>>102850

<'>continue the mission'

https://qagg.news/?wordsearch=on&q=mission { Q Drops: @23 }

4510 (10) (1)

Q !!Hs1Jq13jV6 06/24/2020 13:06:33 ID: f4ac89

Anonymous 06/24/2020 13:04:50 ID:a4abe1

>>9730975

I do solemnly swear (or affirm) that I will support and defend the Constitution of the United States against all enemies, foreign and domestic;

that I will bear true faith and allegiance to the same; that I take this obligation freely, without any mental reservation or purpose of evasion;

and that I will well and faithfully discharge the duties of the office on which I am about to enter: So help me God. WWG1WGA

>>9731023

<>Take the oath.

<>Mission forward.

Q

4509 (18) (9)

Q !!Hs1Jq13jV6 06/24/2020 13:01:36 ID: f4ac89

You have been selected to help serve your Country.

Never retreat from the battlefield [Twitter, FB, etc.].

Use other platforms as a form of centralized command and control.

Organize and connect [bridge through linking].

Source meme(s) material from battlefield and/or garage [highlight & share][take & drop]

Mission 1: Dispute [reject] propaganda push through posting of research and facts

Mission 2: Support role of other digital soldiers [one falls another stands (rises)]

Mission 3: Guide [awaken] others through use of facts [DECLAS 1-99 material and other relevant facts]

and memes [decouple MSDNC control of info stream] _ask 'counter' questions

to initiate 'thought' vs repeat [echo] of MSDNC propaganda

Mission 4: Learn use of camouflage [digitally] _primary account suspended-terminated _use of secondary

Mission 5: Identify strengths / weaknesses [personal and designated target(s)] re: Twitter & FB [+other]

example re: meme(s) failure to read through use of ALGO [think Tron (MCP_master control program)]

_dependence on person-to-person capture [slow response time unidentified user(s)]

Game theory.

Information warfare.

Welcome to the Digital Battlefield.

Together we win.

Q

https://qagg.news/?q=%7BT+12%3A17%3A%7D { Q Drops: 3 }

Date/Time Constraint selected: T 12:17:

Full Search Query: {T 12:17:}

1217 (11) (2)

Q !xowAT4Z3VQ 04/21/2018 11:33:06 ID: 4b14c6

https://wikileaks.org/podesta-emails/emailid/7524

Q

RE: Follow-up Media Call

From:jbenenson@bsgco.com

To: nmerrill@hrcoffice.com, robbymook2015@gmail.com

CC: cheryl.mills@gmail.com, pir@hrcoffice.com, huma@hrcoffice.com, gruncom@aol.com, john@algpolling.com, dschwerin@hrcoffice.com

john.podesta@gmail.com, Jim.Margolis@gmmb.com

Date: 2015-01-14 01:08

Subject: RE: Follow-up Media Call

Rumble

— Adorable older woman receives an #FJB t-shirt

Also...

YouTube has reportedly begun purging its massive video platform of all video references of "Let’s Go Brandon,"

citing "medical misinformation".

#FJB #TruthSocial #LetsGoBrandon

https://beckernews.com/youtube-bans-lets-go-brandon-leaves-up-videos-with-death-threats-against-donald-trump-42626/

>>102870

>https://www.nature.com/articles/416274a

>'''Breaking the neural code"

under "Figures:"

Figure 1: Artificially grown networks of neurons could be the key to deciphering the intricate code of neural messages.

>pulled up a very interesting looking PDF (see CAP)

>but it won't download

what link /sawz ?

can attempt

http://apps.bio.uci.edu/bio199/faculty/171/view

http://www.erikphoel.com/uploads/1/7/8/8/17883727/erik_hoel_cv_website.pdf { CV / resume - das-STING !}

{ excerpt from Honors/Awards/Grants }:

b. Awarded grants (contributed to or PI)

DARPA – Breaking the Code: engineering neural controllers and behavior in the hydra (~$7,500,000);

Templeton World Charity Foundation – Grant ID: TWCF 0067/AB41 (~$2,500,000);

https://www.uni-bremen.de/en/university/campus/calendar/event?tx_cal_controller%5Bday%5D=20&tx_cal_controller%5Bmonth%5D=11&tx_cal_controller%5Btype%5D=tx_cal_phpicalendar&tx_cal_controller%5Buid%5D=11514&tx_cal_controller%5Bview%5D=event&tx_cal_controller%5Byear%5D=2020&cHash=9894c07fb75e204e994d4943edfe0e1b

{ https://archive.ph/GUiXY }

Breaking the neural code of a cnidarian

Organizer: Prof. Dr. Hans-Günther Döbereiner

Location: Online-Seminar: Anmeldung per E-Mail an ampe@uni-bremen.de

Start Time: 20. November 2020, 16:00

End Time: 20. November 2020, 18:00

Rafael Yuste, Columbia University

Abstract

The small freshwater cnidarian Hydra vulgarishas one of the simplest nervous systems in the animal kingdom[1],

yet exhibits surprisingly complex behaviors, like somersaulting[2].

Due to its transparency, its complete neural[3] and muscle activity[4] can be effectively imaged.

Our goal to take advantage of this experimental angle to "break the neural code" of Hydra:

to understand the complete set of transformations from neural activity to muscle activation to behavior.

1. Bosch, T.C., et al., Back to the Basics: Cnidarians Start to Fire. Trends Neurosci, 2017. 40(2): p. 92-105.

2. Han, S., et al., Comprehensive machine learning analysis of Hydra behavior reveals a stable basal behavioral repertoire. Elife, 2018. 7.

3. Dupre, C. and R. Yuste, Non-overlapping Neural Networks in Hydra vulgaris. Curr Biol, 2017.

4. Szymanski, J.R. and R. Yuste, Mapping the Whole-Body Muscle Activity of Hydra vulgaris. Curr Biol, 2019. 29(11): p. 1807-1817 e3.

4>>102870

~ linky ~ link

>>102874

>followed up on your hydra dig

>>102883

'''ML-trackinq.pdf'''

got it, no err mesg

>>102889

forgot to cap is 1 page only

Using Machine-Learning for tracking individual neurons in the small cnidarian Hydra

The small cnidarian Hydra possesses one of the simplest “brain” of the animal kingdom.

Therefore, the tracking of all his individual neurons is possible and might lead to the first entire

decoding of a brain (i.e. the understanding of how interacting neurons can integrate the

environment’s cues, compute the animal’s state and trigger appropriate behaviors).

This project will aim at developing single-particle tracking that will integrate machine-learning

for finding the optimal associations between moving particles.

Figure 1: Breaking the neural code of Hydra consists in relating the sequential activity of neurons or ensemble of neurons,

observed with fluorescence imaging (a) to each specific action of the animal (f), as they document significant relations of

causality between the time series of individual neurons’ activities. Our multi-step approach consists in (b) long-term, single

particle tracking of ≈1000−2000 neurons in freely-behaving and deforming animal (manual tracking over only 200 frames

(20 s) is shown here (adapted from [3]), (c) extraction of individual fluorescence traces and spikes (highlighted with black

stars for neuron #1), (d) statistical inference of neurons’ functional connectivity (line thickness indicate connection weights)

and, (e) clustering into significant neuronal ensembles. Finally, recasting the activity and functional connectivity of individual

neurons in an optimal control theory framework will help to understand how the coordinated activity of hundreds to

thousands neurons control the animal’s state (f). Methodological developments (aims 1&2) are highlighted in red and green.