Top Tweets for #Simbrain
We’ve added a graphical interface to the neuroevolution framework in #Simbrain. Watch as genes mutate and see how this impacts fitness. In this example, two networks controlling two agents are evolved and fitness corresponds to how much they’ve eaten.
I’m thrilled to announce the release of #Simbrain 4, which I have been working on for over 10 years! For the last five of those years I have met almost every weeknight with Yulin Li and we have pair-programmed our way through the entire app, adding hundreds of new features. 1/
I stumbled on what I believe is an “Aizawa Attractor”, a chaotic attractor in which states travel from one side to another of an ellipsoid along spiral trajectories. It showcases the visualization capabilities of #Simbrain and also shows how discovery is facilitated by Simbrain.
I wanted to share this cool thank you card from Suzanne Garcia. She took my class on neural networks and made a card describing her college development in the style of a #Simbrain network.
Check out my new website design! 🎉 We’ve embedded #Simbrain right in the browser—move nodes, tweak activations, add connections, and more! Try it out at https://t.co/gOJTWsjn95. Hover over my name and click ? for commands. #AI #NeuralNetworks
Hi everyone, test, test is this working? I’m stepping up my social media game with a new tool. Stay tuned for more regular posts in these categories: #Simbrain, dynamical systems, phenomenology, and serious games. Bonus point if you can figure out what movie this is from...
It's been a hectic few weeks and so I am late posting about this cool poster Kanly Thao made as part of https://t.co/rIeJcq0DYm. He evolved a #simbrain network for optimal resource pursuit (get food when hungry, but don't waste energy). Shown here is a top performer.
A #Simbrain reproduction of McCulloch and Pitt's (1943) account of the heat illusion. Fun to see the model come alive, but also reveals what I suspect was an unintended consequence of the model. 1/5.
Getting close on this old challenge. Sure the cows are grazing backwards, but it works! Group fitness is based (roughly) on number of dandelions jointly eaten. A first demo of multi-agent evolution in #Simbrain.1/
Game on! Now on my todo list: build a toy model of cow grazing in #simbrain. Target data right outside the window.
#Simbrain 3.06 is out (https://t.co/Za8UVbUZBG). Even though most of my current work is on 4.0 (beta by Fall, I hope?), I am still actively maintaining 3. A few highlights. 1/
Implementation of @jbenfalandays' object-tracking network in #Simbrain. An agent with an array of sensors and 2 effectors learns to track cheese rotating around it. Shown is a run from a random initial state. Within a few rotations it succeeds! 1/n
Amazing how compelling Braitenberg vehicles can be, despite their simplicity. Shown here is preliminary work modeling isopods in #Simbrain with Peter Hinow and Spencer Franks. Just wire up a pursuer, add a dash of noise, and voila!
A #Simbrain reproduction of McCulloch and Pitt's (1943) account of the heat illusion. Fun to see the model come alive, but also reveals what I suspect was an unintended consequence of the model. 1/5.
Congratulations to @gaminoksteph for being among the first to complete a degree in @UCMCogSci
's new Master's in Cognitive & Information Sciences program! 🥳 Her thesis was a UX evaluation of #Simbrain. 1/
I've finally publicly released my co-authored, open source, #Simbrain-based textbook on neural networks in cognitive science. https://t.co/n2lDJqUoD0 It has some cool features I'll describe here. 1/
Finally implemented array-based spiking networks in #Simbrain. Thousands of integrate and fire neurons reacting to a changing input, with a "jump and decay" post synaptic response. 1/
Thanks to @multiscalebio for pointing me to CellPAINT just now. Build a cell in your browser. Amazing! https://t.co/hiKdCpbGdE. A little like #simbrain but for cells.
The edge of chaos in #simbrain. Two identical networks and a time series showing hamming difference between their states. Perturb one, and differences fade, chaotically expand, or (at the edge) ripple and die off, which is optimal for complex computation.
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