Aluisi, F. | bioRxiv Science

bioRxiv2026

Directionally tuned signals in mouse visual and subicular cortex during passive rotation

Head-direction (HD) cells integrate visual and vestibular inputs and provide an internal compass which is essential for navigation, but how the directional activity in the subicular complex (SUB) and in the visual cortex (VIS) depends on the visual scene has not been compared directly. Here, we recorded simultaneously from the two regions with Neuropixels probes, while rotating head-fixed mice across all azimuthal directions, in controlled visual conditions, and in darkness. We found many directionally tuned units in both SUB and VIS. The preferred directions of SUB units were distributed uniformly and followed the rotation of a single prominent visual cue. The preferred directions of VIS units on the other hand were concentrated towards the visual cue. Heading was decoded with good accuracy from population activity from either region in cue rich visual conditions. In the presence of a single cue, decoding performance was higher in VIS. In darkness, the directional tuning was apparently lost at the single cell level, in both VIS and SUB. Importantly, even in darkness, the tuning-ordered structure of pairwise correlations was largely maintained in SUB, but not in VIS. This dissociation suggests that the SUB coherence reflects an internally organized attractor that can maintain its structure without vision or active movement, whereas VIS coherence largely reflects the external sensory structure. A wave-like distortion in decoded heading with respect to a visual cue suggests that a single salient landmark can shape the HD signal. Taken together, visual and head direction signals allow for an internal compass with continuity in the absence of landmarks, and recalibration as landmarks are reinstated.

neuroscience↗

bioRxiv2018

Animal learning in amultidimensional discrimination task as explained by dimension-specific allocation of attention

Reinforcement learning describes the process by which during a series of trial-and-error attempts, actions that culminate in reward are strengthened. When the actions are based on sensory stimuli, an association is formed between the stimulus, the action and the reward. Computational, behavioral and neurobiological accounts of this process successfully explain simple stimulus-response learning. However, if the cue is multi-dimensional, identifying which of its features are relevant for the reward is not trivial, and the underlying cognitive process is poorly understood. To study this we adapted an intra-dimensional/ extra-dimensional set-shifting paradigm to train rodents on a multidimensional sensory discrimination task. In our setup, stimuli of different modalities (spatial, olfactory and visual) are combined into complex cues and manipulated independently. In each set, only a single stimulus dimension is relevant for reward. To distinguish between learning and decision-making we suggest a weighted attention model (WAM). It combines a learning model where each feature-dimension is reinforced separately with a decision rule that chooses an alternative according to a weighted average of learnt values, in which weight is associated with each dimension. We estimated the parameters of the WAM (decision weights, learning rate and noise) and demonstrated that is outperforms an alternative model in which a value learnt is assigned to each combination of features, or every state. Estimated decision weights of WAM reveal an experience-based bias in learning. The intra-dimensional set shift separated the decision weights. While in the first phase of the experiment the weights were roughly the same, in the second phase the weight on the dimension that was key to finding the reward became higher than others. After the extra-dimensional shift this dimension became irrelevant, however its decision weight remained high for the early learning stage in this last phase, providing an explanation for the poor performance of the animals. By the end of the phase when the rats performance improved, the weights for the two dimensions converged. Thus, estimated weights can be viewed as a possible way to quantify the experience-based bias.

animal behavior and cognition↗