ESI Lecture by Dana Ballard
- Tuesday, November 20, 2018, 11:30-13:00
- Lecture Hall, ESI
- Dana Ballard (University of Texas)
- Martin Vinck
Cortical spike coding using gamma frequency latencies
One of the fundamental problems in understanding the brain, in particular the cerebral cortex, is that we only have a partial understanding of the basic communication protocols that underlie signal transmission. This makes it difficult to interpret the significance of particular phenomena such as basic spike firing patterns and oscillations at different frequencies. There are, of course, useful models. Motivated by cell spike recording technology, Poisson statistics of cortical action potentials have long been a basic component in models of signal representation in the cortex. However, it is increasingly difficult to integrate slow Poisson spiking with much faster spike timing signals in the gamma frequency spectrum. A potential way forward is being sparked by advances in patch clamping methodologies that allow the exploration of communication strategies that use millisecond timescales. Specifically, the voltage potential of a cell’s soma now can be recorded at 20 kilohertz in vivo, allowing its high resolution structure to be correlated with behaviors. To interpret this signal, we have developed a unified model that takes advantage of a single cycle of cell’s somatic gamma frequency to modulate the generation of its action potentials. This capability can be seen as organized into a general-purpose method of coding fast computation in cortical networks. In particular, this coding strategy has three important additional advantages over traditional formalisms: 1) Its processing speed is two to three orders of magnitude times faster than population coding methods, 2) It allows multiple, independent processes to run in parallel, greatly increasing the processing capability of the cortex and 3) Its processes are not bound to specific locations, but migrate across cortical cells as a function of time, facilitating the maintenance of cortical cell calibration.