Abstract
Neuronal variability in sensory cortex predicts perceptual decisions. This relationship, termed choice probability (CP), can arise from sensory variability biasing behaviour and from top-down signals reflecting behaviour. To investigate the interaction of these mechanisms during the decision-making process, we use a hierarchical network model composed of reciprocally connected sensory and integration circuits. Consistent with monkey behaviour in a fixed-duration motion discrimination task, the model integrates sensory evidence transiently, giving rise to a decaying bottom-up CP component. However, the dynamics of the hierarchical loop recruits a concurrently rising top-down component, resulting in sustained CP. We compute the CP time-course of neurons in the medial temporal area (MT) and find an early transient component and a separate late contribution reflecting decision build-up. The stability of individual CPs and the dynamics of noise correlations further support this decomposition. Our model provides a unified understanding of the circuit dynamics linking neural and behavioural variability.
Highlights
Neuronal variability in sensory cortex predicts perceptual decisions
We developed a computational model of perceptual decision making that allowed us to isolate and quantify the dynamics and the relative contributions of bottom-up and top-down mechanisms to spike count noise correlations and choice probability (CP) of sensory neurons
We have developed a hierarchical network model to investigate how CP, the correlation between neuron response variability and perceptual decisions, emerges from recurrent cortical network dynamics
Summary
Neuronal variability in sensory cortex predicts perceptual decisions. This relationship, termed choice probability (CP), can arise from sensory variability biasing behaviour and from top-down signals reflecting behaviour. In the top-down interpretation[9,15,16,17], the variability of sensory neurons that correlates with choice arises due to trial-to-trial fluctuations in top-down signals, which modulate the magnitude of the evoked responses[18,19,20] The nature of these top-down signals remains, largely unknown: it is not clear on what time-scale they operate[16], what causes their variability, and whether they are generated before the stimulus presentation, reflecting some kind of bias or expectation, or they are instead recruited by sensory inputs as some kind of bottom-up attentional signal. We present a hierarchical network model of spiking neurons, representing a sensory and an associative cortical area and carrying out the discrimination of two stimulus categories
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
