Transient changes in the firing of midbrain dopamine neurons have been closely tied to the unidimensional value-based prediction error contained in temporal difference reinforcement learning models. However, whereas an abundance of work has now shown how well dopamine responses conform to the predictions of this hypothesis, far fewer studies have challenged its implicit assumption that dopamine is not involved in learning value-neutral features of reward. Here, we review studies in rats and humans that put this assumption to the test, and which suggest that dopamine transients provide a much richer signal that incorporates information that goes beyond integrated value.

The major pathological feature of Parkinson 's disease (PD), the second most common neurodegenerative disease and most common movement disorder, is the predominant degeneration of dopaminergic neurons in the substantia nigra, a part of the midbrain. Despite decades of research, the molecular mechanisms of the origin of the disease remain unknown. While the disease was initially viewed as a purely neuronal disorder, results from single-cell transcriptomics have suggested that oligodendrocytes may play an important role in the early stages of Parkinson's. Although these findings are of high relevance, particularly to the search for effective disease-modifying therapies, the actual functional role of oligodendrocytes in Parkinson's disease remains highly speculative and requires a concerted scientific effort to be better understood. This Unsolved Mystery discusses the limited understanding of oligodendrocytes in PD, highlighting unresolved questions regarding functional changes in oligodendroglia, the role of myelin in nigral dopaminergic neurons, the impact of the toxic environment, and the aggregation of alpha-synuclein within oligodendrocytes.

Understanding how corticostriatal circuits mediate behavioral selection and initiation in a naturalistic setting is critical to understanding behavior choice and execution in unconstrained situations. The central striatum (CS) is well poised to play an important role in these spontaneous processes. Using fiber photometry and optogenetics, we identify a role for CS in grooming initiation. However, CS-evoked movements resemble short grooming fragments, suggesting additional input is required to appropriately sustain behavior once initiated. Consistent with this idea, the anterior lateral motor area (ALM) demonstrates a slow ramp in activity that peaks at grooming termination, supporting a potential role for ALM in encoding grooming bout length. Furthermore, optogenetic stimulation of ALM-CS terminals generates sustained grooming responses. Finally, dual-region photometry indicates that CS activation precedes ALM during grooming. Taken together, these data support a model in which CS is involved in grooming initiation, while ALM may encode grooming bout length.

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Recent advances in the field of Voltage Imaging, with a special focus on new constructs and novel implementations.

Work related to place tuning, spatial navigation, orientation and direction. Mainly includes articles on connectivity in the hippocampus, retrosplenial cortex, and related areas.

The conception of time as a universal and independent parameter is a foundational assumption in physical models. However, it does not address the subjective nature of temporal perception and leads to inconsistencies in complex systems. This paper introduces the Recursive Entropic Time framework, a theory proposing that subjective time is not fixed but instead emerges from neural systems involved in interpretation and association. We hypothesize that the brain uses a divided system for processing time. Primary sensory cortices handle objective clock-based time, while higher-order associative cortices construct subjective time through a mechanism in which the rate of temporal flow is inversely influenced by the amount of information being processed. To test this theory, we conducted a two-part investigation. In the first part, we used a public dataset involving brain scans of subjects under the influence of a hallucinogenic substance. This revealed that the Recursive Entropic Time model had greater effectiveness in associative regions of the brain compared to primary sensory areas. This finding suggested a region-specific effect rather than a global one. In the second part, we examined brain activity during a temporal reproduction task and analyzed two trials where participants produced nearly identical time durations. Despite the behavioral similarity, the information processing differed between the trials. The Recursive Entropic Time model accurately predicted these outcomes by reflecting internal durations derived from the information load. These findings support Recursive Entropic Time as a falsifiable and mechanistic explanation of how the brain constructs subjective time. We argue that time, as it is experienced, is not a simple reflection of external reality but a mental construction shaped by higher cognition. This framework provides a measurable and testable method for understanding subjective time and may lead to applications such as brain-based time atlases and insights into cognitive disorders.

The brain excels at extracting meaning from noisy and degraded input, yet the computational principles that underlie this robustness remain unclear. We propose a theory of spatiotemporal abstraction (STA), in which localized cortical networks integrate inputs across space and time to produce multi-scale, concept-level representations that remain stable despite loss of detail. We demonstrate how this principle explains a long-standing paradox of how cochlear implant patients can understand speech despite severely scrambled neural patterns. STA provides a unified framework that explains fundamental questions: Why do we have so many neurons that respond very similarly in one cortical location? Why do we have different inhibitory neurons? It also forces us to re-examine long-standing explanations of memory, creativity, illusions, attractor dynamics, excitatory-to-inhibitory balance, and the structure and purpose of the ubiquitous canonical circuits seen throughout the brain. We conclude with STA implications for improving neural implants and artificial neural networks.