Chopra, Y. | bioRxiv Science

bioRxiv2025

Nanoscale Lattice Organization of Molecular Condensates Drives Compositional Degeneracy in Synaptic Plasticity

Synaptic plasticity is essential for neuronal communication, involving coordinated structural, molecular and functional changes that are shaped by the nanoscale alterations of the active zone and postsynaptic density. Emerging evidence suggests that synapses function as complex information processing machines, where unique molecular assemblies shape transmission properties. Central to this is the organization of voltage-gated calcium channels (VGCCs) and Bassoon within active zones. Utilizing advanced techniques like liquid-liquid phase separation, super-resolution microscopy, and data-driven models of synaptic transmission, we reveal how nanoscale "compositional degeneracy" in Bassoon and VGCCs enables synapses to achieve functional adaptability through multiple molecular configurations. By modulation of local uncertainty and implementing probabilistic inference, synapses fine-tune transmission efficiency by regulating dynamic entropy and free energy. These principles are especially evident during homeostatic scaling, where synaptic scaling mechanisms differ with neuronal maturity. This study highlights how distinct thermodynamic states in VGCC and Bassoon organization optimize information transfer at different plasticity stages. Our findings propose a refined framework for understanding synaptic transmission as an adaptable, entropy-modulated process, balancing resilience and efficiency.

neuroscience↗

bioRxiv2024

Diverse perceptual biases emerge from Hebbian plasticity in a recurrent neural network model

Perceptual biases offer a glimpse into how the brain processes sensory stimuli. While psycho-physics has uncovered systematic biases such as contraction (stored information shifts towards a central tendency), and repulsion (the current percept shifts away from recent percepts), a unifying neural network model for how such seemingly distinct biases emerge from learning is lacking. Here, we show that both contractive and repulsive biases emerge from continuous Hebbian plasticity in a single recurrent neural network. We test the model in four different datasets, two sensory modalities and three experimental paradigms: two working memory tasks, a reference memory task, and a novel "one-back task" that we designed to test the robustness of the model. We find excellent agreement between model predictions and experimental data without fine-tuning the model to any particular paradigm. These results show that apparently contradictory perceptual biases can in fact emerge from a simple local learning rule in a single recurrent region of the brain.

neuroscience↗