One of the problems that connectionist models need to confront is that inputs are temporally sequenced, so that in order to combine constituent parts, the network must retain information about recently processed parts. Two possible approaches are the use of simple recurrent networks (SRNs) and, in localist networks, sustained activation. SRNs use one-to-one feedback connections from the hidden layer to special context units aligned with the previous layer (normally the input layer or perhaps a secondary hidden layer), in effect storing their current outputs in those context units. Thus at the next cycle, the hidden units can use their own previous outputs, along with the new inputs from the input layer, to determine their next outputs. In localist models it is common to assume that once a unit (standing for a particular concept) becomes active, it stays active for some length of time, so that multiple concepts corresponding to multiple parts of the same sentence, and their properties, can be simultaneously active. A problem that arises is how the properties of an entity that are active at a given point in time can be properly tied to that entity, and not to other activated entities. (This is the variable binding problem, which has spawned a variety of approaches—see Browne and Sun 1999). One solution is to assume that unit activation consists of pulses emitted at a globally fixed frequency, and pulse trains that are in phase with one another correspond to the same entity (e.g., see Henderson 1994).  Much current connectionist research borrows from symbolic processing perspectives, by assuming that parsing assigns linguistic phrase structures to sentences, and treating the choice of a structure as simultaneous satisfaction of symbolic linguistic constraints (or biases). Also, more radical forms of hybridization and modularization are being explored, such as interfacing a NN parser to a symbolic stack, or using a neural net to learn the probabilities needed in a statistical parser, or interconnecting the parser network with separate prediction networks and learning networks.  For an overview of connectionist sentence processing and some hybrid methods (see Crocker 2010).