sdleiF | ||
int channelCount | Number of output channels | |
NeuroClass classObject RO | Neuron class for this neuron | |
Creature creature RO | Gets owner creature | |
float currState | Current neuron state (channel 0)When read, it behaves just like the 'state' field. When written, changes the current neuron state immediately, which disturbs the regular synchronous NN operation. This feature should only be used while controlling the neuron 'from outside' (like a neuro probe) and not in the neuron definition. See also: Neuro.hold | |
NeuroDef def RO | Neuron definition from which this live neuron was built | |
int getInputCount RO | Get input count | |
int hold0 .. 1 (false/true) | Hold state"Holding" means keeping the neuron state as is, blocking the regular neuron operation. This is useful when your script needs to inject some control signals into the NN. Without "holding", live neurons would be constantly overwriting your changes, and the rest of the NN could see inconsistent states, depending on the connections. Setting hold=1 ensures the neuron state will be only set by you, and not by the neuron. The enforced signal value can be set using Neuro.currState before or after setting hold=1. Set hold=0 to resume normal operation. | |
float inputSum RO | Full signal sum | |
MechJoint mechjoint RO | MechJoint objectThe MechJoint object where this neuron is located | |
MechPart mechpart RO | MechPart objectThe MechPart object where this neuron is located | |
NeuroProperties neuroproperties RO | Custom neuron fieldsNeurons can have different fields depending on their class. Script neurons have their fields defined using the "property:" syntax. If you develop a custom neuron script you should use the NeuroProperties object for accessing your own neuron fields. The Neuro.neuroproperties property is meant for accessing the neuron fields from the outside script. Examples: var c=Populations.createFromString("X[N]"); Simulator.print("standard neuron inertia="+c.getNeuro(0).neuroproperties.in); c=Populations.createFromString("X[Nn,e:0.1]"); Simulator.print("noisy neuron error rate="+c.getNeuro(0).neuroproperties.e); The Interface object can be used to discover which fields are available for a certain neuron object: c=Populations.createFromString("X[N]"); var iobj=Interface.makeFrom(c.getNeuro(0).neuroproperties); var i; for(i=0;i<iobj.size;i++) Simulator.print(iobj.getId(i)+" ("+iobj.getName(i)+")"); | |
float position_x RO | Position x | |
float position_y RO | Position y | |
float position_z RO | Position z | |
Orient relative_orient RO | Relative orientation | |
XYZ relative_pos RO | Relative position | |
NeuroSignals signals RO | Signals | |
float state | Neuron state (channel 0)When read, returns the current neuron state. When written, sets the 'internal' neuron state that will become current in the next step. Typically you should use this field, and not currState. | |
float weightedInputSum RO | Full weighted signal sum | |
snoitcnuF | ||
function getInputChannelCount(int input)returns int | Get channel count for input | |
function getInputState(int input)returns float | Get input signal | |
function getInputStateChannel(int input, int channel)returns float | Get input signal from channel | |
function getInputSum(int input)returns float | Get signal sum | |
function getInputWeight(int input)returns float | Get input weight | |
function getStateChannel(int channel)returns float | Get state for channel | |
function getWeightedInputState(int input)returns float | Get weighted input signal | |
function getWeightedInputStateChannel(int input, int channel)returns float | Get weighted input signal from channel | |
function getWeightedInputSum(int input)returns float | Get weighted signal sumUses any number of inputs starting with the specified input. getWeightedInputSum(0)=weightedInputSum | |
function setCurrStateChannel(int channel, float value)doesn't return a value | Set current neuron state for channelAnalogous to "currState". | |
function setStateChannel(int channel, float value)doesn't return a value | Set state for channel |
Global context | >> | Experiment definition | >> | Neuron definitions |