Helmbrecht 2018

Helmbrecht, T. O., dal Maschio, M., Donovan, J. C., Koutsouli, S., & Baier, H. (2018). Topography of a Visuomotor Transformation. Neuron.

In ZF, two distinct pathways from OT to HB: one for prey, another for avoidance. Rev: damage to OT prevents orienting towards prey. Optogenetics: stimulate diff neurons in the OT, detect motor outputs. Clustering: 2 distinct behav: escapes and approaches. Moreover, retinotopic position and eye position are integrated, to calculate the maneuver. Combined with whole-brain gcamp img, observed targeted HB neurons activated. Then traced individ neurons and classified into 8 classes, based on projection targets, including ipsi- and contra- to HB, several distinct type of commissural, etc. Up-down retinotopy is less precisely encoded than LR (aka rostro-caudal).

Haas 2001

Haas, K., Sin, W. C., Javaherian, A., Li, Z., & Cline, H. T. (2001). Single-cell electroporationfor gene transfer in vivo. Neuron, 29(3), 583-591.

Among other things, describes that each cell in XL OT is about 20 um in diameter.

Williamson 2018

Williamson, W. R., Peek, M. Y., Breads, P., Coop, B., & Card, G. M. (2018). Tools for Rapid High-Resolution Behavioral Phenotyping of Automatically Isolated Drosophila. Cell Reports, 25(6), 1636-1649.

[Williamson 2018] Describes some outrageously sci-fi equipment for automated tracking of fruitflies, one by one, with a dispensor, an optogenetic setup, and virtual reality dome that employes a conical mirror! Very inspiring, and also a great video-dictionary of elementary behaviors (fixed action patterns?) towards the end.

Abbas 2017

Abbas, F., Triplett, M. A., Goodhill, G. J., & Meyer, M. P. (2017). A Three-Layer Network Model of Direction Selective Circuits in the Optic Tectum. Frontiers in neural circuits, 11, 88.

[Abbas 2017] Define 3 layers as: axons of RGCs, supervisial inhibitory interneurons (SIN), and principal or paraventricular neurons (PVN). ZF larvae. Claim that at first 2 levels there are only 3 principal directions (a trifoil, equally split), but they get recalculated into 4 at last layer (rostro-caudal is absent in the RGCs input, but is present in OT). In intro: there seems to be a controversy in ZF, about how exactly inhibition is tuned in direction-selective circuits in the OT, and whether it is sharpening, or defining the responses. Model: just 10 neurons, as each one represents an entire population; 3 layers, ReLU units, diff equations (not too discrete). Custom set input sensitivity curves and tuned connectivity scheme, reproduces tuning from Ca img experiments

Bassett 2018

Bassett, D. S., Zurn, P., & Gold, J. I. (2018). On the nature and use of models in network neuroscience. Nature Reviews Neuroscience, 1.

These days "a model" can mean many different things. Great figure with a summary of typical graph questions (communities, hubs, etc.) and extensions (dynamics, simplices). Hierarchy of models: descriptive (when graph makes it easy); predictive (dynamics, or edge dynamics). Then introduces another 3D classification: data vs theory, coarse vs low-level, and functional vs structural.

Greenland 2016

Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N., & Altman, D. G. (2016). Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. European journal of epidemiology, 31(4), 337-350.

Great summary of neo-Fisherian interpretation of p-values (amont other things).

Gilpin 2018

Gilpin, W. (2018). Cellular automata as convolutional neural networks. arXiv preprint arXiv:1809.02942.

Tried to predict cellular automata with deep networks (ReLU). Curious measure of entropy of activation patterns for almost-binary networks (just go through all possible inputs, look at the distribution of activation patterns, calculate H). But then at later layers entropy drops, and it drops faster if automata rules are simpler.