We describe and demonstrate the key features of a neu- romorphic, analog VLSI chip (termed F-LANN) hosting 128 integrate-and-fire (IF) neurons with spike-frequency adap- tation, and 16 384 plastic bistable synapses implementing a self-regulated form of Hebbian, spike-driven, stochastic plasticity. We were successfully able to test and verify the basic operation of the chip as well as its main new fea- ture, namely the synaptic configurability. This configura- bility enables us to configure each individual synapse as either excitatory or inhibitory and to receive either recur- rent input from an on-chip neuron or AER (Address Event Representation)-based input from an off-chip neuron. It?s also possible to set the initial state of each synapse as po- tentiated or depressed, and the state of each synapse can be read and stored on a computer. The main aim of this chip is to be able to efficiently perform associative learning ex- periments on a large number of synapses. In the future we would like to connect up multiple F-LANN chips together to be able to perform associative learning of natural stimulus sets.