AnimatLab versions

It combines biomechanical simulation and biologically realistic neural networks
2.1
Apr 3, 2015
Review
2.0
Jul 30, 2013
1.1
Feb 21, 2011
Review

What's new

v2.1 [Apr 3, 2015]
1. You can now add robot part interfaces to your existing simulations to control real hardware using the neural network control system from your simulation. Please see the Robotics documentation for more details.
2. AnimatLab now has a framework in place to allow physical devices to interact with a neural network in a simulation or on a robot. Right now only the wireless XBee commander joystick is supported, but I plan to add keyboard and standard joystick interactivity soon. Please see the remote control documentation for more details.
3. I have created realistic mesh files for all parts of the base Bioloid robot frame kit. They are available for free in the AnimatWarehouse.
4. Contact sensors can now become "sticky." Your neural network can turn on/off the stickiness of a contact sensor, and when it is on and comes into contact with another part in the scene they will stick together until stickiness is turned off. I used this in the robot arm simulation to allow the gripper to pick up parts. However, there are a number of other applications. For example, you could simulate a gecko's ability to walk up walls.
5. You can now specify a target data type for adapters. Previously the incoming data type for a node was hard-coded. Now it is possible for the user to change it. The main place where this is used is to allow you to have two adapters connected to a joint where one lets you control the target position and the other sets the target velocity.
6. You can now setup a delay buffer on any adapter, and you have the ability to disable all output from an adapter for a given period immediately after the simulation starts in order to allow the neurons in your simulation to settle down to a steady state.
7. Added a Modulate Neuron Property synapse to the Firing rate neural plug-in. This allows you to modify any of the properties of the post-synaptic neuron based on the firing rate of the pre-synaptic neuron.

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