Nv network

The most basic component included in Nv Networks is the Nv neuron. The purpose of a Nv neuron is simply to take an input, do something with it, and give an output. The most common action of Nv neurons is to give a delay.

The standard for BEAM-based neurons is a capacitor that has one lead as an input and the other going into the input line of an inverter.^[2] That inverter's output is the output of the neuron. The capacitor lead that is inputting into the inverter is pulled to ground with a resistor. The neuron functions because when an input is received (positive power on the input line), it charges the capacitor. Once the input is lost (negative power on the input line), the capacitor discharges into the inverter, causing the inverter to produce an output that is passed to the next neuron. The rate that the capacitor discharges is tied to the resistor that is pulling the input to the inverter to the negative. The larger the resistor, the longer it will take for the capacitor to fully discharge, and the longer it will take for that neuron to completely fire.

There are many common network topologies used in BEAM robots, the most common of which are listed here.

Probably the most utilized Nv Net topology in BEAM, the Bicore consists of two neurons placed in a loop that alternates current to the output.^[3] Input into the loop is given in the form of changing the resistance in each separate Neuron, which changes the rate at which the Neuron discharges, affecting the pace at which the loop oscillates.

Another common topology is using two bicores in a master/slave layout where the master bicore leads the slave and sets the pace, while the slave bicore follows at an offset pace. This layout is most commonly used for dual-motor walkers.

Other larger network topologies include the Tricore, and Quadcore which are laid out in a similar way the bicore is, except with more Neurons in the loop. More complex networks exist, but are not as common due to the simplistic nature of BEAM.