Internal models

…circuits—that allow animals to build internal models, process information, and choose actions. We are searching for a Scientist to develop data-driven theories of how circuit structure shapes neural computation. The Scientist will use the Institute's large-scale connectivity atlases and brain-wide recordings to understand how the brain's wiring organizes neural dynamics and drives flexible behavior. The position o

Flexible behaviour depends on internal models linking objects, actions and events. This talk asks how the brain constructs these relational structures and incorporates unfamiliar problems into existing knowledge. Evidence from rest and sleep suggests that apparently inactive periods support reorganization, new inferences and shortcuts across experience, helping explain how knowledge becomes more useful over time.

…They need to build internal models from this ongoing stream of experiences with few supervisory and reinforcement signals. How they accomplish this feat remains unclear. One possibility is that the brain relies on a slowness objective (SO) which stipulates that neuronal representations change little over time$^1$. This allows extracting slowly varying underlying factors from the sensory experience, in some cases, with local plasticity rules$^2$. However, the approach precludes exploiting the rich

The primary visual cortex is thought to encode low-level features (orientation/direction, contrast, brightness, etc.), while also enabling adaptation and gain control. However, the kinds of computations it is capable of performing, as an isolated circuit, are not clear.Here, we describe a sensory surprise signal in the local field potential (LFP) in the primary visual cortex (V1) of the anesthetized mouse in response to contrast rate change – a complex, time dependent stimulus parameter. We investigate the population activity responsible for this surprise signal and identify neuronal subgroups

…mappings are described by two types of internal models – one that transforms motor signals into predictions of sensory feedback (forward model), and one that transforms sensory goals onto the motor commands necessary to achieve those goals (inverse model). One promising theoretical framework that provides computational ground for learning and maintaining these mappings, is that of predictive processing. The functional implementation of this algorithm in cortex implies two computational

In human and non-human animals, conceptual knowledge is partially organized according to low-dimensional geometries that rely on brain structures and computations involved in spatial representations. Recently, two separate lines of research have investigated cognitive maps, that are associated with the hippocampal formation and are similar to world-centered representations of the environment, and image spaces, that are associated with the parietal cortex and are similar to self-centered spatial relationships. I will suggest that cognitive maps and image spaces may be two manifestations of a m