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Ayvazian-Hancock, A.

Publications and source records attributed to Ayvazian-Hancock, A..

2 recordsLinked to original sources

Discovery of the Honeycomb Synapse in Spinal Motor Circuits

The structural and molecular diversity of synapses in the nervous system contributes to the specialisation of neural circuits underlying diverse behaviours. We have discovered a morphologically distinct postsynaptic specialisation in the mammalian spinal cord. We have named this the Honeycomb Synapse based on its elaborate postsynaptic nanostructure, comprising rings formed of ~6 scaffolding protein domains that create multiple perforations throughout the large postsynaptic domain. Analysis of the organisation of other synaptic proteins reveals that Honeycomb Synapses harbour mixed signalling properties structurally facilitated by a postsynaptic scaffold matrix supporting chemical transmission, with gap junction proteins occupying some of the perforations. We reveal anatomical diversity in the presence of Honeycomb Synapses on populations of -motoneurons across the lumbar spinal cord in mice from approximately 2 weeks of age through to adulthood. The Honeycomb Synapse was found to be a subtype of synapse within the Ia afferent monosynaptic stretch reflex circuit. Finally, we have identified that Honeycomb Synapses are highly vulnerable to degeneration in two different genetically engineered mouse models of Amyotrophic Lateral Sclerosis (ALS), contributing to monosynaptic stretch reflex circuit dysfunction. These findings suggest that synaptic diversity within circuits may confer selective vulnerability to distinct synaptic subclasses.

neuroscience↗

Predictive coding video models capture dorsal parietal representations and human judgments for surfaces defined by motion

Interacting with the world relies on knowing its three-dimensional structure, yet vision receives a stream of two-dimensional images. Recovering 3D geometry from dynamic input is therefore foundational to vision, but lacks a computational account. Here we identify one, using artificial neural networks to predict human behavior and neuronal responses from the two major visual pathways in macaques. Using videos of camouflaged objects whose shape is revealed through motion, we found both pathways carry information for motion-defined surfaces, while neurons in parietal cortex most closely track what humans perceive. Only predictive coding video models--trained to fill in missing content in natural videos--reproduced both neuronal responses and human behavior. Predictive coding thus emerges as a principle that yields a brain-like geometric world model of the physical world.

neuroscience↗