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Models, Neurological

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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 ...

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...

Using intersection information to map stimulus information transfer within neural networks.

Bio Systems
Analytical tools that estimate the directed information flow between simultaneously recorded neural populations, such as directed information or Granger causality, typically focus on measuring how much information is exchanged between such population...

Integrating Flexible Normalization into Midlevel Representations of Deep Convolutional Neural Networks.

Neural computation
Deep convolutional neural networks (CNNs) are becoming increasingly popular models to predict neural responses in visual cortex. However, contextual effects, which are prevalent in neural processing and in perception, are not explicitly handled by cu...

Adversarial Feature Alignment: Avoid Catastrophic Forgetting in Incremental Task Lifelong Learning.

Neural computation
Humans are able to master a variety of knowledge and skills with ongoing learning. By contrast, dramatic performance degradation is observed when new tasks are added to an existing neural network model. This phenomenon, termed , is one of the major r...

Mutual Inhibition with Few Inhibitory Cells via Nonlinear Inhibitory Synaptic Interaction.

Neural computation
In computational neural network models, neurons are usually allowed to excite some and inhibit other neurons, depending on the weight of their synaptic connections. The traditional way to transform such networks into networks that obey Dale's law (i....

Spiking Neural Networks and online learning: An overview and perspectives.

Neural networks : the official journal of the International Neural Network Society
Applications that generate huge amounts of data in the form of fast streams are becoming increasingly prevalent, being therefore necessary to learn in an online manner. These conditions usually impose memory and processing time restrictions, and they...

Locally connected spiking neural networks for unsupervised feature learning.

Neural networks : the official journal of the International Neural Network Society
In recent years, spiking neural networks (SNNs) have demonstrated great success in completing various machine learning tasks. We introduce a method for learning image features with locally connected layers in SNNs using a spike-timing-dependent plast...

Robust computation with rhythmic spike patterns.

Proceedings of the National Academy of Sciences of the United States of America
Information coding by precise timing of spikes can be faster and more energy efficient than traditional rate coding. However, spike-timing codes are often brittle, which has limited their use in theoretical neuroscience and computing applications. He...

Moving in time: Simulating how neural circuits enable rhythmic enactment of planned sequences.

Neural networks : the official journal of the International Neural Network Society
Many complex actions are mentally pre-composed as plans that specify orderings of simpler actions. To be executed accurately, planned orderings must become active in working memory, and then enacted one-by-one until the sequence is complete. Examples...