Spontaneous emergence and drifting of sequential neural activity in recurrent networks

Repeating sequences of neural activity exist across diverse brain regions of different animals and are thought to underlie diverse computations. However, their emergence and evolution in the presence of ongoing synaptic plasticity remain poorly understood. To gain mechanistic insights into this process, we modeled how biologically-inspired rules of activity-dependent synaptic plasticity in recurrent circuits interact to produce connectivity structures that support sequential neuronal activity. Even under unstructured inputs, our recurrent networks developed strong unidirectional connections, resulting in spontaneous repeating spiking sequences. During ongoing plasticity these sequences repeated despite turnover of individual synaptic connections, a process reminiscent of synaptic drift. The turnover process occurred over different timescales, with certain connectivity types and motif structures leading to sequences with different volatility. Structured inputs could reinforce or retrain the resulting connectivity structures underlying sequences, enabling stable but still flexible encoding of inputs. Our model unveils the interplay between synaptic plasticity and sequential activity in recurrent networks, providing insights into how brains implement reliable but flexible computations.