Figure 4. A synthetic gene network for associative learning.

(A) Schematic diagram of the PFNM associative learning network. Positive and negative regulations are indicated by arrows and hammerheads, respectively. Input 1 stimulates nodes u, v and y. Input 2 stimulates nodes w and y. (B) Simulation of the behaviour of the network. Either input 1 or 2 alone leads to a weak activation of the output y, at times t1 and t2. When both inputs 1 and 2 are applied simultaneously, a ‘memory’ is formed by a self-sustained expression of u due to its positive auto-regulation. Because of this memory a subsequent input 1 or input 2 alone can cause a strong induction of y. In this way the network has learned to associate inputs 1 and 2. This memory can be erased by a sufficiently large input 1 (due to the direct activation of v), bringing the system back to the default state. This scheme is proposed here by Y.S. and M.C.R. and the simulation was performed by Y.S.