Joint encoding of “what” and “when” predictions through error-modulated plasticity in biologically plausible spiking networks

The brain predicts not only what will occur, but also when it will occur and with what probability. We refer to this joint representation of identity, timing, and frequency-based probability as a complete prediction object. Existing computational models typically treat these dimensions separately or rely on biologically implausible learning rules. Here we show that a single population of spiking neurons can acquire and flexibly maintain a complete prediction object through local learning. Using a recurrent spiking network trained with an error-modulated, attention-gated Hebbian rule, we independently manipulated event identity, latency, and probability. The network developed time-locked anticipatory activity whose amplitude scaled with outcome probability and rapidly recalibrated when timing or probability statistics changed. Identity and timing self-organized into factorized subspaces within a shared neural population. These results suggest that mixed-selective cortical populations, coupled with neuromodulator-gated plasticity, may be sufficient to jointly encode and update multidimensional predictions within a recurrent circuit. A spiking network learns event identity, timing and probability through local, attention-gated Hebbian plasticity. Identity and timing remain separable, and local plasticity recalibrates to changing statistics more effectively than global rules.