Among the main features of biological intelligence are energy efficiency, capacity for continual adaptation, and risk management via uncertainty quantification. Neuromorphic engineering has been thus far mostly driven by the goal of implementing energy-efficient machines that take inspiration from the time-based computing paradigm of biological brains. In this paper, we take steps towards the design of neuromorphic systems that are capable of adaptation to changing learning tasks, while producing well-calibrated uncertainty quantification estimates. To this end, we derive online learning rules for spiking neural networks (SNNs) within a Bayesian continual learning framework. In it, each synaptic weight is represented by parameters that quantify the current epistemic uncertainty resulting from prior knowledge and observed data. The proposed online rules update the distribution parameters in a streaming fashion as data are observed. We instantiate the proposed approach for both real-valued and binary synaptic weights. Experimental results using Intel's Lava platform show the merits of Bayesian over frequentist learning in terms of capacity for adaptation and uncertainty quantification.

翻译：生物情报的主要特征包括能源效率、持续适应的能力和通过不确定性量化进行风险管理。迄今为止,神经地貌工程的驱动力主要来自实施节能机器的目标,这种机器是从生物大脑基于时间的计算范式中得到启发的。在本文中,我们采取步骤设计神经地貌系统,这些系统能够适应变化中的学习任务,同时提出经充分校准的不确定性量化估计。为此,我们从Bayesian持续学习的框架内获得神经网络(SNNS)的在线学习规则。其中,每种合成重量都由参数组成,这些参数可以量化先前知识和观察到的数据所产生的当前特征不确定性。拟议的在线规则随着数据的观察,以流态方式更新分配参数。我们从真实价值和二元合成重量的角度对拟议的方法进行回调。使用Intel's Lava平台的实验结果显示Bayesian在适应能力和不确定性量化能力方面优于经常学习的优点。