Biology subjects

Leeuwen, C. v.

Publications and source records attributed to Leeuwen, C. v..

3 recordsLinked to original sources

bioRxiv2023

Retinal waves in adaptive rewiring networks orchestrate convergence and divergence in the visual system

Spontaneous retinal wave activity shaping the visual system is a complex neurodevelopmental phenomenon. Retinal ganglion cells are the hubs through which activity diverges throughout the visual system. We consider how these divergent hubs emerge, using an adaptively rewiring neural network model. Adaptive rewiring models show in a principled way how brains could achieve their complex topologies. Modular small-world structures with rich club effects and circuits of convergent-divergent units emerge as networks evolve, driven by their own spontaneous activity. Arbitrary nodes of an initially random model network were designated as retinal ganglion cells. They were intermittently exposed to the retinal waveform, as the network evolved through adaptive rewiring. A significant proportion of these nodes developed into divergent hubs within the characteristic complex network architecture. The proportion depends parametrically on the wave incidence rate. Higher rates increase the likelihood of hub formation, while increasing the potential of ganglion cell death. In addition, direct neigbours of designated ganglion cells differentiate like amacrine cells. The divergence observed in ganglion cells resulted in enhanced convergence downstream, suggesting that retinal waves control the formation of convergence in LGN. We conclude that retinal waves stochastically control the distribution of converging and diverging activity in evolving complex networks.

neuroscience↗

bioRxiv2019

Neural Correlates of Task-related Refixation Behaviour

Information uptake during scene viewing under free viewing conditions is crucially determined by the scanning plan. This plan is determined both by top-down and bottom-up factors. To capture top-down factors affecting saccade planning, we compared EEG between first fixations and refixations on items varying in task-relevance. First fixations and refixations impose different working memory costs because first fixations involve encoding of new items whereas refixations involve rehearsal of existing items in working memory. These memory requirements also differ with the task-relevance of the item being encoded. Together, these two factors of task-relevance and memory processes related to refixation behavior would affect saccade planning. In a visual task involving search and memorization of multiple targets, we compared saccade-related potentials (SRPs) between first fixations and refixations for task-relevant (target) and task-irrelevant (distractor) items. We assessed the interval preceding a saccade away from the fixation of interest. Studying this presaccadic interval revealed how mechanisms related to saccade preparation are affected by task-relevance and refixation behavior. We found higher SRP amplitudes for first fixations than refixations over the occipital region for task-relevant items only. Our findings indicate that saccade planning is modulated by both task-relevance of an item and working memory load.

neuroscience↗

bioRxiv2019

Adaptive rewiring based on diffusion balances stability and plasticity in weighted networks while evolving ‘brain-like’ structure

Activity dependent plasticity is the brains mechanism for reshaping neural connections. Representing activity by graph diffusion, we model plasticity as adaptive rewiring. The rewiring involves adding shortcut connections where diffusion on the graph is intensive while pruning underused ones. This process robustly steers initially random networks to high-levels of structural complexity reflecting the global characteristics of brain anatomy: modular or centralized small world topologies, depending on overall diffusion rate. We extend this result, known from binary networks, to weighted ones in order to evaluate the flexibility of their evolved states. Both with normally- and lognormally-distributed weights, networks evolve modular or centralized topologies depending on a single control parameter, the diffusion rate, representing a global homeostatic or normalizing regulation mechanism. Once settled, normally weighted networks lock into their topologies, whereas lognormal ones allow flexible switching between them, tuned by the diffusion rate. For a small range of diffusion rates networks evolve the largest variety of topologies: modular, centralized or intermediate. Weighted networks in the transition range show topological but not weighted rich-club structure matching empirical data in the human brain. The simulation results allow us to propose adaptive rewiring based on diffusion as a parsimonious model for activity-dependent reshaping of the brains connections.\n\nAuthor SummaryThe brain is adapting continuously to a changing environment by strengthening or adding new connections and weakening or pruning existing ones. This forms the basis of flexible and adaptable behaviors. On the other hand, uncontrolled changes to the wiring can compromise the stability of the brain as an adaptive system. We used an abstract model to investigate how this basic problem could be addressed from a graph-theoretical perspective. The model adaptively rewires an initially randomly connected network into a more structured one with properties akin to the human brain, such as small worldness and rich club structure. The adaptive changes made to the network follow the heat diffusion, an abstract representation of brain functional connectivity. Moreover, depending on a parameter of the model, the heat diffusion rate, either modular or centralized connectivity patterns emerge, both found across different regions of the brain. For a narrow range of intermediate heat diffusion rates, networks develop a full range from modular to centralized connectivity patterns. Once settled into a connectivity pattern networks with normally distributed weights lock into that state, whereas networks with lognormally distributed weights show greater flexibility to adjust, while maintaining their small-world and rich club properties. Networks with lognormally distributed weights, therefore, show the combination of stability and flexibility needed to address the fundamental requirements of adaptive networks.

neuroscience↗