AIMC Topic: Models, Neurological

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[Artificial Intelligence and Cerebellar Motor Learning].

Brain and nerve = Shinkei kenkyu no shinpo
Half a century ago, cerebellar learning models based on a simple perceptron were proposed independently by Marr and Albus. Soon, these models were combined with Ito's flocculus hypothesis that the cerebellar flocculus controls the vestibulo-ocular re...

Neural population control via deep image synthesis.

Science (New York, N.Y.)
Particular deep artificial neural networks (ANNs) are today's most accurate models of the primate brain's ventral visual stream. Using an ANN-driven image synthesis method, we found that luminous power patterns (i.e., images) can be applied to primat...

Optimizing Machine Learning Methods to Improve Predictive Models of Alzheimer's Disease.

Journal of Alzheimer's disease : JAD
BACKGROUND: Predicting clinical course of cognitive decline can boost clinical trials' power and improve our clinical decision-making. Machine learning (ML) algorithms are specifically designed for the purpose of prediction; however. identifying opti...

Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision.

Cerebral cortex (New York, N.Y. : 1991)
Convolutional neural network (CNN) driven by image recognition has been shown to be able to explain cortical responses to static pictures at ventral-stream areas. Here, we further showed that such CNN could reliably predict and decode functional magn...

Propagation delays determine neuronal activity and synaptic connectivity patterns emerging in plastic neuronal networks.

Chaos (Woodbury, N.Y.)
In plastic neuronal networks, the synaptic strengths are adapted to the neuronal activity. Specifically, spike-timing-dependent plasticity (STDP) is a fundamental mechanism that modifies the synaptic strengths based on the relative timing of pre- and...

Global firing rate contrast enhancement in E/I neuronal networks by recurrent synchronized inhibition.

Chaos (Woodbury, N.Y.)
Inhibitory synchronization is commonly observed and may play some important functional roles in excitatory/inhibitory (E/I) neuronal networks. The firing rate contrast enhancement is a general feature of information processing in sensory pathways, an...

Chaos versus noise as drivers of multistability in neural networks.

Chaos (Woodbury, N.Y.)
The multistable behavior of neural networks is actively being studied as a landmark of ongoing cerebral activity, reported in both functional Magnetic Resonance Imaging (fMRI) and electro- or magnetoencephalography recordings. This consists of a cont...

A Dynamical Systems Perspective on Flexible Motor Timing.

Trends in cognitive sciences
A hallmark of higher brain function is the ability to rapidly and flexibly adjust behavioral responses based on internal and external cues. Here, we examine the computational principles that allow decisions and actions to unfold flexibly in time. We ...