Zemliak Viktoria, Pipa Gordon, Nieters Pascal
Institute of Cognitive Science, Osnabrück University, Osnabrück, Germany.
Frankfurt Institute of Advanced Studies, Frankfurt, Germany.
PLoS Comput Biol. 2025 Aug 1;21(8):e1013304. doi: 10.1371/journal.pcbi.1013304. eCollection 2025 Aug.
Familiarity memory enables recognition of previously encountered inputs as familiar without recalling detailed stimuli information, which supports adaptive behavior across various timescales. We present a spiking neural network model with lateral connectivity shaped by unsupervised spike-timing-dependent plasticity (STDP) that encodes familiarity via local plasticity events. We show that familiarity can be decoded from network activity using both frequency (spike count) and temporal (spike synchrony) characteristics of spike trains. Temporal coding demonstrates enhanced performance under sparse input conditions, consistent with the principles of sparse coding observed in the brain. We also show how connectivity structure supports each decoding strategy, revealing different plasticity regimes. Our approach outperforms LSTM in temporal generalizability on the continual familiarity detection task, with input stimuli being naturally encoded in the recurrent connectivity without a separate training stage.
熟悉度记忆能够识别先前遇到的输入为熟悉的，而无需回忆详细的刺激信息，这支持了跨各种时间尺度的适应性行为。我们提出了一种具有侧向连接的脉冲神经网络模型，该连接由无监督的脉冲时间依赖可塑性（STDP）塑造，通过局部可塑性事件对熟悉度进行编码。我们表明，可以使用脉冲序列的频率（脉冲计数）和时间（脉冲同步）特征从网络活动中解码熟悉度。时间编码在稀疏输入条件下表现出更高的性能，这与在大脑中观察到的稀疏编码原则一致。我们还展示了连接结构如何支持每种解码策略，揭示了不同的可塑性机制。我们的方法在连续熟悉度检测任务的时间泛化能力上优于长短期记忆网络（LSTM），输入刺激自然地编码在循环连接中，无需单独的训练阶段。
