Abstract
In this article, we introduce the Bioinspired Neuroprosthetic Design Environment (BNDE) as a practical platform for the development of novel brain–machine interface (BMI) controllers, which are based on spiking model neurons. We built the BNDE around a hard real-time system so that it is capable of creating simulated synapses from extracellularly recorded neurons to model neurons. In order to evaluate the practicality of the BNDE for neuroprosthetic control experiments, a novel, adaptive BMI controller was developed and tested using real-time closed-loop simulations. The present controller consists of two in silico medium spiny neurons, which receive simulated synaptic inputs from recorded motor cortical neurons. In the closed-loop simulations, the recordings from the cortical neurons were imitated using an external, hardware-based neural signal synthesizer. By implementing a reward-modulated spike timing-dependent plasticity rule, the controller achieved perfect target reach accuracy for a two-target reaching task in one-dimensional space. The BNDE combines the flexibility of software-based spiking neural network (SNN) simulations with powerful online data visualization tools and is a low-cost, PC-based, and all-in-one solution for developing neurally inspired BMI controllers. We believe that the BNDE is the first implementation, which is capable of creating hybrid biological/in silico neural networks for motor neuroprosthetic control and utilizes multiple CPU cores for computationally intensive real-time SNN simulations.
Highlights
Advances in brain–machine interface (BMI) technologies have allowed rodents (Chapin et al, 1999; DiGiovanna et al, 2009; Manohar et al, 2012), monkeys (Taylor et al, 2002; Carmena et al, 2003; Velliste et al, 2008; Dethier et al, 2011; Pohlmeyer et al, 2014), and humans (Hochberg et al, 2006, 2012; Collinger et al, 2013; Wodlinger et al, 2015) to control different prosthetic devices directly with their neuronal activity
The bioinspired BMI (B-BMI) Learning Performance Prior to starting the experiment to test the learning performance of the B-BMI, we determined the total weight of the excitatory synapses to the medium spiny neurons (MSNs), W in Eq 11, and the weight of the inhibitory synapses between the MSNs to provide a winner-take-all functionality in the network
We present the Bioinspired Neuroprosthetic Design Environment (BNDE) as a practical platform for creating hybrid biological/in silico neural networks and developing neurally inspired neuroprosthetic systems
Summary
Advances in brain–machine interface (BMI) technologies have allowed rodents (Chapin et al, 1999; DiGiovanna et al, 2009; Manohar et al, 2012), monkeys (Taylor et al, 2002; Carmena et al, 2003; Velliste et al, 2008; Dethier et al, 2011; Pohlmeyer et al, 2014), and humans (Hochberg et al, 2006, 2012; Collinger et al, 2013; Wodlinger et al, 2015) to control different prosthetic devices directly with their neuronal activity. In conventional BMI design approach, the main motivation has generally been to find an input–output mathematical model, which optimally transforms firing rates of cortical neurons into control signals for manipulation of a prosthetic actuator. In these systems, spike binning is performed in order to provide firing-rate inputs to the model used and this process leads to loss in the information encoded by the timing of neural spikes (Riehle, 1997; Hatsopoulos et al, 1998; Grammont and Riehle, 2003; Engelhard et al, 2013). Its outputs would be used in manipulating a neuroprosthesis (Figure 1) This novel, SNN-based design approach has the potential to bring several advantages in neuroprosthetic system control, adaptation, and implementation. The interactions of real neurons with model neurons could be investigated during neuroprosthetic control experiments and these investigations can provide new insights into the information processing principles in the motor cortex during neuroprosthetic control and learning
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