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Spectral signatures of serotonergic psychedelics and glutamatergic dissociatives.

NeuroImage
Classic serotonergic psychedelics are remarkable for their capacity to induce reversible alterations in consciousness of the self and the surroundings, mediated by agonism at serotonin 5-HT receptors. The subjective effects elicited by dissociative d...

Discrimination of bursts and tonic activity in multifunctional sensorimotor neural network using the extended hill-valley method.

Journal of neurophysiology
Individual neurons can exhibit a wide range of activity, including spontaneous spiking, tonic spiking, bursting, or spike-frequency adaptation, and can also transition between these activity types. Manual identification of these activity patterns can...

Pairwise Interactions among Brain Regions Organize Large-Scale Functional Connectivity during Execution of Various Tasks.

Neuroscience
Spatially separated brain areas interact with each other to form networks with coordinated activities, supporting various brain functions. Interaction structures among brain areas have been widely investigated through pairwise measures. However, inte...

Putting a bug in ML: The moth olfactory network learns to read MNIST.

Neural networks : the official journal of the International Neural Network Society
We seek to (i) characterize the learning architectures exploited in biological neural networks for training on very few samples, and (ii) port these algorithmic structures to a machine learning context. The moth olfactory network is among the simples...

The application of machine learning methods for prediction of metal sorption onto biochars.

Journal of hazardous materials
The adsorption of six heavy metals (lead, cadmium, nickel, arsenic, copper, and zinc) on 44 biochars were modeled using artificial neural network (ANN) and random forest (RF) based on 353 dataset of adsorption experiments from literatures. The regres...

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...

Convolutional Neural Networks for Recognition of Lymphoblast Cell Images.

Computational intelligence and neuroscience
This paper presents the recognition for WHO classification of acute lymphoblastic leukaemia (ALL) subtypes. The two ALL subtypes considered are T-lymphoblastic leukaemia (pre-T) and B-lymphoblastic leukaemia (pre-B). They exhibit various characterist...

Machine learning identifies unaffected first-degree relatives with functional network patterns and cognitive impairment similar to those of schizophrenia patients.

Human brain mapping
Schizophrenia (SCZ) patients and their unaffected first-degree relatives (FDRs) share similar functional neuroanatomy. However, it remains largely unknown to what extent unaffected FDRs with functional neuroanatomy patterns similar to patients can be...

Effective Dimensionality Reduction for Visualizing Neural Dynamics by Laplacian Eigenmaps.

Neural computation
With the development of neural recording technology, it has become possible to collect activities from hundreds or even thousands of neurons simultaneously. Visualization of neural population dynamics can help neuroscientists analyze large-scale neur...

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...