DOI: 10.5061/dryad.5x69p8d52
title: Data from: Awake responses suggest inefficient dense coding in the mouse retina

description: The structure and function of the vertebrate retina have been extensively
 studied across species with an isolated, ex vivo preparation. Retinal
 function in vivo, however, remains elusive, especially in awake animals.
 Here we performed single-unit extracellular recordings in the optic tract
 of head-fixed mice to compare the output of awake, anesthetized, and ex
 vivo retinas. While the visual response properties were overall similar
 across conditions, we found that awake retinal output had in general 1)
 faster kinetics with less variability in the response latencies; 2) a
 larger dynamic range; and 3) higher firing activity, by ~20 Hz on average,
 for both baseline and visually evoked responses. Our modeling analyses
 further showed that such awake response patterns convey comparable total
 information but less efficiently, and allow for a linear population
 decoder to perform significantly better than the anesthetized or ex vivo
 responses. These results highlight distinct retinal behavior in awake
 states, in particular suggesting that the retina employs dense coding in
 vivo, rather than sparse efficient coding as has been often assumed from
 ex vivo studies.
description: We performed single-unit
 extracellular recordings in the optic tract of head-fixed mice under
 anesthetized or awake conditions and monitored the retinal output
 responses to visual stimuli in vivo. For full details, please see the
 methods in the associated article.
description: *** # Awake responses suggest inefficient dense coding in the mouse retina
 Hiroki Asari asari@embl.it 2023-Oct-05 *** This repository contains all
 the data files and codes used in the following paper: Boissonnet T,
 Tripodi M, and Asari H (2023) Awake responses suggest inefficient dense
 coding in the mouse retina bioRxiv 2022.02.15.480512 doi:
 https://doi.org/10.1101/2022.02.15.480512 If you have any question, please
 address correspondence to: Hiroki Asari, PhD Epigenetics and Neurobiology
 Unit (EMBL Rome) European Molecular Biology Laboratory Via Ramarini 32,
 00015 Monterotondo, Italy tel: +39 06 90091439 email: asari@embl.it web:
 https://www.embl.org/groups/asari/ # INSTALLATION Download and install
 theonerig in a conda env (https://github.com/Tom-TBT/theonerig). Within
 the downloaded folder, in a terminal, run: conda create -n tor python=3.6
 conda activate tor pip install packaging pip install -e . # FILE
 DESCRIPTION AND USAGE ## DATA -&gt; cells\_dataframe.csv Pandas
 dataframe generated within "0 - Dataframe generation.ipynb" See
 "cells\_dataframe\_keys.txt" and "Read df
 examples.ipynb" for the dataframe key description and data retreaval.
 -&gt; unique\_cells.csv Indices of cells selected manually in each
 recording. -&gt; chirp\_stim\_trace This directory contains stimulus
 intensity data for each "chirp" stimulus type in NumPy binary
 format: * chirp\_am\_intensity.npy: Chirp-AM stimulus *
 chirp\_am\_old\_intensities.npy: Chirp-AM stimulus (old version) *
 chirp\_fe\_intensities.npy: Chirp-FM stimulus *
 chirp\_fm\_old\_intensities.npy: Chirp-FM stimulus (old version) See
 methods of the associated publication for details. -&gt;
 in\_vitro\_data This directory contains *in vitro* RGC recording data
 sets, reorganized from: Vlasiuk and Asari (2021) Feedback from retinal
 ganglion cells to the inner retina doi: 10.5281/zenodo.5057577 See
 "ReadMe.txt" in the directory for details. -&gt;
 in\_vivo\_data This directory contains *in vivo* optic tract recording
 data sets, collected in the associated study: * awake: awake chronic
 recordings * fmm: recordings under FMM anesthesia * isoflurane: recordings
 under isoflurane anesthesia * new: awake acute recordings The data for
 each recording is stored as "Record\_Master" object in HDF5
 format, including the followings: * main\_tp: main time points in samples
 (to which each data is aligned) * S\_matrix: N-by-M matrix containing
 spikes trains of N-cells for M-frames * signals: stimulus marker
 (photodiode) signals * checkerboard: X-by-Y-by-T matrix containing
 presented stimulus intensity data (X-by-Y checkers for T frames) *
 chirp\_am, chirp\_freq\_epoch, fullfield\_flicker: presented stimulus
 intensity data * moving\_gratings: moving grating parameters [bar widths,
 angle, moving speed] * eye\_tracking: eye tracking data [X-center,
 Y-center, X-diameter, Y-diameter, rotation] * treadmill: treadmilll data
 (-10 to 10 V, where negative value for running forward) See "0 -
 Dataframe generation.ipynb" and documentions of "theonerig"
 (https://github.com/Tom-TBT/theonerig) for data import and processing
 examples. -&gt; individual\_cells\_pdfs This directory contains PDF
 files summarizing the cells' responses, generated within "1 -
 Units PDF export.ipynb" -&gt; matlab This directory contains M-
 and MAT-files for running the analyses in MATLAB for Figures 7 and 8 and
 Supplementary Figures 1-4. * Fig7\_Information.m, Fig7\_Information.mat:
 for Figure 7 * Fig8\_decoding.m, Fig8\_decoding.mat: for Figure 8 *
 SFig1\_behavior.m, SFig1\_behavior.mat: for Supplementary Figure 1 *
 SFig2\_correlation.m, SFig2\_correlation.mat: for Supplementary Figure 2 *
 SFig3\_BatchEffect.m, SFig3\_BatchEffect.mat: for Supplementary Figure 3 *
 SFig4\_coding.m, SFig4\_coding.mat: for Supplementary Figure 4 ## USAGE
 Run the Jupyter notebooks in the numerical order to reproduce the analysis
 results in the associated publication. See comments inside the Jupyter
 notebooks for details. -&gt; masks.py, shared\_functions.py,
 new\_functions.py Python scripts with code shared among the Jupyter
 notebooks. -&gt; 0 - Dataframe generation.ipynb Generates the
 dataframe from in vitro and in vivo data by processing the response of
 individual cells to stimuli, fit those response and compute various
 indexes. -&gt; 1 - Units PDF export.ipynb Export individual
 cells' response plots in a PDF format. -&gt; 2 - Units
 proportions.ipynb Run analyses for figure 2. -&gt; 3 - tSTA
 analysis.ipynb Run analyses for figures 5 and 6. -&gt; 4 - Chirp
 amplitude modulation modelling.ipynb Run analyses for figure 3. -&gt;
 5 - Chirp amplitude modulation modelling.ipynb Run analyses for figure 4.
 -&gt; Read df examples.ipynb Examples on how to explore
 "cells\_dataframe.csv"