14 papers · ranked by Valyu relevance
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…
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…
Ganchao Wei, Zeinab Tajik Mansouri, Xiaojing Wang, Ian H. Stevenson
Accurately decoding external variables from observations of neural activity is a major challenge in systems neuroscience. Bayesian decoders, that provide probabilistic estimates, are some of the most widely used. Here we show how, in many common settings, the probabilistic predictions made by traditional Bayesian…
Yizi Zhang, Hanrui Lyu, Cole Hurwitz, Shuqi Wang + 5 more
Traditional neural decoders model the relationship between neural activity and behavior within individual trials of a single experimental session, neglecting correlations across trials and sessions. However, animals exhibit similar neural activities when performing the same behavioral task, and their behaviors are…
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…
Fabio Hedayioglu, Emma J. Mead, Sathishkumar Kurusamy, James E.D. Thaventhiran + 3 more
The codon sequence of messenger RNAs affects ribosome dynamics, translational control, and transcript stability. Here we describe an advanced computational modelling tool and its application to studying the effect of different tRNA species on the codon decoding process. We show that simulated codon decoding times are…
Lorenzo Posani
Neural decoding is a powerful approach for inferring which variables are represented in the activity of a population of neurons, with broad applications ranging from basic neuroscience to clinical settings such as brain-computer interfaces. More recently, decoding has also been used as a cross-validated tool for…
Thirza Dado, Lynn Le, Marcel van Gerven, Yağmur Güçlütürk + 1 more
Attention mechanisms enhance deep learning models by focusing on the most relevant parts of the input data. We introduce predictive attention mechanisms (PAMs) – a novel approach that dynamically derives queries during training which is beneficial when predefined queries are unavailable. We applied PAMs to neural…
Ethan M. Meyers
Neural decoding is a powerful method to analyze neural activity. However, the code needed to run a decoding analysis can be complex, which can present a barrier to using the method. In this paper we introduce a package that makes it easy to perform decoding analyses in the R programing language. We describe how the…
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…
Alicia Zeng, Jack Gallant
Encoding models based on word embeddings or artificial neural network (ANN) features reliably predict brain responses to naturalistic stimuli but remain difficult to interpret. A central limitation is superposition—the entanglement of distinct semantic features along correlated directions in dense embeddings, which…
Rishav Ray, Julin N. Maloof, Troy S. Magney
Leaf reflectance spectra are emerging as a viable substitute for gas-exchange measurements of photosynthetic capacity, with a community benchmark reporting that a spectrum accurately recovers most Farquhar–von Caemmerer–Berry (FvCB) parameters. This study re-scores the recovery under dataset-blocked, species-blocked…
Kevin J. Miller, Maria Eckstein, Matthew M. Botvinick, Zeb Kurth-Nelson
Computational cognitive models are a fundamental tool in behavioral neuroscience. They instantiate in software precise hypotheses about the cognitive mechanisms underlying a particular behavior. Constructing these models is typically a difficult iterative process that requires both inspiration from the literature and…
Alan Veliz-Cuba, Stephen Randal Voss, David Murrugarra
A primary challenge in building predictive models from temporal data is selecting the appropriate network and the regulatory functions that describe the data. Software packages are available for equation learning of continuous models, but not for discrete models. In this paper we introduce a method for building model…