Biology subjects

Ferreira Castro, A.

Publications and source records attributed to Ferreira Castro, A..

3 recordsLinked to original sources

bioRxiv2025

Synaptic density and relative connectivity conservation maintain circuit stability across development

As bodies grow during postembryonic and postnatal development, nervous systems must expand to preserve circuit integrity. To investigate how circuits retain stable wiring and function throughout development, we combined synaptic-level resolution electron microscopy (EM) with computational modeling in the Drosophila larval nociceptive system. Based on EM data, we generated the "contactome"--the set of synaptic membrane contacts--of this circuit across development to evaluate how different mechanisms contribute to wiring stability. Specifically, we investigated three mechanisms: correlation-based plasticity and synaptic scaling, which modify synaptic strength, and structural plasticity, which preserves synaptic density. We found that synaptic sizes remain largely stable across development, and synapses between the same pre- and postsynaptic neurons do not correlate in size, suggesting that synaptic scaling and correlation-based plasticity play a limited role in shaping connectivity. In contrast, dendritic synaptic density remains invariant despite a previously reported fivefold increase in neuron size and synapse number. This conservation requires increased axonal presynaptic density to compensate for unequal axonal and dendritic growth. As neurons grow, this adjustment is necessary to maintain the relative synaptic input associated with each presynaptic partner across development. Our EM analysis and modeling show that conserving relative connectivity and synaptic density is sufficient to maintain consistent postsynaptic responses across development, highlighting these conserved structural features as key contributors to circuit stability during growth.

neuroscience↗

bioRxiv2025

Physical Network Constraints Define the Lognormal Architecture of the Brain's Connectome

The brain has long been conceptualized as a network of neurons connected by synapses. However, attempts to describe the connectome using established network science models have yielded conflicting outcomes, leaving the architecture of neural networks unresolved. Here, by performing a comparative analysis of eight experimentally mapped connectomes, we find that their degree distributions cannot be captured by the well-established random or scale-free models. Instead, the node degrees and strengths are well approximated by lognormal distributions, although these lack a mechanistic explanation in the context of the brain. By acknowledging the physical network nature of the brain, we show that neuron size is governed by a stochastic multiplicative process, which allows us to analytically derive the lognormal nature of the neuron length distribution. Our framework not only predicts the degree and strength distributions across each of the eight connectomes, but also yields a series of empirically falsifiable relationships between different neuron characteristics. The resulting multiplicative network represents a novel architecture for network science, whose distinctive quantitative features bridge critical gaps between neural structure and function, with implications for brain dynamics, robustness, and synchronization.

neuroscience↗

bioRxiv2024

Invariant synaptic density across species

The nervous system scales with animal size while preserving function, yet the principles underlying this stability remain unclear. Here, we analyse the ultra-structure and connectivity of thousands of neuronal cells across species, including fly, zebrafish, mouse, and human, and found a conserved feature that stabilises neuronal responses across scales: an average of one synapse per micrometre of dendritic cable. We show that the appropriate synaptic density is shaped by correct axon-dendrite positioning and synaptic transmission during development. We find that this specific synaptic density is linked to wiring optimisation in neurons, where dendrites minimise cable length and conduction delays. Finally, simulations indicate invariant synaptic density as a neuronal design principle, conserved for its ability to synergise with other cell-intrinsic properties to stabilise voltage responses across cell types and species.

neuroscience↗