25 papers · ranked by Valyu relevance
Tomoya Nakai, Charlotte Constant-Varlet, Jérôme Prado
Cognitive computational neuroscience has received broad attention in recent years as an emerging area integrating cognitive science, neuroscience, and artificial intelligence. At the heart of this field, approaches using encoding models allow for explaining brain activity from latent and high-dimensional features…
Lane Lewis, Xaq Pitkow, Leila Wehbe
How does the brain process language over time? Research suggests that natural human language is processed hierarchically across brain regions over time. However, attempts to characterize this computation have thus far been limited to tightly controlled experimental settings that capture only a coarse picture of the…
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…
Zijin Gu, Keith Jamison, Mert Sabuncu, Amy Kuceyeski
Quantifying population heterogeneity in brain stimuli-response mapping may allow insight into variability in bottom-up neural systems that can in turn be related to individual’s behavior or pathological state. Encoding models that predict brain responses to stimuli are one way to capture this relationship. However…
Richard Antonello, Alexander Huth
Many recent studies have shown that representations drawn from neural network language models are extremely effective at predicting brain responses to natural language. But why do these models work so well? One proposed explanation is that language models and brains are similar because they have the same objective: to…
Erick Lamilla, Christian Sacarelo, Manuel S. Alvarez-Alvarado, Arturo Pazmino + 4 more
'Arturo Pazmino' 'Peter Iza' 'Yichuang Sun' 'Haeyoung Lee' 'Oluyomi Simpson'] Based on orbital angular momentum (OAM) properties of Laguerre-Gaussian beams LG( $p,ℓ$), a robust optical encoding model for efficient data transmission applications is designed. This paper presents an optical encoding model based on an…
Simone Blanco Malerba, Aurora Micheli, Michael Woodford, Rava Azeredo da Silveira
The efficient coding approach proposes that neural systems represent as much sensory information as biological constraints allow. It aims at formalizing encoding as a constrained optimal process. A different approach, that aims at formalizing decoding, proposes that neural systems instantiate a generative model of the…
Zihan Qin, Hongrui Zhang
Large language models are turning from isolated predictors into agentic systems: they call tools, retrieve evidence, obey environment constraints, use verifiers, and complete tasks through search and multi-turn interaction. We adopts an analytical viewpoint based on "compression is intelligence": under a fixed task…
MEHRAD SARMASHGHI, SHANTANU P. JADHAV, URI T. EDEN
Neurons can code for multiple variables simultaneously and neuroscientists are often interested in classifying neurons based on their receptive field properties. Statistical models provide powerful tools for determining the factors influencing neural spiking activity and classifying individual neurons. However, as…
Théo Desbordes, Itsaso Olasagasti, Nicolas Piron, Sophie Schwartz + 1 more
Multivariate decoding analyses have become a cornerstone method in cognitive neuroscience. When applied to time-resolved brain imaging signals, they provide insights into the temporal dynamics of information processing in the brain. In particular, the temporal generalization (TG) method—where a decoder trained at one…
Jia Lu, Ryan Tsoi, Nan Luo, Yuanchi Ha + 8 more
Dynamical systems often generate distinct outputs according to different initial conditions, and one can infer the corresponding input configuration given an output. This property captures the essence of information encoding and decoding. Here, we demonstrate the use of self-organized patterns, combined with machine…
Peili Chen, Shiji Xiang, Linyang He, Edward F. Chang + 1 more
Recent studies have explored the correspondence between single-modality DNN models (speech or text) and specific brain networks for speech and language. The key factors underlying these correlations and their spatiotemporal evolution within the brain language network remain unclear, particularly across different DNN…
Elena Bolt, Nathalie Giroud
The multivariate temporal response function (mTRF) is an effective tool for investigating the neural encoding of acoustic and complex linguistic features in natural continuous speech. In this study, we investigated how neural representations of speech features derived from natural stimuli are related to early signs of…
Authors not listed
Deep generative models are transforming early-stage drug discovery, yet most current approaches are not well suited for realistic, small-data settings and often rely on simplified molecular representations such as linear strings, overlooking the inherent graph-based structure of molecules. To address this, we first…
Haibin Wu, Xuanjun Chen, Yi‐Cheng Lin, Kai-Wei Chang + 3 more
'Alexander H. Liu' 'Hung-yi Lee'] Abstract—Neural audio codecs are initially introduced to compress audio data into compact codes to reduce transmission latency. Researchers recently discovered the potential of codecs as suitable tokenizers for converting continuous audio into discrete codes, which can be employed to…
Grégoire Delétang, Anian Ruoss, Paul-Ambroise Duquenne, Elliot Catt + 8 more
'Tim Genewein' 'Christopher Mattern' 'Jordi Grau-Moya' 'Li Kevin Wenliang' 'Matthew Aitchison' 'Laurent Orseau' 'Marcus Hütter' 'Joel Veness'] It has long been established that predictive models can be transformed into lossless compressors and vice versa. Incidentally, in recent years, the machine learning community…
Cailum Stienstra, Liam Hebert, Patrick Thomas, Alexander Haack + 2 more
Given that Infrared (IR) spectroscopy is a crucial tool in various chemical and forensic domains, improved in silico methods for predicting experimental spectra are needed due to the time and accuracy limitations of ab initio methods. We employ Graphormer, a graph neural network (GNN) transformer, to predict IR spectra…
Yibo Yang, Stephan Mandt, Lucas Theis
Neural compression is the application of neural networks and other machine learning methods to data compression. Recent advances in statistical machine learning have opened up new possibilities for data compression, allowing compression algorithms to be learned end-to-end from data using powerful generative models such…
Shengpeng Ji, Minghui Fang, Ziyue Karen Jiang, Rongjie Huang + 3 more
and Speech Language Models Authors: ['Shengpeng Ji' 'Minghui Fang' 'Ziyue Karen Jiang' 'Rongjie Huang' 'Jialung Zuo' 'Shulei Wang' 'Zhou Zhao'] In recent years, large language models have achieved significant success in generative tasks (e.g., speech cloning and audio generation) related to speech, audio, music, and…
Authors not listed
In molecular machine learning, the choice of the representation of molecules can have a significant impact on model performance. However, understanding the root causes of these performance differences often proves challenging. One promising approach to explore model behavior is representational alignment, which…
Authors not listed
Compound similarity is fundamental to various cheminformatics analyses, particularly in the drug discovery industry, where the structure-activity principle is central to medicinal chemistry. Historically, binary fingerprints combined with Tanimoto and “Tanimoto-related metrics” (such as Dice, Sørensen–Dice, and…
Dongchao Yang, Songxiang Liu, Rongjie Huang, Jinchuan Tian + 2 more
'Chao Weng' 'Yuexian Zou'] Audio codec models are widely used in audio communication as a crucial technique for compressing audio into discrete representations. Nowadays, audio codec models are increasingly utilized in generation fields as intermediate representations. For instance, AudioLM is an audio generation model…
Helle W. van den Maagdenberg, Martin Šícho, David Alencar Araripe, Sohvi Luukkonen + 9 more
Building reliable and robust quantitative structure-property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated. Second, the number of available methods is continuously growing and evaluating different algorithms and methodologies can be arduous.…
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…
Fuzhan Rahmanian, Robert M. Lee, Dominik Linzner, Kathrin Michel + 4 more
Predicting and monitoring battery life early and across chemistries is a significant challenge due to the plethora of degradation paths, form factors, and electrochemical testing protocols. Existing models typically translate poorly across different electrode, electrolyte, and additive materials, mostly require a fixed…