Short-term neuronal and synaptic plasticity act in synergy for deviance detection in spiking networks

Sensory areas of cortex respond more strongly to infrequent stimuli when these violate previously established regularities, a phenomenon known as deviance detection (DD). Previous modeling work has mainly attempted to explain DD on the basis of synaptic plasticity. However, a large fraction of cortical neurons also exhibit firing rate adaptation, an underexplored potential mechanism. Here, we investigate DD in a spiking neuronal network model with two types of short-term plasticity, fast synaptic short-term depression (STD) and slower threshold adaptation (TA). We probe the model with an oddball stimulation paradigm and assess DD by evaluating the network responses. We find that TA is sufficient to elicit DD. It achieves this by habituating neurons near the stimulation site that respond earliest to the frequently presented standard stimulus (local fatigue), which diminishes the response and promotes the recovery (global fatigue) of the wider network. Further, we find a synergy effect between STD and TA, where they interact with each other to achieve greater DD than the sum of their individual effects. We show that this synergy is caused by the local fatigue added by STD, which inhibits the global response to the frequently presented stimulus, allowing greater recovery of TA-mediated global fatigue and making the network more responsive to the deviant stimulus. Finally, we show that the magnitude of DD strongly depends on the timescale of stimulation. We conclude that highly predictable information can be encoded in strong local fatigue, which allows greater global recovery and subsequent heightened sensitivity for DD. Our brains are constantly processing sensory information, but they must also be able to detect when something unexpected happens. This ability, known as deviance detection, is critical for filtering out irrelevant information and focusing on new or important stimuli. The precise mechanisms underpinning deviance detection remain unknown. While previous research and modeling studies have focused on synaptic changes as the mechanism for deviance detection, we hypothesized that firing rate adaptation, which is a non-synaptic plasticity mechanism that reduces a neuron's response to repeated stimulation, is also involved. Using spiking neural network simulations, we found that adaptation alone is sufficient to produce deviance detection. Additionally, we found that when neuronal adaptation was combined with synaptic short-term depression, the two mechanisms worked together to improve deviance detection. Our findings suggest that the brain may encode predictable information by rapidly adapting both neurons and synapses to frequent stimuli, allowing for heightened sensitivity to unexpected events. An improved understanding of how the brain detects unexpected events could lead to the development of new treatments for disorders such as autism, schizophrenia, or ADHD, which are associated with sensory processing difficulties.