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Researchers Adapt CCM to Detect Causality in Neural Spike Trains

The technique reconstructs state space from interspike intervals to enable model-free causality analysis on irregular spike data.

Overview

  • The peer-reviewed study by Kazuya Sawada and colleagues was published July 28, 2025, in Physical Review E and is now being highlighted in institutional coverage.
  • The method extends convergent cross mapping by pairing ISI-based state-space reconstruction with a temporal correspondence procedure tailored for spike trains.
  • Numerical experiments on a standard neuron model recovered bidirectional, unidirectional, and absent coupling with high fidelity.
  • Performance held up under weak coupling and internal noise, indicating robustness for biologically realistic conditions.
  • Tests so far covered two to three neurons, with the authors prioritizing scale-up to larger networks and noting potential relevance for neuroscience and other point-process fields such as finance, seismology, and logistics.