Spontaneous emergence and evolution of neuronal sequences in recurrent networks

Repeating sequences of neural activity exist across brain regions of different animals are thought to underlie diverse computations. However, their emergence and evolution during ongoing synaptic plasticity remain unclear. To mechanistically understand this process, we investigated the interaction of biologically-inspired activity-dependent synaptic plasticity rules in models of recurrent circuits to produce connectivity structures that support neuronal sequences. Under unstructured inputs, our recurrent networks developed strong unidirectional connections, resulting in spontaneous repeating sequences of spikes. 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 sequence-promoting connectivity types and motif structures being strengthened while others weakened, leading to sequences with different degrees of volatility. Structured inputs could reinforce or retrain the resulting connectivity structures underlying sequences, enabling stable yet flexible encoding of inputs. Our model unveils the interplay between synaptic plasticity and sequential activity in recurrent networks, providing insights into how the brain might implement reliable and flexible computations.