Learning spatiotemporal signals using a recurrent spiking network that discretizes time
Learning spatiotemporal signals using a recurrent spiking network that discretizes time
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DOI：
10.1371/journal.pcbi.1007606
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发表时间：
2020-01-01
影响因子：
4.3
通讯作者：
Clopath, Claudia
中科院分区：
文献类型：
--
作者：
Maes, Amadeus;Barahona, Mauricio;Clopath, Claudia
大脑有能力在广泛的时间尺度上学习灵活的行为。以前的研究已经成功地建立了尖峰网络模型，可以学习各种计算任务，但通常所涉及的学习在生物学上并不合理。在这里，我们研究了一个模型，该模型使用生物学上合理的神经元和学习规则来学习特定的计算任务：时空序列的学习（即，诸如空间、频率或信道索引之类的可观测量的时间演变）。该模型架构通过将时间信息与其他维度分离来促进学习。时间分量被编码到在行为时间尺度上表现出顺序动态的递归网络中，并且该网络然后被用作引擎以驱动对空间信息进行编码的读出神经元（即，第二个维度）。我们证明，该模型可以学习复杂的时空尖峰动态，如一只鸟的歌曲，并重放歌曲鲁棒自发。学习产生时空序列是一个共同的任务，大脑必须解决。大脑可以使用相同的神经基质来产生不同的顺序行为。大脑学习和编码这些任务的方式仍然未知，因为当前的计算模型通常不使用现实的生物学上合理的学习。在这里，我们提出了一个模型，其中兴奋性和抑制性生物物理神经元的尖峰递归网络驱动读出层：驱动递归网络的动态被训练来编码时间，然后通过读出神经元映射来编码另一个维度，例如空间或相位。不同的时空模式可以通过突触权重被学习和编码到遵循共同的赫布学习规则的读出神经元。我们证明，该模型能够学习时空动态的时间尺度上的行为相关的，我们表明，学习的序列是鲁棒性重放期间的自发活动的制度。
Author summary The brain has the ability to learn flexible behaviours on a wide range of time scales. Previous studies have successfully built spiking network models that learn a variety of computational tasks, yet often the learning involved is not biologically plausible. Here, we investigate a model that uses biological-plausible neurons and learning rules to learn a specific computational task: the learning of spatiotemporal sequences (i.e., the temporal evolution of an observable such as space, frequency or channel index). The model architecture facilitates the learning by separating the temporal information from the other dimension. The time component is encoded into a recurrent network that exhibits sequential dynamics on a behavioural time scale, and this network is then used as an engine to drive the read-out neurons that encode the spatial information (i.e., the second dimension). We demonstrate that the model can learn complex spatiotemporal spiking dynamics, such as the song of a bird, and replay the song robustly spontaneously.Learning to produce spatiotemporal sequences is a common task that the brain has to solve. The same neural substrate may be used by the brain to produce different sequential behaviours. The way the brain learns and encodes such tasks remains unknown as current computational models do not typically use realistic biologically-plausible learning. Here, we propose a model where a spiking recurrent network of excitatory and inhibitory biophysical neurons drives a read-out layer: the dynamics of the driver recurrent network is trained to encode time which is then mapped through the read-out neurons to encode another dimension, such as space or a phase. Different spatiotemporal patterns can be learned and encoded through the synaptic weights to the read-out neurons that follow common Hebbian learning rules. We demonstrate that the model is able to learn spatiotemporal dynamics on time scales that are behaviourally relevant and we show that the learned sequences are robustly replayed during a regime of spontaneous activity.