Bayesian models

Bayesian models have been successful in explaining sensorimotor behavior in humans and animals, yet it remains unclear whether they can also explain neural activity. Testing them using neural data is complicated by the fact that linking them to neural activity requires two key moving parts: (1) a hypothesis about which latent variable is represented by a neuron, or neural population (``generative model'') and (2) a hypothesis about how beliefs about a latent variable (``posterior'') are represented by neural responses. Here, we present two new methods that in conjunction allow us to, first, fa

Humans maintain a repertoire of memories, which are continuously acquired, updated, and expressed as appropriate for the current context to meet behavioral demands. This continual learning remains a major challenge for artificial neural networks and has been suggested to require purpose-built mechanisms, such as memory replay or carefully designed learning rules. Sensorimotor learning provides a particularly useful testbed for theories of continual learning, as previous experiments have revealed a rich phenomenology of the complex and often intriguing dependence of motor adaptation on both rec

Bayesian models have been successful at accounting for human and animal behavior, yet to what degree they can also explain neural activity is still an open question. While decoding approaches that link neural variability to behavioral uncertainty provide some evidence, stronger tests have tried to link posterior beliefs about specific latent variables in a generative model to neural responses. On one hand, the specificity of the resulting predictions is desirable since it allows us to decide which of the infinitely many parameterizations of the task model (ideal observer) is more closely align

An important ability for successful interaction with the environment is the estimation of magnitudes such as the duration of an event. To improve duration estimates, the brain must compensate for sensory uncertainty, which it does by encoding the statistical regularities that link co-occurring stimuli and tasks (i.e., temporal context). The entorhinal cortex (EC) supports the encoding of such task regularities, which has been suggested to rely on grid cells that the EC is known to harbor. On population level, grid cells exhibit a six-fold rotational symmetry as a function of gaze direction, wh

…theories propose that generative models formed through learning modulate primary sensory responses on a moment-to-moment basis according to how well incoming signals conform to the animal’s predictions. Experimental data has shown that prediction errors can alter neural responses, even in the absence of behavioral relevance. Yet several aspects