Biology subjects

Ajabi, Z.

Publications and source records attributed to Ajabi, Z..

3 recordsLinked to original sources

bioRxiv2025

Distributed control circuits across a brain-and-cord connectome

Just as genomes revolutionized molecular genetics, connectomes (maps of neurons and synapses) are transforming neuroscience. To date, the only species with complete connectomes are worms1-3 and sea squirts4 (103-104 synapses). By contrast, the fruit fly is more complex (108 synaptic connections), with a brain that supports learning and spatial memory5,6 and an intricate ventral nerve cord analogous to the vertebrate spinal cord7-11. Here we report the first densely reconstructed adult fly connectome that unites the brain and ventral nerve cord, and we leverage this resource to investigate principles of neural control. We show that effector neurons (motor neurons, endocrine cells and efferent neurons targeting the viscera) are primarily influenced by sensory neurons in the same body part, forming local feedback loops. These local loops are linked by long-range circuits involving ascending and descending neurons organized into behavior-centric modules. Single ascending and descending neurons are often positioned to influence the voluntary movements of multiple body parts, together with the endocrine cells or visceral organs that support those movements. Brain regions involved in learning and navigation supervise these circuits. These results reveal an architecture that is distributed, parallelized and embodied, reminiscent of distributed control architectures in engineered systems12,13.

neuroscience↗

bioRxiv2021

Population dynamics of the thalamic head direction system during drift and reorientation

The head direction (HD) system is classically modeled as a ring attractor network1,2 which ensures a stable representation of the animals head direction. This unidimensional description popularized the view of the HD system as the brains internal compass3,4. However, unlike a globally consistent magnetic compass, the orientation of the HD system is dynamic, depends on local cues and exhibits remapping across familiar environments5. Such a system requires mechanisms to remember and align to familiar landmarks, which may not be well described within the classic 1-dimensional framework. To search for these mechanisms, we performed large population recordings of mouse thalamic HD cells using calcium imaging, during controlled manipulations of a visual landmark in a familiar environment. First, we find that realignment of the system was associated with a continuous rotation of the HD network representation. The speed and angular distance of this rotation was predicted by a 2nd dimension to the ring attractor which we refer to as network gain, i.e. the instantaneous population firing rate. Moreover, the 360-degree azimuthal profile of network gain, during darkness, maintained a memory trace of a previously displayed visual landmark. In a 2nd experiment, brief presentations of a rotated landmark revealed an attraction of the network back to its initial orientation, suggesting a time-dependent mechanism underlying the formation of these network gain memory traces. Finally, in a 3rd experiment, continuous rotation of a visual landmark induced a similar rotation of the HD representation which persisted following removal of the landmark, demonstrating that HD network orientation is subject to experience-dependent recalibration. Together, these results provide new mechanistic insights into how the neural compass flexibly adapts to environmental cues to maintain a reliable representation of the head direction.

neuroscience↗

bioRxiv2019

A zero-inflated gamma model for post-deconvolved calcium imaging traces

Calcium imaging is a critical tool for measuring the activity of large neural populations. Much effort has been devoted to developing \"pre-processing\" tools applied to calcium video data, addressing the important issues of e.g., motion correction, denoising, compression, demixing, and deconvolution. However, computational modeling of deconvolved calcium signals (i.e., the estimated activity extracted by a pre-processing pipeline) is just as critical for interpreting calcium measurements. Surprisingly, these issues have to date received significantly less attention. To fill this gap, we examine the statistical properties of the deconvolved activity estimates, and propose several density models for these random signals. These models include a zero-inflated gamma (ZIG) model, which characterizes the calcium responses as a mixture of a gamma distribution and a point mass which serves to model zero responses. We apply the resulting models to neural encoding and decoding problems. We find that the ZIG model out-performs simpler models (e.g., Poisson or Bernoulli models) in the context of both simulated and real neural data, and can therefore play a useful role in bridging calcium imaging analysis methods with tools for analyzing activity in large neural populations.

neuroscience↗