25 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…
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…
Shiva Subbulakshmi Radhakrishnan, Amritanand Sebastian, Aaryan Oberoi, Sarbashis Das + 1 more
Spiking neural networks (SNNs) promise to bridge the gap between artificial neural networks (ANNs) and biological neural networks (BNNs) by exploiting biologically plausible neurons that offer faster inference, lower energy expenditure, and event-driven information processing capabilities. However, implementation of…
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…
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…
Zihan Pan, Yansong Chua, Jibin Wu, Malu Zhang + 2 more
'Eliathamby Ambikairajah'] The auditory front-end is an integral part of a spiking neural network (SNN) when performing auditory cognitive tasks. It encodes the temporal dynamic stimulus, such as speech and audio, into an efficient, effective and reconstructable spike pattern to facilitate the subsequent processing.…
Lopez-Randulfe, Javier, Reeb, Nico + 2 more
Processing sensor data with spiking neural networks on digital neuromorphic chips requires converting continuous analog signals into spike pulses. Two strategies are promising for achieving low energy consumption and fast processing speeds in end-to-end neuromorphic applications. First, to directly encode analog…
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…
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…
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.…
Ahana Gangopadhyay, Kenji Aono, Darshit Mehta, Shantanu Chakrabartty
This paper builds upon our previously reported growth transform based optimization framework to present a novel spiking neuron model and demonstrate its application for spike-based auditory signal processing. Unlike conventional neuromorphic approaches, the proposed Growth Transform (GT) neuron model is tightly coupled…
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…
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…
Brian Gardner, André Grüning
Experimental studies support the notion of spike-based neuronal information processing in the brain, with neural circuits exhibiting a wide range of temporally-based coding strategies to rapidly and efficiently represent sensory stimuli. Accordingly, it would be desirable to apply spike-based computation to tackling…
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…
G. Marsat, K.C. Daly, J.A. Drew
The identity of sensory stimuli is encoded in the spatio-temporal patterns of responses of the encoding neural population. For stimuli to be discriminated reliably, differences in population responses must be accurately decoded by downstream networks. Several methods to compare patterns of responses have been used by…
Fleur Zeldenrust, Niccolò Calcini, Xuan Yan, Ate Bijlsma + 1 more
Sensory neurons reconstruct the world from action potentials (spikes) impinging on them. Recent work argues that the formation of sensory representations are cell-type specific, as excitatory and inhibitory neurons use complementary information available in spike trains to represent sensory stimuli. Here, by measuring…
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…
Anna-Maria Jürgensen, Afshin Khalili, Elisabetta Chicca, Giacomo Indiveri + 1 more
Animal nervous systems are highly efficient in processing sensory input. The neuromorphic computing paradigm aims at the hardware implementation of neural network computations to support novel solutions for building brain-inspired computing systems. Here, we take inspiration from sensory processing in the nervous…
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…
Farzaneh Asadpour, Xinwei Zhang, Mohammad Mazloum-Ardakani, Maysam Mirzaei + 2 more
We used liposomes loaded with different monoamines, dopamine (DA) and serotonin (5-HT), to simulate vesicular release and to monitor the dynamics of chemical release from isolated vesicles during vesicle impact electrochemical cytometry (VIEC). The release of DA from liposomes presents a longer release time compared to…