Distributed context-dependent choice information in mouse dorsal-parietal cortex

Abstract

Choice information appears in the brain as distributed signals with top-down and bottom-up components that together support decision-making computations. In sensory and associative cortical regions, the presence of choice signals, their strength, and area specificity are known to be elusive and changeable, limiting a cohesive understanding of their computational significance. In this study, examining the mesoscale activity in mouse posterior cortex during a complex visual discrimination task, we found that broadly distributed choice signals defined a decision variable in a low-dimensional embedding space of multi-area activations, particularly along the ventral visual stream. The subspace they defined was near-orthogonal to concurrently represented sensory and motor-related activations, and it was modulated by task difficulty and contextually by the animals’ attention state. To mechanistically relate choice representations to decision-making computations, we trained recurrent neural networks with the animals’ choices and found an equivalent decision variable whose context-dependent dynamics agreed with that of the neural data. In conclusion, our results demonstrated an independent decision variable broadly represented in the posterior cortex, controlled by task features and cognitive demands. Its dynamics reflected decision computations, possibly linked to context-dependent feedback signals used for probabilistic-inference computations in variable animal-environment interactions.

Motor-related signals support localization invariance for stable visual perception

“…Experimental observations made at the local-circuits level [84] and at the multi-area level [70], have shown that visual and motor signals, concurrently activating visual areas, can be represented by distinct low-dimensional activity manifolds, that, at the mesoscale level, are near orthogonal to each other [85]. Therefore, we next examined the propagation of visual and motor signals across layers and the separation of the representations.…”

Efficient coding of natural images in the mouse visual cortex

“…As described in Orlandi et. al., 2021 [94], the awake mice were head-fixed and placed under a dual cube THT macroscope (Brainvision Inc.) for widefield imaging in tandem-lens epifluorescence configuration using two AF NIKKOR 50 mm f/1.4D lenses. We imaged the jGCaMP7f fluorescence signals using interleaved shutter-controlled blue and violet LEDs with a CMOS camera (PCO Edge 5.5) with an acquisition framerate of 60 Hz.…”

“…Then, as described in Orlandi et. al., 2021 [94], we corrected for the “hemodynamic component” by removing a calcium-independent component from the recorded signal. For every pixel, the blue and violet data were independently transformed into a relative fluorescence signal, Δ F/F = ( F − aF − b ) /b , where F is the original data, and the a and b coefficients are obtained by linear fitting each time series, i.e., F ( t ) ≈ at − b .…”