14 papers · ranked by Valyu relevance
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…
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…
C. Daniel Greenidge, Benjamin Scholl, Jacob L. Yates, Jonathan W. Pillow
Neural decoding methods provide a powerful tool for quantifying the information content of neural population codes and the limits imposed by correlations in neural activity. However, standard decoding methods are prone to overfitting and scale poorly to high-dimensional settings. Here, we introduce a novel decoding…
Di Wu, Linghao Bu, Yifei Jia, Lu Cao + 11 more
Recent achievements in implantable brain-computer interfaces (iBCIs) have demonstrated the potential to decode cognitive and motor behaviors with intracranial brain recordings; however, individual physiological and electrode implantation heterogeneities have constrained current approaches to neural decoding within…
Erik Rybakken, Nils Baas, Benjamin Dunn
We introduce a novel data-driven approach to discover and decode features in the neural code coming from large population neural recordings with minimal assumptions, using cohomological learning. We apply our approach to neural recordings of mice moving freely in a box, where we find a circular feature. We then observe…
Markus Frey, Sander Tanni, Catherine Perrodin, Alice O’Leary + 6 more
Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data often depends on manual operations and requires considerable knowledge about the nature of the representation. Decoding provides a…
Seungbin Park, Megan Lipton, Maria C. Dadarlat
Two-photon imaging has been a critical tool for dissecting brain circuits and understanding brain function. However, relating slow two-photon calcium imaging data to fast behaviors has been challenging due to relatively low imaging sampling rates, thus limiting potential applications to neural prostheses. Here, we show…
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…
Mingkang Li, Ruixue Wang, Guihua Wan, Yuqi Yang + 1 more
Calcium imaging has gained extensive application in neural decoding tasks because of its high precision in observing cortical neural activity. Nevertheless, the immense data volume and complexity of automated signal extraction algorithms in calcium imaging result in significant delays in extracting neuronal calcium…
Nikhil Parthasarathy, Eleanor Batty, William Falcon, Thomas Rutten + 3 more
Decoding sensory stimuli from neural signals can be used to reveal how we sense our physical environment, and is valuable for the design of brain-machine interfaces. However, existing linear techniques for neural decoding may not fully reveal or exploit the fidelity of the neural signal. Here we develop a new…
Caroline Haimerl, Cristina Savin, Eero P. Simoncelli
Sensory-guided behavior requires reliable encoding of information (from stimuli to neural responses) and flexible decoding (from neural responses to behavior). In typical decision tasks, a small subset of cells within a large population encode task-relevant stimulus information and need to be identified by later…
Jacek P. Dmochowski, Jason Ki, Paul DeGuzman, Paul Sajda + 1 more
In neuroscience, stimulus-response relationships have traditionally been analyzed using either encoding or decoding models. Here we combined both techniques by decomposing neural activity into multiple components, each representing a portion of the stimulus. We tested this hybrid approach on encephalographic responses…
Shahrzad Latifi, Jonathan Chang, Mehdi Pedram, Roshanak Latifikhereshki + 1 more
Neuronal networks in the motor cortex are crucial for driving complex movements. Yet it remains unclear whether distinct neuronal populations in motor cortical subregions encode complex movements. Using in vivo two-photon calcium imaging (2P) on head- fixed grid-walking animals, we tracked the activity of excitatory…