Back to Search
Start Over
Embedding optimization reveals long-lasting history dependence in neural spiking activity
- Source :
- PLoS Computational Biology, PLoS Computational Biology, Vol 17, Iss 6, p e1008927 (2021)
- Publication Year :
- 2021
- Publisher :
- Public Library of Science (PLoS), 2021.
-
Abstract
- Information processing can leave distinct footprints on the statistics of neural spiking. For example, efficient coding minimizes the statistical dependencies on the spiking history, while temporal integration of information may require the maintenance of information over different timescales. To investigate these footprints, we developed a novel approach to quantify history dependence within the spiking of a single neuron, using the mutual information between the entire past and current spiking. This measure captures how much past information is necessary to predict current spiking. In contrast, classical time-lagged measures of temporal dependence like the autocorrelation capture how long—potentially redundant—past information can still be read out. Strikingly, we find for model neurons that our method disentangles the strength and timescale of history dependence, whereas the two are mixed in classical approaches. When applying the method to experimental data, which are necessarily of limited size, a reliable estimation of mutual information is only possible for a coarse temporal binning of past spiking, a so-called past embedding. To still account for the vastly different spiking statistics and potentially long history dependence of living neurons, we developed an embedding-optimization approach that does not only vary the number and size, but also an exponential stretching of past bins. For extra-cellular spike recordings, we found that the strength and timescale of history dependence indeed can vary independently across experimental preparations. While hippocampus indicated strong and long history dependence, in visual cortex it was weak and short, while in vitro the history dependence was strong but short. This work enables an information-theoretic characterization of history dependence in recorded spike trains, which captures a footprint of information processing that is beyond time-lagged measures of temporal dependence. To facilitate the application of the method, we provide practical guidelines and a toolbox.<br />Author summary Even with exciting advances in recording techniques of neural spiking activity, experiments only provide a comparably short glimpse into the activity of only a tiny subset of all neurons. How can we learn from these experiments about the organization of information processing in the brain? To that end, we exploit that different properties of information processing leave distinct footprints on the firing statistics of individual spiking neurons. In our work, we focus on a particular statistical footprint: We quantify how much a single neuron’s spiking depends on its own preceding activity, which we call history dependence. By quantifying history dependence in neural spike recordings, one can, in turn, infer some of the properties of information processing. Because recording lengths are limited in practice, a direct estimation of history dependence from experiments is challenging. The embedding optimization approach that we present in this paper aims at extracting a maximum of history dependence within the limits set by a reliable estimation. The approach is highly adaptive and thereby enables a meaningful comparison of history dependence between neurons with vastly different spiking statistics, which we exemplify on a diversity of spike recordings. In conjunction with recent, highly parallel spike recording techniques, the approach could yield valuable insights on how hierarchical processing is organized in the brain.
- Subjects :
- 0301 basic medicine
Physiology
Computer science
Entropy
Action Potentials
Information theory
Hippocampus
Time Measurement
Mathematical and Statistical Techniques
0302 clinical medicine
Animal Cells
Medicine and Health Sciences
Biology (General)
Visual Cortex
Neurons
0303 health sciences
Measurement
Ecology
Physics
Statistics
Information processing
Brain
Contrast (statistics)
Mutual information
Single Neuron Function
Electrophysiology
medicine.anatomical_structure
Computational Theory and Mathematics
Autocorrelation
Modeling and Simulation
Physical Sciences
Embedding
Engineering and Technology
Thermodynamics
Spike (software development)
Cellular Types
Anatomy
Research Article
QH301-705.5
Ocular Anatomy
Models, Neurological
Neurophysiology
Research and Analysis Methods
Membrane Potential
Measure (mathematics)
Retina
Footprint
03 medical and health sciences
Cellular and Molecular Neuroscience
Ocular System
medicine
Genetics
Humans
Computer Simulation
Statistical Methods
Set (psychology)
Representation (mathematics)
Molecular Biology
Ecology, Evolution, Behavior and Systematics
030304 developmental biology
Computational Neuroscience
business.industry
Biology and Life Sciences
Computational Biology
Pattern recognition
Cell Biology
030104 developmental biology
Cellular Neuroscience
Signal Processing
Neuron
Artificial intelligence
Focus (optics)
business
Mathematics
030217 neurology & neurosurgery
Neuroscience
Coding (social sciences)
Subjects
Details
- ISSN :
- 15537358
- Volume :
- 17
- Database :
- OpenAIRE
- Journal :
- PLOS Computational Biology
- Accession number :
- edsair.doi.dedup.....b46a0a07b226f953fc82ab691a925ee2