No abstract

Abstract-Neuromorphic computing is a promising solution for reducing the size, weight and power of mobile embedded systems. In this paper, we introduce a realization of such a system by creating the first closed-loop battery-powered communication system between an IBM TrueNorth NS1e and an autonomous Android-Based Robotics platform. Using this system, we constructed a dataset of path following behavior by manually driving the Android-Based robot along steep mountain trails and recording video frames from the camera mounted on the robot along with the corresponding motor commands. We used this dataset to train a deep convolutional neural network implemented on the TrueNorth NS1e. The NS1e, which was mounted on the robot and powered by the robot's battery, resulted in a selfdriving robot that could successfully traverse a steep mountain path in real time. To our knowledge, this represents the first time the TrueNorth NS1e neuromorphic chip has been embedded on a mobile platform under closed-loop control.

Understanding why deep neural networks and machine learning algorithms act as they do is a difficult endeavor. Neuroscientists are faced with similar problems. One way biologists address this issue is by closely observing behavior while recording neurons or manipulating brain circuits. This has been called neuroethology. In a similar way, neurorobotics can be used to explain how neural network activity leads to behavior. In real world settings, neurorobots have been shown to perform behaviors analogous to animals. Moreover, a neuroroboticist has total control over the network, and by analyzing different neural groups or studying the effect of network perturbations (e.g., simulated lesions), they may be able to explain how the robot's behavior arises from artificial brain activity. In this paper, we review neurorobot experiments by focusing on how the robot's behavior leads to a qualitative and quantitative explanation of neural activity, and vice versa, that is, how neural activity leads to behavior. We suggest that using neurorobots as a form of computational neuroethology can be a powerful methodology for understanding neuroscience, as well as for artificial intelligence and machine learning.

EEG-based Brain-Computer-Interfaces are becoming available as consumer-grade devices, used in applications from gaming to learning programs with neuro-feedback loops. While enabling attractive applications, their proliferation introduces novel privacy concerns and security threats. One such example are attacks in which adversaries compromise EEG-based BCI devices, and are able to analyze the users brain activity to infer private information about a user, such as their bank or area-of-living. However, a key limitation of the above attacks is that they require user cooperation, and are thus easily detectable and rendered inefcient after discovery.In this paper, we propose and analyze a more serious threat -a subliminal attack in which, given that the visual probing lasts for less than 13.3 milliseconds, the existence of any stimulus is below ones cognitive perception. We show that, even under such strong limitations, the attackers can still analyze subliminal brain activity in response to the rapid visual stimuli and consequently infer private information about the user. By running a proof-of-concept study with 27 participants, we experimentally evaluate the feasibility of subliminal attacks using EEG-based BCI devices. While not perfect, our results show that it is indeed feasible for attackers to subliminally learn probabilistic information about their victims. !

No abstract