Spiking neural networks (SNNs) promise low-power event-driven computation for temporally rich tasks, but commonly used neuron models often trade off gradient-based trainability, dynamical richness, and high activity sparsity. These limitations are acute in regression, where approximation error, noise and spike discretization can severely degrade continuous-valued outputs. Indeed, many state-of-the-art (SOTA) SNNs rely on simple phenomenological dynamics trained with surrogate gradients and offer limited control over spiking diversity and sparsity. To overcome such limitations, we introduce multi-timescale conductance spiking networks, a gradient-trainable framework in which neural dynamics emerge from shaping the current-voltage (I-V) curve by tuning fast, slow and ultra-slow conductances. This parametrization allows systematic control over excitability, can be implemented efficiently in analog circuits, and yields rich firing regimes including tonic, phasic and bursting responses within a single model. We derive a discrete-time formulation of these differentiable dynamics, enabling direct backpropagation through time without surrogate-gradient approximations. To probe both trainability and accuracy, we evaluate feedforward networks of these neurons at the predictability limit of Mackey-Glass time-series regression and compare them to baseline LIF and SOTA AdLIF networks. Our model outperforms LIF and AdLIF networks, while exhibiting substantially sparser activity from both communication and computational perspectives. These results highlight multi-timescale conductance spiking neurons as a promising building block for energy-aware temporal processing and neuromorphic implementation.

翻译：脉冲神经网络（SNNs）有望在时序丰富任务中实现低功耗事件驱动计算，但常用神经元模型往往在基于梯度的可训练性、动力学丰富性和高活动稀疏性之间进行权衡。这些局限性在回归任务中尤为突出——近似误差、噪声和脉冲离散化会严重降低连续值输出质量。事实上，许多最先进（SOTA）的SNNs依赖基于替代梯度的简单现象学动力学进行训练，对脉冲多样性和稀疏性的控制能力有限。为克服这些限制，我们提出多时间尺度电导脉冲网络——一种可梯度训练框架，其中神经动力学通过调节快、慢和超慢电导来塑造电流-电压（I-V）曲线。这种参数化方法能够系统性地控制兴奋性，可在模拟电路中高效实现，并在单一模型中产生包括强直、相位和爆发响应在内的丰富发放模式。我们推导了这些可微动力学的离散时间公式，使得无需替代梯度近似即可实现直接的时间反向传播。为检验可训练性与准确性，我们在Mackey-Glass时间序列回归的可预测性极限上评估了这些神经元的馈送网络，并与基线LIF和SOTA的AdLIF网络进行比较。我们的模型在性能上优于LIF和AdLIF网络，同时在通信和计算两个维度展现出显著更高的活动稀疏性。这些结果表明多时间尺度电导脉冲神经元有望成为面向能量感知时序处理和神经形态实现的关键构建模块。