The most well-known associative instantiation base is a class of networks called Connectionist networks (see  the entry on connectionism).  Connectionist networks are sometimes pitched at the psychological level (see, e.g., Elman 1991; Elman et al. 1996; Smolensky 1988). This amounts to the claim that models of algorithms embedded in the networks capture the essence of certain mental processes, such as associative learning. Other times connectionist networks are said to be models of neural activity (“neural networks”). Connectionist networks consist in sets of nodes, generally input nodes, hidden nodes, and output nodes. Input nodes are taken to be analogs of sensory neurons (or sub-symbolic sensory representations), output nodes the analog of motor neurons (or sub-symbolic behavioral representations), and hidden nodes are  stand-ins for all other  neurons.[27]  The network consists in these nodes being connected to each other with varying strengths. The topology of the connections gives one an associative mapping of the system, with the associative weights understood as the differing strengths of connections. On the psychological reading, these associations are functionally defined; on the neurological reading, they are generally understood to be representing synaptic conductance (and are the analogs of dendrites).[28] Prima facie, these networks are purely associative and do not contain propositional elements, and the nodes themselves are not to be equated with single representational states (such as concepts; see, e.g., Gallistel and King 2009).