Binary Message Passing Decoding of Product Codes Based on Generalized Minimum Distance Decoding: (Invited Paper)

We propose a binary message passing decoding algorithm for product codes based on generalized minimum distance decoding (GMDD) of the component codes, where the last stage of the GMDD makes a decision based on the Hamming distance metric. The proposed algorithm closes half of the gap between conventional iterative bounded distance decoding (iBDD) and turbo product decoding based on the Chase–Pyndiah algorithm at a bit error rate of $10^{-7}$, at the expense of some increase in complexity. The proposed algorithm entails only a limited increase in data flow compared to iBDD

Critical legal reading: The elements, strategies and dispositions needed to master this essential skill

Alex Steel, Kate Galloway, Mary Heath, Natalie Skead, Mark Israel, Anne Hewit

A theory of sensorimotor learning for brain-machine interface control

A remarkable demonstration of the flexibility of mammalian motor systems is primates’ ability to learn to control brain-machine interfaces (BMI’s). This constitutes a completely novel and artificial form of motor behavior, yet primates are capable of learning to control BMI’s under a wide range of conditions. BMI’s with carefully calibrated decoders, for example, can be learned with only minutes to hours of practice. With a few weeks of practice, even BMI’s with random decoders can be learned. What are the biological substrates of this learning process? This thesis proposes a simple theory of the computational principles underlying BMI learning. Through comprehensive numerical and formal analysis, we demonstrate that this theory can provide a unifying explanation for various disparate phenomena observed during BMI learning in three different BMI learning tasks. By explicitly modeling the underlying neural circuitry, the theory reveals an interpretation of these phenomena in terms of the biological non-linear dynamics of neural circuits

Learning and adaptation in brain machine interfaces

Balancing subject learning and decoder adaptation is central to increasing brain machine interface (BMI) performance. We addressed these complementary aspects in two studies: (1) a learning study, in which mice modulated “beta” band activity to control a 1D auditory cursor, and (2) an adaptive decoding study, in which a simple recurrent artificial neural network (RNN) decoded intended saccade targets of monkeys. In the learning study, three mice successfully increased beta band power following trial initiations, and specifically increased beta burst durations from 157 ms to 182 ms, likely contributing to performance. Though the task did not explicitly require specific movements, all three mice appeared to modulate beta activity via active motor control and had consistent vibrissal motor cortex multiunit activity and local field potential relationships with contralateral whisker pad electromyograms. The increased burst durations may therefore by a direct result of increased motor activity. These findings suggest that only a subset of beta rhythm phenomenology can be volitionally modulated (e.g. the tonic “hold” beta), therefore limiting the possible set of successful beta neuromodulation strategies. In the adaptive decoding study, RNNs decoded delay period activity in oculomotor and working memory regions while monkeys performed a delayed saccade task. Adaptive decoding sessions began with brain-controlled trials using pre-trained RNN models, in contrast to static decoding sessions in which 300-500 initial eye-controlled training trials were performed. Closed loop RNN decoding performance was lower than predicted by offline simulations. More consistent delay period activity and saccade paths across trials were associated with higher decoding performance. Despite the advantage of consistency, one monkey’s delay period activity patterns changed over the first week of adaptive decoding, and the other monkey’s saccades were more erratic during adaptive decoding than during static decoding sessions. It is possible that the altered session paradigm eliminating eye-controlled training trials led to either frustration or exploratory learning, causing the neural and behavioral changes. Considering neural control and decoder adaptation of BMIs in these studies, future work should improve the “two-learner” subject-decoder system by better modeling the interaction between underlying brain states (and possibly their modulation) and the neural signatures representing desired outcomes