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<p>We learn to recognize a vast array of familiar objects, a process involving learning-related changes in inferotemporal cortex (IT) activity. A challenge to discovering mechanisms of familiarity learning is that it spans multiple timescales from minutes to days, and is accompanied by simultaneous changes in cellular, synaptic, and network properties. We leverage an integrated experimental-theoretical approach, using IT recordings in two male macaques during familiarity learning within and across sessions to infer underlying plasticity mechanisms. We identified two timescales of learning-related changes spanning minutes to days, consistent with distinct synaptic and cellular mechanisms. Across sessions, averaged responses gradually decreased with familiarity, consistent with synaptic plasticity. In contrast, within-session changes, including rapid response decay and increased spontaneous activity, aligned with intrinsic plasticity mechanisms. Recurrent networks endowed with learning rules inferred from experiments replicated the observed learning dynamics, supporting our hypothesis of distinct learning mechanisms - slow, synaptic plasticity at long timescales and fast, intrinsic plasticity at short timescales.</p>

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Visual learning at fast and slow timescales is driven by distinct plasticity rules in primate inferotemporal cortex

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月4日

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  • Krithika Mohan,
  • Ulises Pereira-Obilinovic,
  • Stanislav Srednyak,
  • Nicolas Brunel,
  • David J Freedman

摘要

We learn to recognize a vast array of familiar objects, a process involving learning-related changes in inferotemporal cortex (IT) activity. A challenge to discovering mechanisms of familiarity learning is that it spans multiple timescales from minutes to days, and is accompanied by simultaneous changes in cellular, synaptic, and network properties. We leverage an integrated experimental-theoretical approach, using IT recordings in two male macaques during familiarity learning within and across sessions to infer underlying plasticity mechanisms. We identified two timescales of learning-related changes spanning minutes to days, consistent with distinct synaptic and cellular mechanisms. Across sessions, averaged responses gradually decreased with familiarity, consistent with synaptic plasticity. In contrast, within-session changes, including rapid response decay and increased spontaneous activity, aligned with intrinsic plasticity mechanisms. Recurrent networks endowed with learning rules inferred from experiments replicated the observed learning dynamics, supporting our hypothesis of distinct learning mechanisms - slow, synaptic plasticity at long timescales and fast, intrinsic plasticity at short timescales.