24 papers · ranked by Valyu relevance
Lei Guo, Nancheng Ma, Zhuoxuan Wang, Rumeng Liu + 1 more
Spiking neural networks (SNNs) offer inherent advantages in processing temporal information. However, their network topologies are predominantly algorithm-generated, lacking constraints from biological brain connectivity, which limits their bio-plausibility. In our previous work, we constructed a spiking neural network…
Ahmad El Ferdaoussi, Éric Plourde, Jean Rouat
—The problem of spike encoding of sound consists in transforming a sound waveform into spikes. It is of interest in many domains, including the development of audio-based spiking neural networks, where it is the first and most crucial stage of processing. Many algorithms have been proposed to perform spike encoding of…
Alexandru Vasilache, Jona Scholz, Vincent Schilling, Sven Nitzsche + 3 more
'Florian Kaelber' 'Johannes Korsch' 'Juergen Becker'] Abstract—Spiking Neural Networks (SNNs) offer promising energy efficiency advantages, particularly when processing sparse spike trains. However, their incompatibility with traditional datasets, which consist of batches of input vectors rather than spike trains…
Sidi Yaya Arnaud Yarga, Jean Rouat, Sean U. N. Wood
Spiking Neural Networks (SNN) encode information in an eventdriven fashion using spikes that are dynamically transmitted between neurons in the network. Recent work in neuromorphic computing has demonstrated that SNNs can result in significantly lower power requirements compared to traditional state of the art deep…
Lei Zhang
—This paper presents a novel approach for signal reconstruction using Spiking Neural Networks (SNN) based on the principles of Cognitive Informatics and Cognitive Computing. The proposed SNN leverages the Discrete Fourier Transform (DFT) to represent and reconstruct arbitrary time series signals. By employing N spiking…
Lingsheng Meng, Kaushik Jayaram, Jean-Michel Mongeau
Tactile sensing enables humans and animals to detect and discriminate features during exploration and guide context appropriate actions. Compared to conventional touch sensors, sensing of tactile features in animals is fundamentally event-based through spikes. Yet how sensor mechanics shape spike activity for tactile…
Boris Sotomayor-Gómez, Francesco P. Battaglia, Martin Vinck
Information in the nervous system is encoded by the spiking patterns of large populations of neurons. The analysis of such high-dimensional data is typically restricted to simple, arbitrarily defined features like spike rates, which discards information in the temporal structure of spike trains. Here, we use a recently…
Evelina Forno, Vittorio Fra, Riccardo Pignari, Enrico Macii + 1 more
Spiking Neural Networks (SNNs), known for their potential to enable low energy consumption and computational cost, can bring significant advantages to the realm of embedded machine learning for edge applications. However, input coming from standard digital sensors must be encoded into spike trains before it can be…
Stefano Panzeri, Nicola Marie Engel, Marco Celotto
The publication of Mainen and Sejnowski’s 1995 seminal paper strongly renewed interest in how spike timing contributes to the neural code. In the 3 decades since then, considerable experimental and theoretical research has investigated the timescales at which spike timing contributes to the neural code. Here we review…
Fang Wang, Xiaoqiang Liang, Xingqian Du, Mariusz Szwoch
In this paper, we explore the spiking encoding methodology within spiking neural networks for affective state recognition, deriving inspiration from the principles of quantum entanglement. A pioneering encoding strategy is proposed based on the strategic utilization of the quantum mechanical phenomenon of entanglement.…
Wilten Nicola, Thomas Robert Newton, Claudia Clopath
Precisely timed and reliably emitted spikes are hypothesized to serve multiple functions, including improving the accuracy and reproducibility of encoding stimuli, memories, or behaviours across trials. When these spikes occur as a repeating sequence, they can be used to encode and decode a potential time series. Here…
Ranxi Lin, Benzhe Dai, Yingkai Zhao, Gang Chen + 2 more
'Muthuraman Muthuraman'] In recent years, a third-generation neural network, namely, spiking neural network, has received plethora of attention in the broad areas of Machine learning and Artificial Intelligence. In this paper, a novel differential-based encoding method is proposed and new spike-based learning rules for…
Yiwen Gu, J.C. Gu, Haibin Shen, Kejie Huang
Spiking Neural Networks (SNNs) seek to mimic the spiking behavior of biological neurons and are expected to play a key role in the advancement of neural computing and artificial intelligence. The efficiency of SNNs is often determined by the neural coding schemes. Existing coding schemes either cause huge delays and…
MHD Anas Alsakkal, Runze Wang, Jayawan Wijekoon, Piotr Dudek
Hardware Details and Real-life Applications Authors: ['MHD Anas Alsakkal' 'Runze Wang' 'Jayawan Wijekoon' 'Piotr Dudek'] Abstract— Spiking Neural Networks (SNNs) offer a biologically inspired computational paradigm, enabling energyefficient data processing through spike-based information transmission. Despite notable…
Pietro Savazzi, Anna Vizziello, Fabio Dell’Acqua
—Wireless Spiking neural networks (WSNNs) allow energy-efficient device-to-device (D2D) or vehicle-to-everything (V2X) communications, especially while considering edge intelligence and learning for beyond 5G and 6G systems. Recent research work has revealed that distributed wireless SNNs (DWSNNs) show good performance…
Yizi Zhang, Tianxiao He, Julien Boussard, Charlie Windolf + 9 more
Neural decoding and its applications to brain computer interfaces (BCI) are essential for understanding the association between neural activity and behavior. A prerequisite for many decoding approaches is spike sorting, the assignment of action potentials (spikes) to individual neurons. Current spike sorting…
Thach V. Bui
Neural coding is an important tool to discover the inner workings of mind. In this work, we propose and consider a simple but novel self-decoding model for neural coding based on the principle that the neuron body represents ongoing stimulus while dendrites are used to store that stimulus as a memory. In particular…
Alejandro Juárez-Lora, Luis M. García-Sebastián, Victor H. Ponce-Ponce, Elsa Rubio-Espino + 3 more
'Victor H. Ponce-Ponce' 'Elsa Rubio-Espino' 'Herón Molina-Lozano' 'Humberto Sossa' 'Miguel Ángel Conde'] A Kalman filter can be used to fill space-state reconstruction dynamics based on knowledge of a system and partial measurements. However, its performance relies on accurate modeling of the system dynamics and a…
Nima Maleki, Hamid Karimi-Rouzbahani
Sensory neural coding, the brain’s process of transforming inputs into informative patterns of neural activity, generates complex and multiplexed neural codes which are hard to interpret. Although decoding methods have facilitated the interpretation of these codes, the specific features of neural activity that…
Aakansha Nangarlia, Farah Fazloon Hassen, Gabriela Canziani, Praneeta Bandi + 10 more
Host cell infection by SARS-CoV-2, similar to that by HIV-1, is driven by a conformationally metastable and highly glycosylated surface entry protein complex, and infection by these viruses has been shown to be inhibited by the mannose-specific lectins Cyanovirin-N (CV-N) and Griffithsin (GRFT). We discovered in this…
Jun Liu, Arron F Hall, Dong V Wang
Memories are crucial for our daily lives, yet the network-level organizing principle that governs neural representations of our experiences remains to be determined. Employing dual-site electrophysiology recording in freely behaving mice, we discovered that hippocampal dorsal CA1 (dCA1) and basolateral amygdala (BLA)…
Yuanmo Wang, Ajay Pradhan, Pankaj Gupta, Jörg Hanrieder + 2 more
Acetylcholine (ACh) is a critical neurotransmitter influencing various neurophysiological functions. Despite its significance, there is a lack of quantitative methods with adequate spatiotemporal resolution for recording single exocytotic efflux of ACh. In this study, we present an ultrafast amperometric ACh biosensor…
Authors not listed
Acetylcholine (ACh) is a key neurotransmitter involved in cognitive function, motor control, and synaptic modulation, yet its electrochemical inactivity and the rapid kinetics of exocytosis have hindered real-time quantal detection. Previous micrometer-scale enzymatic ACh biosensors enabled sub-millisecond…
Authors not listed
Carbon fiber has become the standard electrode material to study neuronal communication. For techniques such as single-cell amperometry at synapses and intracellular recordings of neurotransmitters in secretory vesicles, nanometric electrodes are required. The smaller electrode dimension also offers the added benefit…