Active Sensing Through Oscillatory Synchronisation A Possible Mechanism for Filtering and Amplifying Input in Both Humans and Arti cial Cognitive Agents
Brain oscillations are known to re ect uctuations of low and high excitability states in neuronal populations. These oscillations can adjust to the surrounding environment such that high excitability states co-occur with relevant sensory information. Such adjustment is a promising mechanism for ltering sensory input and could occur through neural entrainment. Driven by an external rhythmic input, intrinsic oscillations might phase-align with (i.e., entrain to) this input, resulting in the optimal processing of stimuli that are in phase with the rhythm. Oscillatory adjustment could also occur through covert active sensing which entails that the motor cortex drives the signals in the sensory cortex. Thus, covert active sensing and entrainment could explain a novel behavioural e ect found in prior work, namely that subjects respond faster in a discrimination task when the external rhythm is faster. 13 subjects performed a visual discrimination task while brain signals were recorded using MEG. Targets were cued by a rhythmic stream of visual stimuli at di erent frequencies and appeared after one, two, or three cycles, or not at all. In summary, we found support for the aforementioned behavioural e ect (i.e., subjects responding faster when cued by faster external rhythms) and covert active sensing, but not for entrainment. We further discuss how the ndings of the current study could inspire the development of arti cial cognitive agents to tackle the problem of determining which information from the environment is relevant. Importantly, this includes a proposal for how the elds of neuroscience and AI can actively interact with each other, such that both elds bene t.
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