George Jude Baby, Abraham Grace Mathew, Singh Katyayani, Ankolekar Shreya M, Amrutur Bharadwaj, Sikdar Sujit Kumar
Center for Nanoscience and Engineering, IISC Bangalore, India.
Molecular Biophysics Unit, IISC Bangalore, India.
Biosystems. 2014 Dec;126:1-11. doi: 10.1016/j.biosystems.2014.08.002. Epub 2014 Aug 7.
Liquid State Machines have been proposed as a framework to explore the computational properties of neuro-electronic hybrid systems (Maass et al., 2002). Here the neuronal culture implements a recurrent network and is followed by an array of linear discriminants implemented using perceptrons in electronics/software. Thus in this framework, it is desired that the outputs of the neuronal network, corresponding to different inputs, be linearly separable. Previous studies have demonstrated this by either using only a small set of input stimulus patterns to the culture (Hafizovic et al., 2007), large number of input electrodes (Dockendorf et al., 2009) or by using complex schemes to post-process the outputs of the neuronal culture prior to linear discriminance (Ortman et al., 2011). In this study we explore ways to temporally encode inputs into stimulus patterns using a small set of electrodes such that the neuronal culture's output can be directly decoded by simple linear discriminants based on perceptrons. We demonstrate that network can detect the timing and order of firing of inputs on multiple electrodes. Based on this, we demonstrate that the neuronal culture can be used as a kernel to transform inputs which are not linearly separable in a low dimensional space, into outputs in a high dimension where they are linearly separable. Thus simple linear discriminants can now be directly connected to outputs of the neuronal culture and allow for implementation of any function for such a hybrid system.
液态机器已被提出作为一种探索神经电子混合系统计算特性的框架（Maass等人，2002年）。在这里，神经元培养物实现了一个递归网络，随后是一系列使用电子学/软件中的感知器实现的线性判别器。因此，在这个框架中，期望神经网络对应于不同输入的输出是线性可分的。先前的研究通过以下方式证明了这一点：要么仅对培养物使用一小部分输入刺激模式（Hafizovic等人，2007年），要么使用大量输入电极（Dockendorf等人，2009年），要么在进行线性判别之前使用复杂的方案对神经元培养物的输出进行后处理（Ortman等人，2011年）。在本研究中，我们探索了使用一小部分电极将输入在时间上编码为刺激模式的方法，以便神经元培养物的输出可以通过基于感知器的简单线性判别器直接解码。我们证明网络可以检测多个电极上输入的放电时间和顺序。基于此，我们证明神经元培养物可以用作内核，将在低维空间中非线性可分的输入转换为在高维空间中线性可分的输出。因此，现在简单的线性判别器可以直接连接到神经元培养物的输出，并允许为这样的混合系统实现任何功能。
