Coherent noise enables probabilistic sequence replay in spiking neuronal networks

Abstract

Animals rely on different decision strategies when faced with ambiguous or uncertain cues. Depending on the context, decisions may be biased towards events that were most frequently experienced in the past, or be more explorative. A particular type of decision making central to cognition is sequential memory recall in response to ambiguous cues. A previously developed spiking neuronal network implementation of sequence prediction and recall learns complex, high-order sequences in an unsupervised manner by local, biologically inspired plasticity rules. In response to an ambiguous cue, the model deterministically recalls the sequence shown most frequently during training. Here, we present an extension of the model enabling a range of different decision strategies. In this model, explorative behavior is generated by supplying neurons with noise. As the model relies on population encoding, uncorrelated noise averages out, and the recall dynamics remain effectively deterministic. In the presence of locally correlated noise, the averaging effect is avoided without impairing the model performance, and without the need for large noise amplitudes. We investigate two forms of correlated noise occurring in nature: shared synaptic background inputs, and random locking of the stimulus to spatiotemporal oscillations in the network activity. Depending on the noise characteristics, the network adopts various replay strategies. This study thereby provides potential mechanisms explaining how the statistics of learned sequences affect decision making, and how decision strategies can be adjusted after learning. Significance statementHumans and other animals often benefit from exploring multiple alternative solutions to a given problem. Such explorative behavior is frequently attributed to noise in the neuronal dynamics. Supplying each neuron or synapse in a neuronal circuit with noise, however, does not necessarily lead to explorative dynamics. If decisions are triggered by the compound activity of ensembles of neurons or synapses, noise averages out, unless it is correlated within these ensembles. This modeling study equips a neuronal sequence-processing circuit with explorative behavior by introducing configurable coherent noise. It contributes to an understanding of the neuronal mechanisms underlying different decision strategies in the face of ambiguity, and highlights the role of coherent network activity during sequential memory recall.