Neural populations with strong excitatory recurrent connections can support bistable states in their mean firing rates. Multiple fixed points in a network of such bistable units can be used to model memory retrieval and pattern separation. The stability of fixed points may change on a slower timescale than that of the dynamics due to short-term synaptic depression, leading to transitions between quasi-stable point attractor states in a sequence that depends on the history of stimuli. To better understand these behaviors, we study a minimal model, which characterizes multiple fixed points and transitions between them in response to stimuli with diverse time- and amplitude-dependencies. The interplay between the fast dynamics of firing rate and synaptic responses and the slower timescale of synaptic depression makes the neural activity sensitive to the amplitude and duration of square-pulse stimuli in a nontrivial, history-dependent manner. Weak cross-couplings further deform the basins of attraction for different fixed points into intricate shapes. We find that while short-term synaptic depression can reduce the total number of stable fixed points in a network, it tends to strongly increase the number of fixed points visited upon repetitions of fixed stimuli. Our analysis provides a natural explanation for the system’s rich responses to stimuli of different durations and amplitudes while demonstrating the encoding capability of bistable neural populations for dynamical features of incoming stimuli.

中文翻译：

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具有突触抑制的神经回路中的吸引子状态迭代。

具有强烈兴奋性经常性联系的神经群体可以在其平均射击率上支持双稳态。这种双稳态单元网络中的多个固定点可用于对内存检索和模式分离进行建模。由于短期突触抑制，固定点的稳定性可能在比动态动力学更慢的时间尺度上发生变化，从而导致准稳定点吸引子状态之间的过渡取决于刺激的历史。为了更好地理解这些行为，我们研究了一个最小模型，该模型描述了多个固定点以及它们之间的变化，以响应具有不同时间和幅度依赖性的刺激。放电速率和突触反应的快速动力学之间的相互作用以及突触抑制的较慢时间尺度之间的相互作用，使得神经活动以非平凡的，历史依赖的方式对方脉冲刺激的幅度和持续时间敏感。弱的交叉耦合进一步将不同固定点的吸引盆变形为复杂的形状。我们发现，虽然短期的突触抑制可以减少网络中稳定的固定点的总数，但它往往会大大增加重复固定刺激后访问的固定点的数目。我们的分析为系统对不同持续时间和振幅的丰富响应提供了自然的解释，同时展示了双稳态神经群体对传入刺激的动态特征的编码能力。