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Stochasticity from function - Why the Bayesian brain may need no noise.

Neural networks : the official journal of the International Neural Network Society
An increasing body of evidence suggests that the trial-to-trial variability of spiking activity in the brain is not mere noise, but rather the reflection of a sampling-based encoding scheme for probabilistic computing. Since the precise statistical p...

Neural dynamics of emotion and cognition: From trajectories to underlying neural geometry.

Neural networks : the official journal of the International Neural Network Society
How can we study, characterize, and understand the neural underpinnings of cognitive-emotional behaviors as inherently dynamic processes? In the past 50 years, Stephen Grossberg has developed a research program that embraces the themes of dynamics, d...

One or two minds? Neural network modeling of decision making by the unified self.

Neural networks : the official journal of the International Neural Network Society
Ever since the seminal work of Tversky and Kahneman starting in the late 1960s, it has generally been accepted that many characteristic human decision patterns do not follow the norms of economic theories based on rational utility maximization and co...

One Step Back, Two Steps Forward: Interference and Learning in Recurrent Neural Networks.

Neural computation
Artificial neural networks, trained to perform cognitive tasks, have recently been used as models for neural recordings from animals performing these tasks. While some progress has been made in performing such comparisons, the evolution of network dy...

A Mechanism for Synaptic Copy Between Neural Circuits.

Neural computation
Cortical oscillations are central to information transfer in neural systems. Significant evidence supports the idea that coincident spike input can allow the neural threshold to be overcome and spikes to be propagated downstream in a circuit. Thus, a...

Intermittent Discrete Observation Control for Synchronization of Stochastic Neural Networks.

IEEE transactions on cybernetics
In this paper, to investigate the exponential synchronization of stochastic neural networks, a new periodically intermittent discrete observation control (PIDOC) is first proposed. Different from the existing periodically intermittent control, our co...

Reachability Analysis of Neural Masses and Seizure Control Based on Combination Convolutional Neural Network.

International journal of neural systems
Epileptic seizures arise from synchronous firing of multiple spatially separated neural masses; therefore, many synchrony measures are used for seizure detection and characterization. However, synchrony measures reflect only the overall interaction s...

Probabilistic associative learning suffices for learning the temporal structure of multiple sequences.

PloS one
From memorizing a musical tune to navigating a well known route, many of our underlying behaviors have a strong temporal component. While the mechanisms behind the sequential nature of the underlying brain activity are likely multifarious and multi-s...

Epileptic Seizure Detection with EEG Textural Features and Imbalanced Classification Based on EasyEnsemble Learning.

International journal of neural systems
Imbalance data classification is a challenging task in automatic seizure detection from electroencephalogram (EEG) recordings when the durations of non-seizure periods are much longer than those of seizure activities. An imbalanced learning model is ...

Spread of α-synuclein pathology through the brain connectome is modulated by selective vulnerability and predicted by network analysis.

Nature neuroscience
Studies of patients afflicted by neurodegenerative diseases suggest that misfolded proteins spread through the brain along anatomically connected networks, prompting progressive decline. Recently, mouse models have recapitulated the cell-to-cell tran...