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Long- and short-term history effects in a spiking network model of statistical learning.

Scientific reports
The statistical structure of the environment is often important when making decisions. There are multiple theories of how the brain represents statistical structure. One such theory states that neural activity spontaneously samples from probability d...

Sparser spiking activity can be better: Feature Refine-and-Mask spiking neural network for event-based visual recognition.

Neural networks : the official journal of the International Neural Network Society
Event-based visual, a new visual paradigm with bio-inspired dynamic perception and μs level temporal resolution, has prominent advantages in many specific visual scenarios and gained much research interest. Spiking neural network (SNN) is naturally s...

Intrinsic neural diversity quenches the dynamic volatility of neural networks.

Proceedings of the National Academy of Sciences of the United States of America
Heterogeneity is the norm in biology. The brain is no different: Neuronal cell types are myriad, reflected through their cellular morphology, type, excitability, connectivity motifs, and ion channel distributions. While this biophysical diversity enr...

Stimulation-mediated reverse engineering of silent neural networks.

Journal of neurophysiology
Reconstructing connectivity of neuronal networks from single-cell activity is essential to understanding brain function, but the challenge of deciphering connections from populations of silent neurons has been largely unmet. We demonstrate a protocol...

Targeting operational regimes of interest in recurrent neural networks.

PLoS computational biology
Neural computations emerge from local recurrent neural circuits or computational units such as cortical columns that comprise hundreds to a few thousand neurons. Continuous progress in connectomics, electrophysiology, and calcium imaging require trac...

Evolution-communication spiking neural P systems with energy request rules.

Neural networks : the official journal of the International Neural Network Society
Evolution-communication spiking neural P systems with energy request rules (ECSNP-ER systems) are proposed and developed as a new variant of evolution-communication spiking neural P systems. In ECSNP-ER systems, in addition to spike-evolution rules a...

Coherent noise enables probabilistic sequence replay in spiking neuronal networks.

PLoS computational biology
Animals rely on different decision strategies when faced with ambiguous or uncertain cues. Depending on the context, decisions may be biased towards events that were most frequently experienced in the past, or be more explorative. A particular type o...

Effective and efficient neural networks for spike inference from calcium imaging.

Cell reports methods
Calcium imaging provides advantages in monitoring large populations of neuronal activities simultaneously. However, it lacks the signal quality provided by neural spike recording in traditional electrophysiology. To address this issue, we developed a...

A deep learning network based on CNN and sliding window LSTM for spike sorting.

Computers in biology and medicine
Spike sorting plays an essential role to obtain electrophysiological activity of single neuron in the fields of neural signal decoding. With the development of electrode array, large numbers of spikes are recorded simultaneously, which rises the need...