(A) The response to a 1 s long step current is displayed for three sample neurons from the Allen brain cell database (Allen Institute, 2018b). The cell id and sweep number identify the exact cell recording in the Allen brain cell database. (B) The response of a simple leaky integrate-and-fire (LIF) neuron model with SFA to the 1-s-long step current. Neuron parameters used: top row , , ; middle row , , ; bottom row , , . (C) Symbolic architecture of recurrent spiking neural network (SNN) consisting of LIF neurons with and without SFA. (D) Minimal SNN architecture for solving simple instances of STORE-RECALL tasks that we used to illustrate the negative imprinting principle. It consists of four subpopulations of input neurons and two LIF neurons with SFA, labeled NR and NL, that project to two output neurons (of which the stronger firing one provides the answer). (E) Sample trial of the network from (D) for two instances of the STORE-RECALL task. The input ‘Right’ is routed to the neuron NL, which fires strongly during the first STORE signal (indicated by a yellow shading of the time segment), that causes its firing threshold (shown at the bottom in blue) to strongly increase. The subsequent RECALL signal (green shading) excites both NL and NR, but NL fires less, that is, the storing of the working memory content ‘Right’ has left a ‘negative imprint’ on its excitability. Hence, NR fires stronger during recall, thereby triggering the answer ‘Right’ in the readout. After a longer pause, which allows the firing thresholds of NR and NL to reset, a trial is shown where the value ‘Left’ is stored and recalled.