Classification of Whisker Deflections From Evoked Responses in the Somatosensory Barrel Cortex With Spiking Neural Networks.
Classification of Whisker Deflections From Evoked Responses in the Somatosensory Barrel Cortex With Spiking Neural Networks.
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DOI：
10.3389/fnins.2022.838054
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发表时间：
2022
影响因子：
4.3
通讯作者：
Vassanelli, Stefano
中科院分区：
文献类型：
--
作者：
Petschenig, Horst;Bisio, Marta;Maschietto, Marta;Leparulo, Alessandro;Legenstein, Robert;Vassanelli, Stefano
关键词：
基于尖峰的神经形态硬件在低能量脑机接口方面具有很大的潜力，从而为神经假体提供了一种新的范例，其中硅中的尖峰神经元读出并控制大脑回路的活动。神经形态处理器可以从植入的神经探针记录的棘波和局部场电位（LFP）中接收关于大脑活动的丰富信息。然而，目前还不清楚在这些设备上实现的尖峰神经网络（SNN）是否可以有效地处理这些信息。在这里，我们证明了SNN可以训练分类不同幅度的胡须偏转在一个单一的桶大鼠体感皮层诱发反应。我们表明，分类性能与最先进的机器学习方法相当，甚至上级。我们发现，SNN是相当不敏感的记录信号类型：多单元尖峰活动和LFPs产生类似的结果，其中LFPs从皮质层III和IV似乎更适合比那些深层。此外，不需要从数据中提取手工制作的特征-多单元活动可以直接馈送到这些网络中，并且LFP的简单事件编码足以获得良好的性能。此外，我们发现SNN的性能是不敏感的网络状态，他们的性能是类似的UP和DOWN状态。
Spike-based neuromorphic hardware has great potential for low-energy brain-machine interfaces, leading to a novel paradigm for neuroprosthetics where spiking neurons in silicon read out and control activity of brain circuits. Neuromorphic processors can receive rich information about brain activity from both spikes and local field potentials (LFPs) recorded by implanted neural probes. However, it was unclear whether spiking neural networks (SNNs) implemented on such devices can effectively process that information. Here, we demonstrate that SNNs can be trained to classify whisker deflections of different amplitudes from evoked responses in a single barrel of the rat somatosensory cortex. We show that the classification performance is comparable or even superior to state-of-the-art machine learning approaches. We find that SNNs are rather insensitive to recorded signal type: both multi-unit spiking activity and LFPs yield similar results, where LFPs from cortical layers III and IV seem better suited than those of deep layers. In addition, no hand-crafted features need to be extracted from the data—multi-unit activity can directly be fed into these networks and a simple event-encoding of LFPs is sufficient for good performance. Furthermore, we find that the performance of SNNs is insensitive to the network state—their performance is similar during UP and DOWN states.
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DOI：
10.1109/tnsre.2016.2612001
发表时间：
2017-10
期刊：
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
影响因子：
--
作者：
Jackson A;Hall TM
通讯作者：
Hall TM
细胞外田地和电流的起源-EEG，ECOG，LFP和尖峰。
DOI：
10.1038/nrn3241
发表时间：
2012-05-18
期刊：
Nature reviews. Neuroscience
影响因子：
--
作者：
Buzsáki G;Anastassiou CA;Koch C
通讯作者：
Koch C
影响因子：
5.8
作者：
Buccelli, Stefano;Bornat, Yannick;Chiappalone, Michela
通讯作者：
Chiappalone, Michela
影响因子：
2.7
作者：
LIU, DC;NOCEDAL, J
通讯作者：
NOCEDAL, J
影响因子：
5.4
作者：
Mahmud, Mufti;Cecchetto, Claudia;Vassanelli, Stefano
通讯作者：
Vassanelli, Stefano