Search Results for author:
Found 9 papers, 4 papers with code
Infomorphic networks: Locally learning neural networks derived from partial information decomposition
Understanding the intricate cooperation among individual neurons in performing complex tasks remains a challenge to this date.
A Measure of the Complexity of Neural Representations based on Partial Information Decomposition
However, the specific way in which this mutual information about the classification label is distributed among the individual neurons is not well understood: While parts of it may only be obtainable from specific single neurons, other parts are carried redundantly or synergistically by multiple neurons.
Intrinsic timescales of spiking activity in humans during wakefulness and sleep
Information processing in the brain requires integration of information over time.
Dendritic predictive coding: A theory of cortical computation with spiking neurons
However, experimental evidence for error units, which are central to the theory, is inconclusive, and it remains unclear how hPC can be implemented with spiking neurons.
Information-theoretic analyses of neural data to minimize the effect of researchers' assumptions in predictive coding studies
We demonstrate our approach by investigating two opposing accounts of predictive coding-like processing strategies, where we quantify the building blocks of predictive coding, namely predictability of inputs and transfer of information, by local active information storage and local transfer entropy.
A Rigorous Information-Theoretic Definition of Redundancy and Relevancy in Feature Selection Based on (Partial) Information Decomposition
We argue that this lack is inherent to classical information theory which does not provide measures to decompose the information a set of variables provides about a target into unique, redundant, and synergistic contributions.
Bits and Pieces: Understanding Information Decomposition from Part-whole Relationships and Formal Logic
In this paper we show, first, that the entire theory of partial information decomposition can be derived from considerations of elementary parthood relationships between information contributions.
Inferring COVID-19 spreading rates and potential change points for case number forecasts
As COVID-19 is rapidly spreading across the globe, short-term modeling forecasts provide time-critical information for decisions on containment and mitigation strategies.
Introducing a differentiable measure of pointwise shared information
We here present a novel measure that satisfies this property, emerges solely from information-theoretic principles, and has the form of a local mutual information.
Information Theory Information Theory
