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Search · four archives
12 papers · ranked by Valyu relevance
Shawn M. Willett, Jennifer M. Groh
How we distinguish multiple simultaneous stimuli is uncertain, particularly given that such stimuli sometimes recruit largely overlapping populations of neurons. One commonly proposed hypothesis is that the sharpness of tuning curves might change to limit the number of stimuli driving any given neuron when multiple…
Lior Galanti, Dennis Shasha, Kristin C. Gunsalus
Output from high throughput sequencing instruments often exceeds what is necessary to assay a single sample. To better utilize this capacity, multiple samples are independently tagged with a unique “barcode” sequence and are then pooled, or “multiplexed”, and sequenced together. Classifying, or “demultiplexing”, the…
Jason Hays, Fabian A. Soto
The use of population encoding models has come to dominate the study of human visual neuroscience, serving as a primary tool for making inferences about neural code changes based on indirect measurements. A popular approach in computational neuroimaging is to use such models to obtain estimates of neural population…
Edgar Y. Walker, R. James Cotton, Wei Ji Ma, Andreas S. Tolias
Bayesian models of behavior suggest that organisms represent uncertainty associated with sensory variables. However, the neural code of uncertainty remains elusive. A central hypothesis is that uncertainty is encoded in the population activity of cortical neurons in the form of likelihood functions. We studied the…
W. Jeffrey Johnston, Stephanie E. Palmer, David J. Freedman
Neuronal activity in the brain is variable, yet both perception and behavior are generally reliable. How does the brain achieve this? Here, we show that the conjunctive coding of multiple stimulus features, commonly known as nonlinear mixed selectivity, may be used by the brain to support reliable information…
Abed Ghanbari, Ian Stevenson
Neural responses to repeated presentations of an identical stimulus often show substantial trial-to-trial variability. Although the mean firing rate in response to different stimuli or during different movements (tuning curves) have been extensively modeled, the variability of neural responses can also have clear…
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…
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…
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…
J. Brendan Ritchie, David Michael Kaplan, Colin Klein
Since its introduction, multivariate pattern analysis (MVPA), or “neural decoding”, has transformed the field of cognitive neuroscience. Underlying its influence is a crucial inference, which we call the Decoder’s Dictum: if information can be decoded from patterns of neural activity, then this provides strong evidence…
Il Memming Park, Jonathan W. Pillow
The efficient coding hypothesis, which proposes that neurons are optimized to maximize information about the environment, has provided a guiding theoretical framework for sensory and systems neuroscience. More recently, a theory known as the Bayesian Brain hypothesis has focused on the brain’s ability to integrate…