Levodopa suppresses grid-like activity and impairs spatial learning in novel environments in healthy young adults.

Accumulated evidence from animal studies suggests a role for the neuromodulator dopamine in memory processes, particularly under conditions of novelty or reward. Our understanding of how dopaminergic modulation impacts spatial representations and spatial memory in humans remains limited. Recent evidence suggests age-specific regulation effects of dopamine pharmacology on activity in the medial temporal lobe, a key region for spatial memory. To which degree this modulation affects spatially patterned medial temporal representations remains unclear. We reanalyzed recent data from a pharmacological dopamine challenge during functional brain imaging combined with a virtual object-location memory paradigm to assess the effect of Levodopa, a dopamine precursor, on grid-like activity in the entorhinal cortex. We found that Levodopa impaired grid cell-like representations in a sample of young adults (n = 55, age = 26-35 years) in a novel environment, accompanied by reduced spatial memory performance. We observed no such impairment when Levodopa was delivered to participants who had prior experience with the task. These results are consistent with a role of dopamine in modulating the encoding of novel spatial experiences. Our results suggest that dopamine signaling may play a larger role in shaping ongoing spatial representations than previously thought.

A large-scale neurocomputational model of spatial cognition integrating memory with vision.

We introduce a large-scale neurocomputational model of spatial cognition called 'Spacecog', which integrates recent findings from mechanistic models of visual and spatial perception. As a high-level cognitive ability, spatial cognition requires the processing of behaviourally relevant features in complex environments and, importantly, the updating of this information during processes of eye and body movement. The Spacecog model achieves this by interfacing spatial memory and imagery with mechanisms of object localisation, saccade execution, and attention through coordinate transformations in parietal areas of the brain. We evaluate the model in a realistic virtual environment where our neurocognitive model steers an agent to perform complex visuospatial tasks. Our modelling approach opens up new possibilities in the assessment of neuropsychological data and human spatial cognition.

Sensory coding and contrast invariance emerge from the control of plastic inhibition over emergent selectivity.

Visual stimuli are represented by a highly efficient code in the primary visual cortex, but the development of this code is still unclear. Two distinct factors control coding efficiency: Representational efficiency, which is determined by neuronal tuning diversity, and metabolic efficiency, which is influenced by neuronal gain. How these determinants of coding efficiency are shaped during development, supported by excitatory and inhibitory plasticity, is only partially understood. We investigate a fully plastic spiking network of the primary visual cortex, building on phenomenological plasticity rules. Our results suggest that inhibitory plasticity is key to the emergence of tuning diversity and accurate input encoding. We show that inhibitory feedback (random and specific) increases the metabolic efficiency by implementing a gain control mechanism. Interestingly, this led to the spontaneous emergence of contrast-invariant tuning curves. Our findings highlight that (1) interneuron plasticity is key to the development of tuning diversity and (2) that efficient sensory representations are an emergent property of the resulting network.

Predictive Place-Cell Sequences for Goal-Finding Emerge from Goal Memory and the Cognitive Map: A Computational Model.

Hippocampal place-cell sequences observed during awake immobility often represent previous experience, suggesting a role in memory processes. However, recent reports of goals being overrepresented in sequential activity suggest a role in short-term planning, although a detailed understanding of the origins of hippocampal sequential activity and of its functional role is still lacking. In particular, it is unknown which mechanism could support efficient planning by generating place-cell sequences biased toward known goal locations, in an adaptive and constructive fashion. To address these questions, we propose a model of spatial learning and sequence generation as interdependent processes, integrating cortical contextual coding, synaptic plasticity and neuromodulatory mechanisms into a map-based approach. Following goal learning, sequential activity emerges from continuous attractor network dynamics biased by goal memory inputs. We apply Bayesian decoding on the resulting spike trains, allowing a direct comparison with experimental data. Simulations show that this model (1) explains the generation of never-experienced sequence trajectories in familiar environments, without requiring virtual self-motion signals, (2) accounts for the bias in place-cell sequences toward goal locations, (3) highlights their utility in flexible route planning, and (4) provides specific testable predictions.