Muralidharan Abirami, Rousche Patrick J
Department of Bioengineering, University of Illinois at Chicago, Chicago, USA.
Neurol Res. 2005 Jan;27(1):4-10. doi: 10.1179/016164105X18089.
Each neuron has a specific set of stimuli, which it preferentially responds to (the receptive field of the neuron). For implantable cortical prosthetic devices specific points of the cortex (or groups of neurons) have to be stimulated to create perceptions of sensory stimulus with specific attributes (such as frequency, temporal characteristics, etc). Such applications would need real time decoding of signals. Previously mathematical techniques, such as computing the receptive field (using electrophysiology data) and artificial neural networks (Kohonen network or SOM and back propagation network) have been used to decode neural signals.
A Large Adaptive Memory Storage and Retrieval (LAMSTAR) neural-network-based decoder was designed to decode responses recorded from neurons in the auditory cortex. It was designed to identify the frequency of the tonal stimuli that elicited a particular discharge rate pattern recorded on two channels of a tungsten wire electrode array.
The network functioned efficiently as a decoder with 100% accuracy for the small sample of stimulus-response data used.
The results show that the network is effective in studying the functional organization of the auditory cortex and other sensory systems. Depending on the input sub-word, information about the kind of stimuli that activates particular parts of the sensory cortex can be studied.
每个神经元都有一组特定的刺激，它对这些刺激有优先反应（神经元的感受野）。对于可植入的皮质假体装置，必须刺激皮质的特定点（或神经元群），以产生具有特定属性（如频率、时间特征等）的感觉刺激感知。此类应用需要对信号进行实时解码。以前曾使用数学技术，如计算感受野（使用电生理数据）和人工神经网络（Kohonen网络或自组织映射以及反向传播网络）来解码神经信号。
设计了一种基于大型自适应记忆存储与检索（LAMSTAR）神经网络的解码器，用于解码从听觉皮质神经元记录的反应。它旨在识别引起在钨丝电极阵列的两个通道上记录的特定放电率模式的音调刺激的频率。
对于所使用的少量刺激-反应数据样本，该网络作为解码器高效运行，准确率达100%。
结果表明，该网络在研究听觉皮质和其他感觉系统的功能组织方面是有效的。根据输入子词，可以研究有关激活感觉皮质特定部分的刺激类型的信息。
