Xuhui Huang

• PhD
• Professor (Associate) at Institute of Automation, Chinese Academy of Sciences

Spiking neural networks (SNNs) possess energy-efficient potential due to event-based computation. However, supervised training of SNNs remains a challenge as spike activities are non-differentiable. Previous SNNs training methods can be generally categorized into two basic classes, i.e., backpropagation-like training methods and plasticity-based le...

Spatially separated brain areas interact with each other to form networks with coordinated activities, supporting various brain functions. Interaction structures among brain areas have been widely investigated through pairwise measures. However, interactions among multiple (e.g., triple and quadruple) areas cannot be reduced to pairwise interaction...

The brain is highly plastic, with synaptic weights changing across a wide range of time scales, from hundreds of milliseconds to days. Changes occurring at different temporal scales are believed to serve different purposes, with long-term changes for learning and memory and short-term changes for adaptation and synaptic computation. By studying the...

The prefrontal cortex (PFC), which plays key roles in many higher cognitive processes, is a hierarchical system consisting of multi-scale organizations. Optimizing the working state at each scale is essential for PFC's information processing. Typical optimal working states at different scales have been separately reported, including the dopamine-me...

Spiking neural networks (SNNs) possess energy-efficient potential due to event-based computation. However, supervised training of SNNs remains a challenge as spike activities are non-differentiable. Previous SNNs training methods can basically be categorized into two classes, backpropagation-like training methods and plasticity-based learning metho...

Speech recognition (SR) has been improved significantly by artificial neural networks (ANNs), but ANNs have the drawbacks of biologically implausibility and excessive power consumption because of the nonlocal transfer of real-valued errors and weights. While spiking neural networks (SNNs) have the potential to solve these drawbacks of ANNs due to t...

Interactions among different brain regions are usually examined through functional connectivity (FC) analysis, which is exclusively based on measuring pairwise correlations in activities. However, interactions beyond the pairwise level, that is, higher-order interactions (HOIs), are vital in understanding the behavior of many complex systems. So fa...

It has been known that the working memory (WM) performance is modulated by dopamine (DA), with a characteristic inverted-U profile. That is, too strong or too weak of DA’s activation in the prefrontal cortex (PFC) would be detrimental for WM, while an intermediate level of DA activation is required to maintain an optimal WM performance. In addition...

Neural networks can generate spatiotemporal patterns of spike activity. Sequential activity learning and retrieval have been observed in many brain areas, and e.g. is crucial for coding of episodic memory in the hippocampus or generating temporal patterns during song production in birds. In a recent study, a sequential activity pattern was directly...

Phase transitions widely exist in nature and occur when some control parameters are changed. In neural systems, their macroscopic states are represented by the activity states of neuron populations, and phase transitions between different activity states are closely related to corresponding functions in the brain. In particular, phase transitions t...

The topological structures of complex networks are often very complicated and there are huge amount of data in their dynamics. It is important to extract useful information from the available data and to explore some simple reduced structures (if they exist) which play key roles determining different functions of the networks. In this paper, models...

Significance Understanding the mechanisms of how neural systems process temporal information is at the core to elucidate brain functions, such as for speech recognition and music appreciation. The present study investigates a simple yet effective mechanism for a neural system to extract the rhythmic information of external inputs in the order of se...

The phenomenon of synchronous firings is investigated in excitable small-world networks (ESWNs) of 2D lattices. Two sharply different types of patterns, wavelet turbulence (WT) patterns and synchronous firing (SF) patterns, and the associated transitions and hysteresis are found in wide parameter regions and in different excitable models. The WT st...

Chaos should occur often in gene regulatory networks (GRNs) which have been widely described by nonlinear coupled ordinary differential equations, if their dimensions are no less than 3. It is therefore puzzling that chaos has never been reported in GRNs in nature and is also extremely rare in models of GRNs. On the other hand, the topic of motifs...

The two-loop structures (TLSs). Complete set of distinctive 3-node and 4-node TLSs satisfying conditions (i) and (ii), serve as possible candidates of chaotic motifs. All the 105 TLSs are listed (4-node (1)−(86) and 3-node (87)−(105)) with the corresponding indexes used in the text and other figures. (EPS)

Exploring the principle and relationship of gene transcriptional regulations (TR) has been becoming a generally researched issue. So far, two major mathematical methods, ordinary differential equation (ODE) method and Boolean map (BM) method have been widely used for these purposes. It is commonly believed that simplified BMs are reasonable approxi...

Recently, self-sustained oscillations in complex networks consisting of non-oscillatory nodes have attracted great interest in diverse natural and social fields. Oscillatory genomic regulatory networks are one of the most typical examples of this kind. Given an oscillatory genomic network, it is important to reveal the central structure generating...

Recently, self-sustained oscillations in complex networks consisting of nonoscillatory nodes (network oscillators) have attracted great interest in diverse natural and social fields. Due to complexity of network behaviors, little is known so far about the basic structures and fundamental rules underlying the oscillations, not to mention the princip...