network with dynamic synapses can discriminate between similar sequences containing multiple stimuli suggesting such networks provide a general basis for neural computations in the brain. The network...

Neural network model of an amphibian ventilatory central pattern generator.

Journal of computational neuroscience
The neuronal multiunit model presented here is a formal model of the central pattern generator (CPG) of the amphibian ventilatory neural network, inspired by experimental data from Pelophylax ridibundus. The kernel of the CPG consists of three pacema...

A Reservoir Computing Model of Reward-Modulated Motor Learning and Automaticity.

Neural computation
Reservoir computing is a biologically inspired class of learning algorithms in which the intrinsic dynamics of a recurrent neural network are mined to produce target time series. Most existing reservoir computing algorithms rely on fully supervised l...