25 papers · ranked by Valyu relevance
Amirhossein Tavanaei, Masoud Ghodrati, Saeed Reza Kheradpisheh, Timothée Masquelier + 1 more
'Timothée Masquelier' 'Anthony S. Maida'] Abstract—1 In recent years, deep learning has revolutionized the field of machine learning, for computer vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is trained in a supervised manner using backpropagation. Vast amounts of labeled…
Biswadeep Chakraborty, Saibal Mukhopadhyay
Spiking Neural Networks are often touted as brain-inspired learning models for the third wave of Artificial Intelligence. Although recent SNNs trained with supervised backpropagation show classification accuracy comparable to deep networks, the performance of unsupervised learning-based SNNs remains much lower. This…
Wenrui Zhang, Hejia Geng, Peng Li
In neural circuits, recurrent connectivity plays a crucial role in network function and stability. However, existing recurrent spiking neural networks (RSNNs) are often constructed by random connections without optimization. While RSNNs can produce rich dynamics that are critical for memory formation and learning…
Bojian Yin, Federico Corradi, Sander M. Bohté
Inspired by more detailed modeling of biological neurons, Spiking neural networks (SNNs) have been investigated both as more biologically plausible and potentially more powerful models of neural computation, and also with the aim of extracting biological neurons’ energy efficiency; the performance of such networks…
Guillaume Bellec, Franz Scherr, Anand Subramoney, Elias Hajek + 3 more
Recurrently connected networks of spiking neurons underlie the astounding information processing capabilities of the brain. But in spite of extensive research, it has remained open how learning through synaptic plasticity could be organized in such networks. We argue that two pieces of this puzzle were provided by…
Amadeus Maes, Mauricio Barahona, Claudia Clopath
Learning to produce spatiotemporal sequences is a common task the brain has to solve. The same neural substrate may be used by the brain to produce different sequential behaviours. The way the brain learns and encodes such tasks remains unknown as current computational models do not typically use realistic…
Hananel Hazan, Daniel J. Saunders, Hassaan Furqan Khan, Devdhar Patel + 3 more
'Devdhar Patel' 'Darpan Sanghavi' 'Hava T. Siegelmann' 'Róbert Kozma'] The development of spiking neural network simulation software is a critical component enabling the modeling of neural systems and the development of biologically inspired algorithms. Existing software frameworks support a wide range of neural…
Yannan Xing, Gaetano Di Caterina, John Soraghan
The combination of neuromorphic visual sensors and spiking neural network offers a high efficient bio-inspired solution to real-world applications. However, processing event- based sequences remains challenging because of the nature of their asynchronism and sparsity behavior. In this paper, a novel spiking…
Priyadarshini Panda, Kaushik Roy
Synaptic Plasticity, the foundation for learning and memory formation in the human brain, manifests in various forms. Here, we combine the standard spike timing correlation based Hebbian plasticity with a non-Hebbian synaptic decay mechanism for training a recurrent spiking neural model to generate sequences. We show…
Bojian Yin, Federico Corradi, Sander M. Bohté
The emergence of brain-inspired neuromorphic computing as a paradigm for edge AI is motivating the search for high-performance and efficient spiking neural networks to run on this hardware. However, compared to classical neural networks in deep learning, current spiking neural networks lack competitive performance in…
Wenrui Zhang, Peng Li
As an important class of spiking neural networks (SNNs), recurrent spiking neural networks (RSNNs) possess great computational power and have been widely used for processing sequential data like audio and text. However, most RSNNs suffer from two problems. 1. Due to a lack of architectural guidance, random recurrent…
Bojian Yin, Federico Corradi, Sander M. Bohté
Inspired by more detailed modeling of biological neurons, Spiking neural networks (SNNs) have been investigated both as more biologically plausible and potentially more powerful models of neural computation, and also with the aim of extracting biological neurons' energy efficiency; the performance of such networks…
Alberto Patiño-Saucedo, Horacio Rostro-González, Teresa Serrano-Gotarredona, Bernabé Linares-Barranco
Liquid State Machines (LSMs) are computing reservoirs composed of recurrently connected Spiking Neural Networks which have attracted research interest for their modeling capacity of biological structures and as promising pattern recognition tools suitable for their implementation in neuromorphic processors, benefited…
Philippe Vincent-Lamarre, Matias Calderini, Jean-Philippe Thivierge
Many cognitive and behavioral tasks – such as interval timing, spatial navigation, motor control and speech – require the execution of precisely-timed sequences of neural activation that cannot be fully explained by a succession of external stimuli. We show how repeatable and reliable patterns of spatiotemporal…
Ceca Kraišniković, Wolfgang Maass, Robert Legenstein
The brain uses recurrent spiking neural networks for higher cognitive functions such as symbolic computations, in particular, mathematical computations. We review the current state of research on spike-based symbolic computations of this type. In addition, we present new results which show that surprisingly small…
Shengjie Zheng, Lang Qian, Pingsheng Li, Chenggang He + 2 more
'Xiaojian Li'] Abstract—Recently, stemming from the rapid development of artificial intelligence, which has gained expansive success in pattern recognition, robotics, and bioinformatics, neuroscience is also gaining tremendous progress. A kind of spiking neural network with biological interpretability is gradually…
Mohamed Sadek Bouanane, Dalila Cherifi, Elisabetta Chicca, Lyes Khacef
'Lyes Khacef'] Spiking neural networks coupled with neuromorphic hardware and event-based sensors are getting increased interest for low-latency and low-power inference at the edge. However, multiple spiking neuron models have been proposed in the literature with different levels of biological plausibility and…
H. Francis Song, Guangyu R. Yang, Xiao-Jing Wang, Olaf Sporns
The ability to simultaneously record from large numbers of neurons in behaving animals has ushered in a new era for the study of the neural circuit mechanisms underlying cognitive functions. One promising approach to uncovering the dynamical and computational principles governing population responses is to analyze…
Stanisław Woźniak, Angeliki Pantazi, Evangelos Eleftheriou
Neural networks have become the key technology of artificial intelligence and have contributed to breakthroughs in several machine learning tasks, primarily owing to advances in deep learning applied to Artificial Neural Networks (ANNs). Simultaneously, Spiking Neural Networks (SNNs) incorporating biologically-feasible…
Κάρολος-Αλέξανδρος Τσάκαλος, Georgios Ch. Sirakoulis, Andrew Adamatzky, James E. Smith
'Andrew Adamatzky' 'James E. Smith'] Nowadays we witness a miniaturisation trend in the semiconductor industry backed up by groundbreaking discoveries and designs in nanoscale characterisation and fabrication. To facilitate the trend and produce ever smaller, faster and cheaper computing devices, the size of…
Xueyuan She, Saurabh Dash, Daehyun Kim, Saibal Mukhopadhyay
This paper introduces a heterogeneous spiking neural network (H-SNN) as a novel, feedforward SNN structure capable of learning complex spatiotemporal patterns with spike-timing-dependent plasticity (STDP) based unsupervised training. Within H-SNN, hierarchical spatial and temporal patterns are constructed with…
Sanjar Adilov
Generative neural networks have shown promising results in de novo drug design. Recent studies suggest that one of the efficient ways to produce novel molecules matching target properties is to model SMILES sequences using deep learning in a way similar to language modeling in natural language processing. In this…
Rongpei Gou, Jingyi Yang, Menghan Guo, Yingjun Chen + 1 more
Central nervous system (CNS) drugs have had a significant impact on human health, e.g., treating a wide range of neurodegenerative and psychiatric disorders. In recent years, deep learning-based generative models, particularly those for designing drugs from scratch, have shown great potential for accelerating drug…
Authors not listed
This research presents a novel approach to obstacle detection during navigation using a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The primary objective is to generate accurate image captions that describe the content of images, which is crucial for applications such…
Morgan Thomas, Noel M. O'Boyle, Andreas Bender, Chris de Graaf
A plethora of AI-based techniques now exists to conduct de novo molecule generation that can devise molecules conditioned towards a particular endpoint in the context of drug design. One popular approach is using reinforcement learning to update a recurrent neural network or language-based de novo molecule generator.…