Do neural spike patterns carry more information than the sum of their individual parts? This study introduces a method to measure the information carried by compound events in neural spike trains, independent of assumptions about what these patterns represent. It focuses on quantifying the synergy among individual spikes within compound patterns. By comparing the information conveyed by a compound pattern to the sum of information from its components, the authors directly measure synergy. Applying this method to motion-sensitive neuron H1 in the fly's visual system, they confirm that closely spaced spikes carry far more than twice the information of a single spike. Further analysis suggests these spike pairs are particularly important in H1’s code. This approach reveals the presence and significance of synergy in neural coding, offering insights into how information is encoded and processed in the brain.
Published in Neural Computation, this paper contributes to the journal's focus on neural coding and information processing in the brain. Its method for measuring synergy in spike trains is relevant to computational neuroscience and understanding how neurons communicate.