@article {69629, title = {The first complete 3D reconstruction and morphofunctional mapping of an insect eye}, journal = {bioRxiv}, year = {2024}, month = {2024 Oct 01}, abstract = {

The structure of compound eyes in arthropods has been the subject of many studies revealing important biological principles. However, until recently, these studies were constrained by the two-dimensional nature of available ultrastructural data. Here, by taking advantage of the novel three-dimensional ultrastructural dataset obtained using volume electron microscopy (vEM), we present the first cellular-level reconstruction of the whole compound eye of an insect, the extremely miniaturized parasitoid wasp Megaphragma viggianii. The compound eye of the female M. viggianii consists of 29 ommatidia and contains 478 cells. Despite the almost anucleate brain, all cells of the compound eye possess nuclei. Like in larger insects, the dorsal rim area (DRA) of the M. viggianii eye contains ommatidia that putatively specialize in the polarized light detection as reflected in their corneal and retinal morphology. We report the presence of three {\textquoteright}ectopic{\textquoteright} photoreceptors. Our results offer new insights into the miniaturization of compound eyes and scaling of sensory organs in general.

}, doi = {10.1101/2024.09.30.615804}, url = {https://doi.org/10.1101/2024.09.30.615804}, author = {Makarova, Anastasia A. and Chua, Nicholas J. and Diakova, Anna V. and Desyatirkina, Inna A. and Gunn, Pat and Pang, Song and Xu, C Shan and Hess, Harald F and Chklovskii, Dmitri B. and Polilov, Alexey A.} } @conference {65046, title = {A Hebbian/Anti-Hebbian network derived from online non-negative matrix factorization can cluster and discover sparse features.}, booktitle = {2014 48th Asilomar Conference on Signals, Systems and Computers2014 48th Asilomar Conference on Signals, Systems and Computers}, year = {2014}, month = {2015 Mar 02}, publisher = {IEEE}, organization = {IEEE}, address = {Pacific Grove, CA, USA}, abstract = {

Olshausen and Field (OF) proposed that neural computations in the primary visual cortex (V1) can be partially modelled by sparse dictionary learning. By minimizing the regularized representation error they derived an online algorithm, which learns Gabor-filter receptive fields from a natural image ensemble in agreement with physiological experiments. Whereas the OF algorithm can be mapped onto the dynamics and synaptic plasticity in a single-layer neural network, the derived learning rule is nonlocal - the synaptic weight update depends on the activity of neurons other than just pre- and postsynaptic ones {\textendash} and hence biologically implausible. Here, to overcome this problem, we derive sparse dictionary learning from a novel cost-function - a regularized error of the symmetric factorization of the input{\textquoteright}s similarity matrix. Our algorithm maps onto a neural network of the same architecture as OF but using only biologically plausible local learning rules. When trained on natural images our network learns Gabor-filter receptive fields and reproduces the correlation among synaptic weights hard-wired in the OF network. Therefore, online symmetric matrix factorization may serve as an algorithmic theory of neural computation.\ 

}, doi = {10.1109/ACSSC.2014.7094553}, url = {http://ieeexplore.ieee.org/document/7094553/http://xplorestaging.ieee.org/ielx7/7066991/7094373/07094553.pdf?arnumber=7094553}, author = {Pehlevan, Cengiz and Chklovskii, Dmitri B.} } @conference {65048, title = {Sparse LMS via online linearized Bregman iteration.}, booktitle = {ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year = {2014}, month = {2014 Jul 14}, publisher = {IEEE}, organization = {IEEE}, address = {Florence, Italy}, abstract = {

We propose a version of least-mean-square (LMS) algorithm for sparse system identification. Our algorithm called online linearized Bregman iteration (OLBI) is derived from minimizing the cumulative prediction error squared along with an l\ 1\ -l\ 2\ norm regularizer. By systematically treating the non-differentiable regularizer we arrive at a simple two-step iteration. We demonstrate that OLBI is bias free and compare its operation with existing sparse LMS algorithms by rederiving them in the online convex optimization framework. We perform convergence analysis of OLBI for white input signals and derive theoretical expressions for the steady state mean square deviations (MSD). We demonstrate numerically that OLBI improves the performance of LMS type algorithms for signals generated from sparse tap weights.

}, doi = {10.1109/ICASSP.2014.6855000}, url = {http://ieeexplore.ieee.org/document/6855000/http://xplorestaging.ieee.org/ielx7/6844297/6853544/06855000.pdf?arnumber=6855000}, author = {Hu, Tao and Chklovskii, Dmitri B.} }