Timothy Müller

The paper shows that value representations in ACC and OFC develop rapidly and use distinct coding strategies, with ACC encoding generalized value and OFC showing context-dependent coding.

The paper investigates how the brain executes programs by analyzing neural activity during mental program execution, finding that fast neural sequences represent candidate solutions.

The paper shows that ventromedial prefrontal cortex uses grid-like coding and theta frequency modulation to represent value space during economic decisions.

This study shows distributional reinforcement learning better explains prefrontal cortex responses compared to classic reinforcement learning.

The paper shows that the anterior cingulate cortex encodes the value of peripheral cues before fixation to guide subsequent fixations and choices during economic decision-making.

The paper shows that visual understanding involves relational and compositional representations in the prefrontal-hippocampal circuit, with replay sequences forming hypotheses about scene configurations.

The paper shows that entorhinal and ventromedial prefrontal cortex representations generalize reinforcement learning problem structures across different tasks.

The paper introduces the Tolman-Eichenbaum machine, a computational model that unifies spatial and relational memory by explaining hippocampal and entorhinal cell representations through generalisation principles.

The paper proposes that grid-like codes can encode global navigation information through cognitive maps, enabling intuitive planning without extensive lookahead.

The paper reviews evidence and theories suggesting cognitive maps organize knowledge across domains, supporting flexible behavior and abstract learning.