Discussions for freeman lab members / collaborators / friends • gitter-robot syncs chat with IRC

Just thought everyone should see the illustration toward the bottom of this page:

I especially love the logo :)

awesome stuff!

quick question, knowing how neurons work in drosophila, how feasible is it to formulate a computational question in drosophila terms and have their behaviour compute the solution?

i.e., is it possible to say that a drosophila organism is going to behave in a way that can roughly be defined with a function of inputs, resulting in behaviour outputs, and then compute using those building blocks?

first somewhat working example of the curve store thing https://mathisonian.github.io/curve-store/ @mikolalysenko @freeman-lab

code for it is here https://github.com/mathisonian/curve-store/blob/master/examples/mouse-move.js

still need to a way to add bounds to the array via a ring buffer

@mathisonian !!!!

this is great!

:smile:

for the derivative, you could compute it analytically

by just taking the difference between two successive samples and dividing by the time between them

i think there are a lot of performance improvements to add, and making the default sampling options nice

@mikolalysenko that's what it should be doing but i implemented this in like 5 minutes https://github.com/mathisonian/curve-store/blob/master/src/samplers/index.js#L20-L29

yeah, this is really cool

have you got a plan for how to do recursive functions/reducers?

so one way to do it is by sampling values at previous times directly in the sample function https://github.com/mathisonian/curve-store/blob/master/src/samplers/index.js#L24

still trying to wrap my head around if this is enough / how to make it more efficient

i guess you would need to snap the lookup time certain value to be able to memoize the past results, but curious to hear what your thoughts are for handling that

(assuming thats what you meant)

well you might also want to do stuff like have some integrator which runs continuously

like suppose you have a physics solver which you are using to implement some animations

so you want to integrate forward in time as some dependent variables change

maybe you could just do it variationally though

like what if we only allowed physical constraints in lagrangian form?

this might be too slow

but it might also be really cool

hm interesting

conceptually lagrangian mechanics is way simpler to work with

though it is not widely known since it isn't taught in high school

it is a more declarative way to do physics though

i think we could have fast approximate integration in a fairly simply way with the current setup

that would be cool

need to read about the lagrangian stuff

in that pixar paper they talk a lot about variational methods for animation

ah cool. good reading for the train

it is probably the most influential paper in computer graphics for that fact alone

how would we enforce these constraints given that the current setup allows arbitrary functions?

so you have to solve some optimization problem to do it

lagrangian mechanics reformulates newtonian dynamics as an optimization problem

instead of integrating these forces, you end up finding a shortest (minimal cost) path in space time

the two formulations are equivalent though

but the neat thing with lagrangian mechanics is it is way simpler to add constraints to it

in newtonian dynamics you do all this horrible hacking to figure out how to enforce something like a distance constraint/non-intersection for collisions, etc.

in lagrangian mechanics you just have a constraint, that's it

ah thats pretty sweet

it is way more intuitive and simpler to compose

but it involves working with time as another dimension

i'm going to spend some time with that paper and try to understand it a little better

@mathisonian that purple square demo is awesome!

and i really like how simple the API is