Controlling spatio-temporal sequences of neural activity by local synaptic changes.

The neural basis of behavior is believed to consist of sequential patterns of neural activity in the relevant brain regions. Behavioral flexibility also requires neural circuit mechanisms that support dynamic control of sequential activity. However, mechanisms to control and reconfigure sequential activity have received little attention. Here, we show that recurrently connected networks with heterogeneous connectivity and a smooth spatial in-degree landscape (which may arise due to asymmetric neuron morphologies) provide a robust mechanism to evoke and control sequential activity. By modulating the synaptic strength of only a few neurons in local neighborhoods, we uncovered high-impact locations that can start, stop, extend, gate, and redirect sequences. Interestingly, highimpact locations coincide with mid in-degree regions. We demonstrate that these motifs can flexibly reconfigure sequential activity, and hence, provide a framework for fast and flexible computations on behavioral timescales, while the individual parts of the pathways remain rigid and reliable.Significance Statement Neuron morphologies are often asymmetric and differ in size, through which neural networks become strongly heterogeneous. Beyond being a mechanism for stabilizing network dynamics, we investigate the computational capabilities of heterogeneous networks. The resulting networks display a wide range of input connectivity across space. Hence, various cognitive processes can be computed in parallel in distinct regions of the spatial network. We demonstrate that the interactions among the computations can be flexibly reconfigured by a mechanism that utilizes local modulation. Consequently, spatially heterogeneous networks provide a framework for fast computations that can be fine-tuned in a context-dependent manner on a behavioral timescale.