Revealing and reshaping attractor dynamics in large networks of cortical neurons

Attractors play a key role in a wide range of processes including learning, memory, decision making and navigation. Due to recent innovations in recording methods, there is increasing evidence for the existence of attractor dynamics in the brain. Yet, our understanding of how these attractors emerge or disappear in a biological system is lacking. In vitro cultured cortical neurons have been used extensively as a realistic experimental tool to understand the underlying mechanisms in neuronal assemblies. One of the main characteristics of the activity of such networks are the spontaneous synchronized bursts, in which a large fraction of the neurons fire almost simultaneously within several hundreds of milliseconds. We follow the spontaneous activity of such networks and identify these bursting events. We create a vocabulary of spatiotemporal patterns and show that they function as discrete attractors in the network dynamics. We then repeatedly trigger specific attractors via electrical stimulation. We find that the targeted attractors are eliminated from the spontaneous vocabulary, while they are robustly evoked by the electrical stimulation. This seemingly paradoxical finding can be explained by a Hebbian-like strengthening of specific pathways into the attractors, at the expense of weakening non-evoked pathways into the same attractors. We verify this hypothesis, provide a mechanistic explanation for the underlying changes supporting this effect. To our knowledge, this work provides the first direct evidence for discrete multi-stability in a biological neural network. In addition, the plasticity principles we describe improve our understanding on how attractors in a biological system evolve.