is carried out. In this search procedure, recognition neurons are disabled one by one by the reset function until the vigilance parameter is overcome by a recognition match. In particular, at each cycle of the search procedure the most active recognition neuron is selected and then switched off, if its activation is below the vigilance parameter (note that it thus releases the remaining recognition neurons from its inhibition). If no committed recognition neuron's match overcomes the vigilance parameter, then an uncommitted neuron is committed and its weights are adjusted towards matching the input vector. The vigilance parameter has considerable influence on the system: higher vigilance produces highly detailed memories (many, fine-grained categories), while lower vigilance results in more general memories (fewer, more-general categories). Training There are two basic methods of training ART-based neural networks: slow and fast. In the slow learning method, the degree of training of the recognition neuron's weights towards the input vector is calculated to continuous values with differential equations and is thus dependent on the length of time the input vector is presented. With fast learning, algebraic equations are used to calculate degree of weight adjustments to be made, and binary values are used. While fast learning is effective and efficient for a variety of tasks, the slow learning method is more biologically plausible and can be used with continuous-time networks (i.e. when the input vector can vary continuously). Types ART 1 is the simplest variety of ART networks, accepting only binary inputs. ART 2 extends network capabilities to support continuous inputs. ART 2-A is a streamlined form of ART-2 with a drastically accelerated runtime, and with qualitative results being only rarely inferior to the full ART-2 implementation. ART 3 builds on ART-2 by simulating rudimentary neurotransmitter regulation of synaptic activity by incorporating simulated sodium (Na+) and calcium (Ca2+) ion concentrations into the system's equations, which results in a more physiologically realistic means of partially inhibiting categories that trigger mismatch resets. ARTMAP also known as Predictive ART, combines two slightly modified ART-1 or ART-2 units into a supervised learning structure where the first unit takes the input data and the second unit takes the correct output data, then used to make the minimum possible adjustment of the vigilance parameter in the first unit in order to make the correct classification. Fuzzy ART implements fuzzy logic into ART's pattern recognition, thus enhancing generalizability. An optional (and very