12 papers · ranked by Valyu relevance
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…
John A. Berkowitz, Tatyana O. Sharpee
Cortical tissue has a circuit motif termed the cortical column, which is thought to represent its basic computational unit but whose function remains unclear. Here we propose, and show quantitative evidence, that the cortical column performs computations necessary to decode incoming neural activity with minimal…
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…
Jingcheng Zhang, Lei Chen, Jinlin Sun, Shumin Li + 5 more
DNA has emerged as a compelling archival storage medium, offering unprecedented information density and millennia-scale durability. Despite its promise, DNA-based data storage faces critical challenges due to error-prone processes during DNA synthesis, storage, and sequencing. In this study, we introduce Gungnir, a…
Dylan Le, Xue-Xin Wei
Understanding how correlated neural noise affects neural population coding is a basic question in computational and systems neuroscience [1, 2, 3, 4]. Recent theoretical work suggests that shared noise along the stimulus encoding direction is the primary factor that limits information encoding (i.e.…
Gabriel Matías Lorenz, Nicola M. Engel, Marco Celotto, Loren Kocillari + 3 more
Information theory has deeply influenced the conceptualization of brain information processing and is a mainstream framework for analyzing how neural networks in the brain process information to generate behavior. Information theory tools have been initially conceived and used to study how information about sensory…
Kyle Bojanek, Baptiste Lefebvre, Jared Salisbury, Olivier Marre + 1 more
Efficient coding theory postulates that a sensory system maximizes information between its response and the input, yet it is unclear if a different measure of optimality that takes into account output function might give a better fit to neural data. The sensory processing delays in many systems suggest that the…
Jason R. Climer, Daniel A. Dombeck
Information theoretic metrics have proven highly useful to quantify the relationship between behaviorally relevant parameters and neuronal activity with relatively few assumptions. However, such metrics are typically applied to action potential recordings and were not designed for the slow timescales and variable…
Roberto Maffulli, Miguel A. Casal, Marco Celotto, Stefano Zucca + 3 more
Information theory provides a popular and principled framework for the analysis of neural data. It allows to uncover in an assumption-free way how neurons encode and transmit information, capturing both linear and non-linear coding mechanisms and including the information carried by interactions of any order. To…
Julie E. Elie, Frédéric E. Theunissen
Although information theoretic approaches have been used extensively in the analysis of the neural code, they have yet to be used to describe how information is accumulated in time while sensory systems are categorizing dynamic sensory stimuli such as speech sounds or visual objects. Here, we present a novel method to…
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…
Xinhao Fan, Shreesh P Mysore
A cornerstone of our understanding of both biological and artificial neural networks is that they store information in the strengths of connections among the constituent neurons. However, in contrast to the well-established theory for quantifying information encoded by the firing patterns of neural networks, little is…