{"ID":2854479,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.14382","arxiv_id":"2510.14382","title":"Joint encoding of \"what\" and \"when\" predictions through error-modulated plasticity in biologically-plausible spiking networks","abstract":"The brain anticipates future events using internal models that specify not only what will occur, but also when it will occur and with what probability. We refer to this joint specification of identity, timing, and likelihood as a complete prediction object. Existing computational models typically capture identity and timing separately, omit probability as an explicit representational dimension, or rely on biologically implausible global learning rules. Here we show that a single population of spiking neurons can acquire and flexibly maintain a complete prediction object through biologically grounded learning. We implemented a heterogeneous Izhikevich spiking reservoir with multiplexed readouts trained by an error-modulated, attention-gated three-factor Hebbian rule, and tested it on a task that independently manipulates event identity, latency, and probability. The network develops time-locked anticipatory activity whose amplitude scales with outcome probability and rapidly adapts when timing or probability statistics change. Identity and timing components self-organize into near-orthogonal readout subspaces within a shared neural population, demonstrating that multidimensional predictive structure can emerge without anatomical modularization or global error broadcast. Compared with least-squares-based approaches, local gated plasticity enables stable recalibration under nonstationary conditions. These results suggest that cortical mixed-selective populations, coupled with neuromodulator-gated synaptic plasticity, may be sufficient to jointly encode and update identity, timing, and probability within a single recurrent circuit. Flexible predictive cognition may therefore arise from generic population dynamics shaped by local learning rules rather than from specialized predictive modules.","short_abstract":"The brain anticipates future events using internal models that specify not only what will occur, but also when it will occur and with what probability. We refer to this joint specification of identity, timing, and likelihood as a complete prediction object. Existing computational models typically capture identity and t...","url_abs":"https://arxiv.org/abs/2510.14382","url_pdf":"https://arxiv.org/pdf/2510.14382v3","authors":"[\"Yohei Yamada\",\"Zenas C. Chao\"]","published":"2025-10-16T07:30:34Z","proceeding":"q-bio.NC","tasks":"[\"q-bio.NC\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}