Attractor Dynamics of Superbursts in Living Neural Networks
33rd Annual Meeting of the Society for Neuroscience, New Orleans, LA, 2003
Many brain processes, from odor recognition to motion sequence generation, can be described in terms of dynamic attractors. Here we explore the emergence of attractor dynamics in the spiking activity of neuronal cultures growing on multi-electrode arrays (MEAs). We recorded spiking activity through 58 surface electrodes, continuously for 24h periods. Using superparamagnetic clustering (SPC), we were able to isolate in excess of 200 units per culture. The most prominent feature of the spontaneous firing behavior of these cultures is population bursting. In contrast with earlier reports, we find that many cultures generate “superbursts” during development with a complex internal dynamics. While cultures displaying simple population bursts exhibit varying spatio-temporal patterns, superbursts have much more stereotyped dynamics for a given culture: - The order in which different cells are engaged in bursts is highly conserved from burst to burst, and is independent of the firing rate of individual cells. - Principal component analysis (PCA) reveals that consecutive bursts trace similar orbits through activity space. - Burst composition is more conserved across successive superbursts than within a superburst, indicating a superburst level coordination of spike dynamics. These results demonstrate that even in dissociated culture, cortical neurons can form networks that exhibit rich dynamics with recurring structure at timescales far beyond those of individual action potentials. Since networks with attractor dynamics express learning capability, we plan to utilize this feature to control robots (’animats’, or ’hybrots’). Feedback stimulation derived from the environment of the robot will modify the attractor landscape enabling the culture to learn new behavior.