H.Wanhong

Paper Information

Title: An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG [1]

Authors: Mohammadali Sharifshazileh, Karla Burelo, Johannes Sarnthein, Giacomo Indiveri

Year: 2021

DOI: 10.1038/s41467-021-23342-2

Citation: Sharifshazileh, M., Burelo, K., Sarnthein, J.et al.An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG.Nat Commun12**, 3095 (2021). https://doi.org/10.1038/s41467-021-23342-2

Brief Introduction

High Frequency Oscillations (HFO) phenomenon in the context of EEG signal processing tasks refers to the observation of EEG at $80$-$500$ Hz from brain activity [1].HFO has shown a correlation with seizures in epileptic disorders, and has been utilized in several applications as a detection of epileptogenic zone as a kind of biomarkers.

In biological neural networks, the membrane potential of nerve cells changes over time and generates action potentials (spikes) under certain conditions. These spikes propagate through the synapse to the postsynaptic neuron. Artificial neural networks that simulate such behavior are also known as Spiking Neural Network (SNN).

Title: The Grammar of the Ising Model: A New Complexity Hierarchy

Authors: Tobias Reinhart, Gemma De las Cuevas

Citation: Tobias Reinhart, & Gemma De las Cuevas (2022). The Grammar of the Ising Model: A New Complexity Hierarchy.

To explore the complexity of the Ising model, an effective approach is to analyze the complexity of its Ground State Energy (GSE) problem. Specifically, the GSE problem is defined as follows: given an interaction graph and a specific energy value, determine whether there exists an Ising spin configuration such that the system’s energy is lower than the given value.

The decidability of the GSE problem fundamentally depends on the planarity of the interaction graph of Ising sites, which divides the complexity of the Ising model into two categories. However, this classification method only considers the planarity of the interaction graph, which has certain limitations. To address this, Tobias Reinhart and colleagues, in their study, proposed an analysis method for the Ising model based on formal language modeling, from the perspectives of formal languages, generative grammars, and computational complexity theory. By associating the language of the Ising model with its position in the Chomsky hierarchy, they classified the complexity of the Ising model. Additionally, the study provided detailed discussions and proofs of related theorems and the complexity of seven Ising models as learning cases.

The ising model energy is defined as:
$$
E(\sigma) = -\sum_{ij}J_{ij}\sigma_i\sigma_j - \sum_{i} H_i\sigma_i
$$
In which, $\sigma_i = {0, 1}$ in this work.

Let $\mathcal S$ is the set of all possible configuration. $|\mathcal S| = 2^n$,

where $n$ is the number of sites, and is equal to the length of $\sigma$.

In maximum entropy principle, we expect to maximize the entropy $S(p)=-\sum_\sigma p(\sigma)logp(\sigma)$, in the constraints that

$\langle \sigma_i \sigma_j\rangle^{emp} = \langle \sigma_i \sigma_j\rangle$, $\langle \sigma_i\rangle^{emp}=\langle \sigma_i\rangle$ and $\sum_\sigma p(\sigma) = 1$.

Combine to the Lagrange function:

$\mathcal L(p;J;H)=S(p) -\lambda((\sum_\sigma p(\sigma))-1)-\sum_{ij}J_{ij}(\langle \sigma_i \sigma_j\rangle -\langle \sigma_i \sigma_j\rangle^{emp})-\sum_{j}H_{i}(\langle \sigma_i \rangle -\langle \sigma_i\rangle^{emp})$​

Thank you for visiting this site ^_^.

I am currently an independent researcher conducting interdisciplinary studies across the social sciences, nomological sciences,natural sciences, and engineering.

My work includes, for example, applying natural language processing techniques to electroencephalogram (EEG) analysis, exploring modern nomology and the philosophy of law, and developing field-Theoretic models of structural emergence and dynamics.

My major interests are in domain of philosophy, linguistics (especially computational linguistics), neuroscience and social and nomological sciences.

More specifically, my research interests involve following keywords:

• Field-Theoretic Physics, Statistical Physics, and Dynamical Systems
• Philosophy
• Social and Nomological Sciences
• Statistical Learning and Deep Learning
• Emerging Computer Architectures
• Emerging Computing Paradigms (e.g., neuromorphic computing)
• Computational Linguistics
• Programming Language Theory and Programming Language Processing Systems
• Optimization Algorithms
• Bio-signal Processing

I am engaged in interdisciplinary research and study across the social sciences, nomological sciences, natural sciences, and engineering to better understand human beings and the world. My interests also include developing applications in healthcare, communication enhancement, well-being, policymaking, nomological development, and related fields.

This website aims to share some of my research discoveries and personal reflections.
Thank you again for taking the time to browse this site.

Best wishes,
Wanhong HUANG
November 7, 2025

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Email: huangwanhong.g.official@gmail.com

Chinese link: https://zhuanlan.zhihu.com/p/717409759

1. Temporal Event Encoding in Dynamical Systems

Time is the basis of many interesting human behaviors [1].

In connectionist methods, such as neural networks, explicitly encoding temporal events based on their absolute positions can pose problems for recognizing similar patterns. A similar example of this issue is pointed out in work [1]. Pattern vectors $[0,0,0,1,1,1,0,0]$ and $[0,1,1,1,0,0,0,0]$ (where $1$ represent temporal events) exhibit similar patterns, even though the relative positions of the temporal events differ. However, the explicit encoding of absolute positions causes these vectors to have large differences.

Moreover, since it is difficult to explain how biological systems might use mechanisms similar to shift registers to process patterns with differences in relative positions, this encoding method lacks some degree of biological interpretability [1].

In Jeffrey L. Elman et al.’s work in 1990 [1], a method was proposed for implicitly representing the influence of time on temporal data processing during sequence processing. Specifically, a recurrent neural network was used, where the network’s internal hidden states encoded input events at each moment.