Learning and representation of hierarchical concepts in hippocampus and prefrontal cortex

A key aspect of conceptual knowledge is that it can be flexibly applied at different levels of abstraction, implying a hierarchical organization. It is yet unclear how this hierarchical structure is acquired and represented in the brain. Here we investigate the computations underlying the acquisition and representation of the hierarchical structure of conceptual knowledge in the hippocampal-prefrontal system of 32 human participants (22 females). We assessed the hierarchical nature of learning during a novel tree-like categorization task via computational model comparisons. The winning model allowed to extract and quantify estimates for accumulation and updating of hierarchical compared with single-feature-based concepts from behavior. We find that mPFC tracks accumulation of hierarchical conceptual knowledge over time, and mPFC and hippocampus both support trial-to-trial updating. As a function of those learning parameters, mPFC and hippocampus further show connectivity changes to rostro-lateral PFC, which ultimately represented the hierarchical structure of the concept in the final stages of learning. Our results suggest that mPFC and hippocampus support the integration of accumulated evidence and instantaneous updates into hierarchical concept representations in rostro-lateral PFC.SIGNIFICANCE STATEMENT A hallmark of human cognition is the flexible use of conceptual knowledge at different levels of abstraction, ranging from a coarse category level to a fine-grained subcategory level. While previous work probed the representational geometry of long-term category knowledge, it is unclear how this hierarchical structure inherent to conceptual knowledge is acquired and represented. By combining a novel hierarchical concept learning task with computational modeling of categorization behavior and concurrent fMRI, we differentiate the roles of key concept learning regions in hippocampus and PFC in learning computations and the representation of a hierarchical category structure

Improving audio-visual temporal perception through training enhances beta-band activity

Multisensory integration strongly depends on the temporal proximity between two inputs. In the audio-visual domain, stimulus pairs with delays up to a few hundred milliseconds can be perceived as simultaneous and integrated into a unified percept. Previous research has shown that the size of this temporal window of integration can be narrowed by feedback-guided training on an audio-visual simultaneity judgment task. Yet, it has remained uncertain how the neural network that processes audio-visual asynchronies is affected by the training. In the present study, participants were trained on a 2-interval forced choice audio-visual simultaneity judgment task. We recorded their neural activity with magnetoencephalography in response to three different stimulus onset asynchronies (0 ms, each participant’s individual binding window, 300 ms) before, and one day following training. The Individual Window stimulus onset asynchrony condition was derived by assessing each participant’s point of subjective simultaneity. Training improved performance in both asynchronous stimulus onset conditions (300 ms, Individual Window). Furthermore, beta-band amplitude (12–30 Hz) increased from pre-compared to post-training sessions. This increase moved across central, parietal, and temporal sensors during the time window of 80–410 ms post-stimulus onset. Considering the putative role of beta oscillations in carrying feedback from higher to lower cortical areas, these findings suggest that enhanced top-down modulation of sensory processing is responsible for the improved temporal acuity after training. As beta oscillations can be assumed to also preferentially support neural communication over longer conduction delays, the widespread topography of our effect could indicate that training modulates not only processing within primary sensory cortex, but rather the communication within a large-scale network

A Stochastic Description of Dictyostelium Chemotaxis

Chemotaxis, the directed motion of a cell toward a chemical source, plays a key role in many essential biological processes. Here, we derive a statistical model that quantitatively describes the chemotactic motion of eukaryotic cells in a chemical gradient. Our model is based on observations of the chemotactic motion of the social ameba Dictyostelium discoideum, a model organism for eukaryotic chemotaxis. A large number of cell trajectories in stationary, linear chemoattractant gradients is measured, using microfluidic tools in combination with automated cell tracking. We describe the directional motion as the interplay between deterministic and stochastic contributions based on a Langevin equation. The functional form of this equation is directly extracted from experimental data by angle-resolved conditional averages. It contains quadratic deterministic damping and multiplicative noise. In the presence of an external gradient, the deterministic part shows a clear angular dependence that takes the form of a force pointing in gradient direction. With increasing gradient steepness, this force passes through a maximum that coincides with maxima in both speed and directionality of the cells. The stochastic part, on the other hand, does not depend on the orientation of the directional cue and remains independent of the gradient magnitude. Numerical simulations of our probabilistic model yield quantitative agreement with the experimental distribution functions. Thus our model captures well the dynamics of chemotactic cells and can serve to quantify differences and similarities of different chemotactic eukaryotes. Finally, on the basis of our model, we can characterize the heterogeneity within a population of chemotactic cells

Mapping Conceptual Knowledge Acquisition in the Hippocampal System

Contains fulltext : 219703.pdf (publisher's version ) (Open Access)Radboud University, 23 juni 2020Promotor : Döller, C.F.A. Co-promotor : Fernandez, G.189 p