arXiv subjects

Daniel Toker

Publications and source records attributed to Daniel Toker.

3 recordsLinked to original sources

arXiv2020

A simple method for detecting chaos in nature

Chaos, or exponential sensitivity to small perturbations, appears everywhere in nature. Moreover, chaos is predicted to play diverse functional roles in living systems. A method for detecting chaos from empirical measurements should therefore be a key component of the biologist's toolkit. But, classic chaos-detection tools are highly sensitive to measurement noise and break down for common edge cases, making it difficult to detect chaos in domains, like biology, where measurements are noisy. However, newer tools promise to overcome these limitations. Here, we combine several such tools into an automated processing pipeline, and show that our pipeline can detect the presence (or absence) of chaos in noisy recordings, even for difficult edge cases. As a first-pass application of our pipeline, we show that heart rate variability is not chaotic as some have proposed, and instead reflects a stochastic process in both health and disease. Our tool is easy-to-use and freely available.

q-bio.QM↗

arXiv2019

Information Integration In Large Brain Networks

An outstanding problem in neuroscience is to understand how information is integrated across the many modules of the brain. While classic information-theoretic measures have transformed our understanding of feedforward information processing in the brain's sensory periphery, comparable measures for information flow in the massively recurrent networks of the rest of the brain have been lacking. To address this, recent work in information theory has produced a sound measure of network-wide "integrated information," which can be estimated from time-series data. But, a computational hurdle has stymied attempts to measure large-scale information integration in real brains. Specifically, the measurement of integrated information involves a combinatorial search for the informational "weakest link" of a network, a process whose computation time explodes super-exponentially with network size. Here, we show that spectral clustering, applied on the correlation matrix of time-series data, provides an approximate but robust solution to the search for the the informational weakest link of large networks. This reduces the computation time for integrated information in large systems from longer than the lifespan of the universe to just minutes. We evaluate this solution in brain-like systems of coupled oscillators as well as in high-density electrocortigraphy data from two macaque monkeys, and show that the informational "weakest link" of the monkey cortex splits posterior sensory areas from anterior association areas. Finally, we use our solution to provide evidence in support of the long-standing hypothesis that information integration is maximized by networks with a high global efficiency, and that modular network structures promote the segregation of information.

q-bio.NC↗

arXiv2016

Moving Past the Minimum Information Partition: How To Quickly and Accurately Calculate Integrated Information

An outstanding challenge with the Integrated Information Theory of Consciousness (IIT) is to find a way of rapidly and accurately calculating integrated information from neural data. A number of measures of integrated information based on time series data have been proposed, but most measures require finding the Minimum Information Partition of a network, which is computationally expensive and not practical for real brain data. Here, we introduce a novel partition, the Maximum Modularity Partition, across which to quickly calculate integrated information. We also introduce a novel detection task on simulated data to evaluate the performance of integrated information measures across different partitions. We show that integrated information can be reliably and quickly calculated across the Maximum Modularity Partition, as well as the previously proposed atomic partition, even in relatively large networks, which constitutes an advance in researchers' ability to empirically test the predictions of IIT in real brain data.

q-bio.NC↗