AIMC Topic: Brain

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Distinct role of flexible and stable encodings in sequential working memory.

Neural networks : the official journal of the International Neural Network Society
The serial-position effect in working memory is considered important for studying how a sequence of sensory information can be retained and manipulated simultaneously in neural memory circuits. Here, via a precise analysis of the primacy and recency ...

Design Space Exploration of Hardware Spiking Neurons for Embedded Artificial Intelligence.

Neural networks : the official journal of the International Neural Network Society
Machine learning is yielding unprecedented interest in research and industry, due to recent success in many applied contexts such as image classification and object recognition. However, the deployment of these systems requires huge computing capabil...

Post-hoc modification of linear models: Combining machine learning with domain information to make solid inferences from noisy data.

NeuroImage
Linear machine learning models "learn" a data transformation by being exposed to examples of input with the desired output, forming the basis for a variety of powerful techniques for analyzing neuroimaging data. However, their ability to learn the de...

Phasic dopamine release identification using convolutional neural network.

Computers in biology and medicine
Dopamine has a major behavioral impact related to drug dependence, learning and memory functions, as well as pathologies such as schizophrenia and Parkinson's disease. Phasic release of dopamine can be measured in vivo with fast-scan cyclic voltammet...

Decoding and encoding (de)mixed population responses.

Current opinion in neurobiology
A central tenet of neuroscience is that the brain works through large populations of interacting neurons. With recent advances in recording techniques, the inner working of these populations has come into full view. Analyzing the resulting large-scal...

Event-driven implementation of deep spiking convolutional neural networks for supervised classification using the SpiNNaker neuromorphic platform.

Neural networks : the official journal of the International Neural Network Society
Neural networks have enabled great advances in recent times due mainly to improved parallel computing capabilities in accordance to Moore's Law, which allowed reducing the time needed for the parameter learning of complex, multi-layered neural archit...

EEG-based single-channel authentication systems with optimum electrode placement for different mental activities.

Biomedical journal
BACKGROUND: Electroencephalogram (EEG) signals of a brain contain a unique pattern for each person and the potential for biometric applications. Authentication and security is a very important issue in our life and brainwave-based authentication is a...

Deep learning and deep knowledge representation in Spiking Neural Networks for Brain-Computer Interfaces.

Neural networks : the official journal of the International Neural Network Society
OBJECTIVE: This paper argues that Brain-Inspired Spiking Neural Network (BI-SNN) architectures can learn and reveal deep in time-space functional and structural patterns from spatio-temporal data. These patterns can be represented as deep knowledge, ...

Image reconstruction for positron emission tomography based on patch-based regularization and dictionary learning.

Medical physics
PURPOSE: Positron emission tomography (PET) is an important tool for nuclear medical imaging. It has been widely used in clinical diagnosis, scientific research, and drug testing. PET is a kind of emission computed tomography. Its basic imaging princ...

Rethinking the performance comparison between SNNS and ANNS.

Neural networks : the official journal of the International Neural Network Society
Artificial neural networks (ANNs), a popular path towards artificial intelligence, have experienced remarkable success via mature models, various benchmarks, open-source datasets, and powerful computing platforms. Spiking neural networks (SNNs), a ca...