The ability to behave differently according to the situation is essential for survival in a dynamic environment. This requires past experiences to be encoded and retrieved alongside the contextual schemas in which they occurred. The complementary learning systems theory suggests that these schemas are acquired through gradual learning via the neocortex and rapid learning via the hippocampus. However, it has also been shown that new information matching a preexisting schema can bypass the gradual learning process and be acquired rapidly, suggesting that the separation of memories into schemas is useful for flexible learning. While there are theories of the role of schemas in memory consolidation, we lack a full understanding of the mechanisms underlying this function. For this reason, we created a biologically plausible neural network model of schema consolidation that studies several brain areas and their interactions. We believe that this model will have an important impact on the study of memory consolidation, providing fresh and testable hypotheses that will further motivate experiments in the interaction between neuromodulation, schemas, and indexing. Moreover, the topic is far-reaching, as a systems level understanding of how the brain separates the learning of tasks is valuable for machine learning researchers solving the challenges of transfer learning and reducing catastrophic forgetting in artificial neural networks.

A manuscript of this work is on BioRxiv at https://www.biorxiv.org/content/early/2018/10/04/434696

Recent developments in neuromorphic engineering have enabled low-powered processing and sensing in robotics, leading to more efficient brain-like computation for many robotic tasks such as motion planning and navigation. However, present experiments in neuromorphic robotic systems have mostly been performed under controlled indoor settings, often with unlimited power supply. While this may be suitable for many applications, these algorithms often fail in outdoor dynamic environments that could benefit the most from the low size, weight, and power of neuromorphic devices. I am interested in the current challenges of outdoor robotics, how current neuromorphic solutions can address these problems, our current approaches to the task, and what further needs to be achieved to create a complete neuromorphic solution to outdoor navigation and path planning.

T. Hwu, A. Y. Wang, N. Oros, and J. L. Krichmar. (2018). Adaptive robot path planning using a spiking neuron algorithm with axonal delays. IEEE Transactions on Cognitive and Developmental Systems. [pdf]

T. Hwu, J. Isbell, N. Oros, and J. L. Krichmar. (2017). A self-driving robot using deep convolutionalneural networks on neuromorphic hardware. IEEE International Joint Conference on Neural Networks (IJCNN), Anchorage, AK. [pdf]

T. Hwu, J. L. Krichmar, and X. Zou. (2017). A complete neuromorphic solution to outdoor navigation and path planning. Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS), Baltimore, Maryland. [pdf]

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. 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.

M. Frank, T. Hwu, S. Jain, R. Knight, I. Martinovic, P. Mittal, D. Perito, I. Sluganovic, D. Song. (2017). Using EEG-Based BCI Devices to Subliminally Probe for Private Information. Proceedings of the 2017 on Workshop on Privacy in the Electronic Society (pp. 133-136). ACM. [pdf]

Recommender systems are integrated into many internet-based services, including movie recommendations, shopping suggestions, and automatic playlist generation. Although there are a number of effective recommender models inspired by traditional machine learning methods, the use of psychological models in recommendation is far less explored. We propose that the process of finding similar music tracks parallels the cognitive task of generalization, which could potentially be used to aid in music playlist recommendation. In generalization, stimuli are defined within a psychological space, in which previously experienced stimuli are used to create generalizations about newly presented stimuli. Similarly, a person who is trying to construct a playlist will use their prior musical knowledge or intuitions to find songs of similar taste. The main objective of this work is to evaluate the effectiveness of applying psychological models to large scale online datasets which contain the listening histories of users. The models are tested both qualitatively and quantitively by holding out portions of the dataset and evaluating how well they can predict the missing information. Using common metrics from information retrieval, we explore the advantages and differences of using psychological models over traditional machine learning models in recommender systems. Additionally, we provide an example of how large existing databases of human behavior can be used to conduct psychology experiments in a robust and affordable manner.

T. Hwu. Exploring the Uses of Psychological Models of Generalization in Music Recommendation Systems. Undergraduate Thesis. University of California, Berkeley, 2014. [pdf]