This letter presents a noninvasive imaging technique that captures the exact timing and locations of cortical activity sequences that are specific to a cognitive process. These precise spatiotemporal sequences can be detected in the human brain as specific time-position pattern associated with a cognitive task. They are consistent with direct measurements of population activity recorded in nonhuman primates, thus suggesting that specific time-position patterns associated with a cognitive task can be identified. This imaging technique is based on estimating the amplitude of cortical current dipoles from MEG recordings. Although the spatial resolution of these estimations is poor (approximately 2 cm), the temporal resolution is high (milliseconds). We show that within these cortical current dipoles, time points of cortical activation can be identified as brief amplitude undulations and that sequences of these transients repeat with millisecond accuracy, hence making it possible to treat the timing of these transients as point processes. We illustrate the feasibility of finding spatiotemporal templates specific to the cognitive processes associated with following the rhythm of drumbeats that involve the activation at multiple cortical and cerebellar loci. These templates evolve at an accuracy of a few milliseconds. This approach can thus pave the way for new perspectives on the relationships between brain dynamics and cognition.

We investigate the formation of synfire waves in a balanced network of integrate-and-fire neurons. The synaptic connectivity of this network embodies synfire chains within a sparse random connectivity. This network can exhibit global oscillations but can also operate in an asynchronous activity mode. We analyze the correlations of two neurons in a pool as convenient indicators for the state of the network. We find, using different models, that these indicators depend on a scaling variable. Beyond a critical point, strong correlations and large network oscillations are obtained. We looked for the conditions under which a synfire wave could be propagated on top of an otherwise asynchronous state of the network. This condition was found to be highly restrictive, requiring a large number of neurons for its implementation in our network. The results are based on analytic derivations and simulations.