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Found 10 papers, 1 papers with code
Constraints on the design of neuromorphic circuits set by the properties of neural population codes
In the brain, information is encoded, transmitted and used to inform behaviour at the level of timing of action potentials distributed over population of neurons.
Biologically plausible solutions for spiking networks with efficient coding
Here, we revisit the theory of efficient coding with spikes to develop spiking neural networks that are closer to biological circuits.
Causal learning with sufficient statistics: an information bottleneck approach
Using these statistics we formulate new additional rules of causal orientation that provide causal information not obtainable from standard structure learning algorithms, which exploit only conditional independencies between observable variables.
Conditionally-additive-noise Models for Structure Learning
Methods based on additive-noise (AN) models have been proposed to further discriminate between causal structures that are equivalent in terms of conditional independencies.
Synthesizing realistic neural population activity patterns using Generative Adversarial Networks
The ability to synthesize realistic patterns of neural activity is crucial for studying neural information processing.
Baseline-corrected space-by-time non-negative matrix factorization for decoding single trial population spike trains
Activity of populations of sensory neurons carries stimulus information in both the temporal and the spatial dimensions.
Quantifying how much sensory information in a neural code is relevant for behavior
Determining how much of the sensory information carried by a neural code contributes to behavioral performance is key to understand sensory function and neural information flow.
The identity of information: how deterministic dependencies constrain information synergy and redundancy
Harder et al. (2013) proposed an identity axiom stating that there cannot be redundancy between two independent sources about a copy of themselves.
Invariant components of synergy, redundancy, and unique information among three variables
In a system of three stochastic variables, the Partial Information Decomposition (PID) of Williams and Beer dissects the information that two variables (sources) carry about a third variable (target) into nonnegative information atoms that describe redundant, unique, and synergistic modes of dependencies among the variables.
Mixed vine copulas as joint models of spike counts and local field potentials
Our methods hold the promise to considerably improve statistical analysis of neural data recorded simultaneously at different scales.
