Slow ramping emerges from spontaneous fluctuations in spiking neural networks

Abstract

AbstractThe capacity to initiate actions endogenously is critical for goal-directed behavior. Spontaneous voluntary actions are typically preceded by slow-ramping medial frontal cortex activity that begins around two seconds before movement, which may reflect spontaneous fluctuations that influence action timing. However, the mechanisms by which these slow ramping signals emerge from single-neuron and network dynamics remain poorly understood. Here, we developed a spiking neural network model that produces spontaneous slow ramping activity in single neurons and population activity with onsets ∼2 seconds before threshold crossings. A key prediction of our model is that neurons that ramp together have correlated firing patterns before ramping onset. We confirmed this model-derived hypothesis in a dataset of human single neuron recordings from medial frontal cortex. Our results suggest that slow ramping signals reflect bounded spontaneous fluctuations that emerge from quasi-winner-take-all dynamics in clustered networks that are temporally stabilized by slow-acting synapses.HighlightsWe reveal a mechanism for slow-ramping signals before spontaneous voluntary movements.Slow synapses stabilize spontaneous fluctuations in spiking neural network.We validate model predictions in human frontal cortical single neuron recordingsThe model recreates the readiness potential in an EEG proxy signal.Neurons that ramp together had correlated activity before ramping onset.