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This paper introduces the Task-Amortized Variational Autoencoder (TAVAE), a generative model of V1 activity, to investigate how task-specific priors are learned and deployed in the visual cortex. TAVAE extends the VAE framework to efficiently acquire new tasks by reusing previously learned representations, allowing for flexible adaptation of priors. By comparing TAVAE's posterior distributions with large-scale V1 recordings from mice performing a discrimination task, the study demonstrates that the visual system can rapidly learn and utilize task-specific contextual priors, reflected in bimodal response profiles when task statistics are violated.
Task-optimized generative models can explain how the visual cortex rapidly adapts its priors to new tasks, revealing signatures of uncertainty when expectations are violated.
The brain interprets visual information through learned regularities, a computation formalized as probabilistic inference under a prior. The visual cortex establishes priors for this inference, some delivered through established top-down connections that inform low-level cortices about statistics represented at higher levels in the cortical hierarchy. While evidence shows that adaptation leads to priors reflecting the structure of natural images, it remains unclear whether similar priors can be flexibly acquired when learning a specific task. To investigate this, we built a generative model of V1 optimized for a simple discrimination task and analyzed it together with large-scale recordings from mice performing an analogous task. In line with recent approaches, we assumed that neuronal activity in V1 corresponds to latent posteriors in the generative model, enabling investigation of task-related priors in neuronal responses. To obtain a flexible test bed, we extended the VAE formalism so that a task can be acquired efficiently by reusing previously learned representations. Task-specific priors learned by this Task-Amortized VAE were used to investigate biases in mice and model when presenting stimuli that violated trained task statistics. Mismatch between learned task statistics and incoming sensory evidence produced signatures of uncertainty in stimulus category in the TAVAE posterior, reflecting properties of bimodal response profiles in V1 recordings. The task-optimized generative model accounted for key characteristics of V1 population activity, including within-day updates to population responses. Our results confirm that flexible task-specific contextual priors can be learned on demand by the visual system and deployed as early as the entry level of visual cortex.