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Search · four archives
22 papers · ranked by Valyu relevance
Jian-wei Liu, Bingrong Xu, Zhiyan Song
Recursive and recurrent neural networks are the main realization forms of sequence models based on neural networks, which have been developed rapidly in recent years. Recurrent neural networks are basically standard processing methods for machine translation, question and answer, and sequence video analysis. It is also…
Lúcio Folly Sanches Zebendo, Eleonora Cicciarella, Michele Rossi
Spiking neural networks (SNNs) are rapidly gaining momentum as an alternative to conventional artificial neural networks in resource constrained edge systems. In this work, we continue a recent research line on recurrent SNNs where axonal delays are learned at runtime along with the other network parameters. The first…
Zhenis Otarbay, Abzal Kyzyrkanov
Motor imagery (MI) based electroencephalography (EEG) classification is central to brain-computer interface (BCI) research but practical deployment remains challenging due to poor generalization across subjects. Inter-individual variability in neural activity patterns significantly limits the development of…
Sara Varetti, Sebastian Goldt, Eugenio Piasini
In vision neuroscience, the temporal dynamics of the sensory stream and of its neural representations are thought to be deeply linked to the function of the hierarchy of cortical areas that deal with object recognition, known as the visual ventral stream. Neural representations that are invariant under…
Timothee Maniquet, Hans Op de Beeck, Andrea Ivan Costantino
Object recognition requires flexible and robust information processing, especially in view of the challenges posed by naturalistic visual settings. The ventral stream in visual cortex is provided with this robustness by its recurrent connectivity. Recurrent deep neural networks (DNNs) have recently emerged as promising…
Aakash Sarkar, Marc W. Howard
Human cognition integrates information across nested timescales. While the cortex exhibits hierarchical Temporal Receptive Windows (TRWs), local circuits often display heterogeneous time constants. To reconcile this, we trained biologically constrained deep networks, based on scale-invariant hippocampal time cells, on…
Aakash Sarkar, Marc W. Howard
Human cognition integrates information across nested timescales. While the cortex exhibits hierarchical Temporal Receptive Windows (TRWs), local circuits often display heterogeneous time constants. To reconcile this, we trained biologically constrained deep networks, based on scale-invariant hippocampal time cells, on…
Matteo De Matola, Giorgio Arcara
Convolutional neural networks (CNNs) are a class of artificial neural networks (ANNs). Since the early 2010s, they have been widely adopted as models of primate vision and classifiers of neuroimaging data, becoming relevant for a wealth of neuroscientific fields. However, the majority of neuroscience researchers come…
Lorenzo Mazzaschi, Andrew J King, Ben DB Willmore, Nicol S Harper
Memory is essential for the neural processing of natural sounds. It has been proposed that cortical memory is subserved by gated recurrency, a powerful machine learning method that enables memory of sequential dependencies. However, standard forms lack biological realism. We built a computational model using a…
Authors not listed
Metal–organic frameworks (MOFs) represent a versatile class of porous materials, yet efficiently exploring their vast chemical space for target gas adsorption properties remains a major challenge. MOFid, a text-based encoding of MOF structures, has enabled large-scale data mining using natural language processing (NLP)…
Ulysse Rançon, Timothée Masquelier, Benoit R. Cottereau
Computational models of neural processing in the auditory cortex usually ignore that neurons have an internal memory: they characterize their responses from simple convolutions with a finite temporal window. To circumvent this limitation, we propose here a new, simple and fully recurrent neural network (RNN)…
Chuang Liu, Yang Chen, Chang Seok Bang
Spiking neural networks (SNNs) have emerged as a promising paradigm for energy-efficient neuromorphic computing, particularly when processing asynchronous event streams from dynamic vision sensors (DVSs). However, SNNs often suffer from limited representational capacity and suboptimal feature recalibration compared to…
Abdullah M. Zyarah, Dhireesha Kudithipudi
—Continual learning on edge platforms remains challenging because recurrent networks depend on energy-intensive training procedures and frequent data movement that are impractical for embedded deployments. This work introduces M2RU, a mixed-signal architecture that implements the minion recurrent unit for efficient…
Sebastian Klavinskis-Whiting, Andrew J. King, Nicol S. Harper, Tim Christian Kietzmann
A major goal of neuroscience is to identify general principles that can explain the diverse structures and functions of the brain. The principle of temporal prediction provides one approach, arguing that the sensory brain is optimized to represent stimulus features that efficiently predict the immediate future input.…
Jirui Fu, Helen J Huang, Yue Wen
Convolutional neural networks (CNNs) have shown promise in decoding neural drive from high-density surface electromyography (HD-sEMG) signals. However, the effects of convolutional kernel dimensionality on the generalizability and computational efficiency of CNN-based neural drive decoding remain unclear. This study…
Authors not listed
Machine learning (ML) models have been widely used as efficient surrogates to predict adsorption in metal-organic frameworks (MOFs), for gas storage, chemical separations, and catalysis applications. The “black box” nature of these ML models, however, remains a significant barrier between predictions and the design of…
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Accurate prediction of chemical reaction yields remains essential for accelerating synthesis optimization, yet current machine learning models face critical limitations in capturing temporal dynamics, providing calibrated uncertainty estimates, and explicitly modeling reactant-to-product transformations. Here we…
Antony W. N'dri, Thomas Barbier, Céline Teulière, Jochen Triesch
The ability to predict the future is of great value for biological and artificial cognitive systems alike. However, successfully predicting the future typically requires maintaining a memory of the recent past. It is currently unclear how biological or artificial spiking neural networks can learn to maintain past…
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Inverse molecular design aims to generate novel chemical structures that satisfy multiple property constraints, yet reinforcement-learning (RL) fine-tuning can be sensitive to how objectives are converted into a scalar reward. Here, we systematically analyze how scalarization choices and stabilization mechanisms shape…
Matteo Farina, Pietro Zamberlan, Arno Onken, Ulisse Ferrari
For datasets with thousands of neurons and images, vision transformers have proven successful at predicting neural responses to stimuli. However, they are expected to underperform in low-data regimes, where CNNs and Gaussian processes are considered more effective. We ask whether transformers can be made competitive…
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Olfaction arises from the interaction of odorants with olfactory receptors, a process shaped by molecular geometry, electron distribution, and conformational preference. We present ConfDENSE, a Set2Set enhanced PointNet model that learns directly from Hirshfeld promolecule electron-density point clouds, preserving full…
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Crystal structure prediction (CSP) is a valuable computational technique used to anticipate the likely crystal structures of a compound of interest. These methods have been proven useful in research and development of pharmaceutical solid forms and in guiding the discovery of materials with targeted properties. Despite…