Gireeja Ranade

Cortical Representation of Ipsilateral Arm Movements in Monkey and Man

A fundamental organizational principle of the primate motor system is cortical control of contralateral limb movements. Motor areas also appear to play a role in the control of ipsilateral limb movements. Several studies in monkeys have shown that individual neurons in primary motor cortex (M1) may represent, on average, the direction of movements of the ipsilateral arm. Given the increasing body of evidence demonstrating that neural ensembles can reliably represent information with a high temporal resolution, here we characterize the distributed neural representation of ipsilateral upper limb kinematics in both monkey and man. In two macaque monkeys trained to perform center-out reaching movements, we found that the ensemble spiking activity in M1 could continuously represent ipsilateral limb position. Interestingly, this representation was more correlated with joint angles than hand position. Using bilateral electromyography recordings, we excluded the possibility that postural or mirror movements could exclusively account for these findings. In addition, linear methods could decode limb position from cortical field potentials in both monkeys. We also found that M1 spiking activity could control a biomimetic brain–machine interface reflecting ipsilateral kinematics. Finally, we recorded cortical field potentials from three human subjects and also consistently found evidence of a neural representation for ipsilateral movement parameters. Together, our results demonstrate the presence of a high-fidelity neural representation for ipsilateral movement and illustrates that it can be successfully incorporated into a brain–machine interface.

ProjecToR: Agile Reconfigurable Data Center Interconnect

We explore a novel, free-space optics based approach for building data center interconnects. It uses a digital micromirror device (DMD) and mirror assembly combination as a transmitter and a photodetector on top of the rack as a receiver (Figure 1). Our approach enables all pairs of racks to establish direct links, and we can reconfigure such links (i.e., connect different rack pairs) within 12 us. To carry traffic from a source to a destination rack, transmitters and receivers in our interconnect can be dynamically linked in millions of ways. We develop topology construction and routing methods to exploit this flexibility, including a flow scheduling algorithm that is a constant factor approximation to the offline optimal solution. Experiments with a small prototype point to the feasibility of our approach. Simulations using realistic data center workloads show that, compared to the conventional folded-Clos interconnect, our approach can improve mean flow completion time by 30-95% and reduce cost by 25-40%.

Model-based estimation of cardiac output and total peripheral resistance

We describe a novel model-based approach to estimate cardiac output (CO) and total peripheral resistance (TPR) continuously from peripheral arterial blood pressure (ABP) waveforms. Our method exploits the intra-beat and inter-beat variability in ABP to estimate the lumped time constant of a beat-to-beat averaged Windkessel model of the arterial tree, from which we obtain an uncalibrated estimate of CO. To estimate absolute CO, we determine the lumped arterial compliance using calibration data, and assuming either constant or state-dependent compliance. We applied our method to a porcine data set in which stroke volume was measured with an ultrasonic flowmeter. We obtain root-mean-square normalized errors of 11-13% across all pigs, lower than those obtained on the same data set using various other estimation methods. The CO estimates, and TPR estimates derived from them track intravenous drug infusions quite closely.

Cooperative communication for high-reliability low-latency wireless control

The Internet of Things envisions not only sensing but also actuation of numerous wirelessly connected devices. Seamless control with humans in the loop requires latencies on the order of a millisecond with very high reliabilities, paralleling the requirements for high-performance industrial control. Todays practical wireless systems cannot meet these reliability and latency requirements, forcing the use of wired systems. This paper introduces a wireless communication protocol, dubbed “Occupy CoW,” based on cooperative communication among nodes in the network to build the diversity necessary for the target reliability. Simultaneous retransmission by many relays achieves this without significantly decreasing throughput or increasing latency. The protocol is analyzed using the communication theoretic delay-limited-capacity framework and compared to baseline schemes that primarily exploit frequency diversity. In particular, we develop a novel “diversity meter” designed to measure “effective diversity” in the non-asymptotic regime. For a scenario inspired by an industrial printing application with 30 nodes in the control loop, total information throughput of 4.8 Mb/s, and cycle time under 2 ms, the protocol can robustly achieve a system probability of error better than 10-9 with nominal SNR below 5 dB.

Network coding for high-reliability low-latency wireless control

The Internet of Things (IoT) envisions simultaneous sensing and actuation of numerous wirelessly connected devices. Emerging human-in-the-loop applications demand low-latency high-reliability communication protocols, paralleling the requirements for high-performance industrial control. This paper introduces a wireless communication protocol based on network coding that in conjunction with cooperative communication techniques builds the necessary diversity to achieve the target reliability. The proposed protocol, XOR-CoW, is analyzed by using a communication theoretic delay-limited-capacity framework and compared to different realizations of previously proposed protocols without network coding. The results show that as the network size or payload increases, XOR-CoW gains advantage in minimum SNR to achieve the target latency. For a scenario inspired by an industrial printing application with 30 nodes in the control loop, total information throughput of 4.8 Mb/s, 20MHz of bandwidth and cycle time under 2 ms, the protocol can robustly achieve a system probability of error better than 10−9 with a nominal SNR less than 2 dB with Rayleigh fading.

Non-coherence in estimation and control

Control strategies for systems with information bottlenecks often follow an estimate-then-control paradigm. This paper presents a “non-coherent” system where this strategy cannot work and provides an alternative. The paper considers the estimation and control of a discrete-time linear system with continuous random observation gain, i.e. through a non-coherent channel. It is shown that such an unstable system is not mean-squared observable regardless of the density of the random observation gain: the mean-squared estimation error for any estimator must go to infinity. This is surprising in the context of threshold results for rate-limited estimation. In contrast to other results with rate-limited feedback, the paper shows that the system can be closed-loop mean-square stabilized in a certain parameter regime even though its open-loop counterpart is not mean-square observable. Finally, carry-free models (generalized deterministic models) provide an intuitive interpretation for the results.

Carry-free models and beyond

The generalized deterministic models recently proposed by Niesen and Maddah-Ali [1] successfully capture real-interference alignment as observed in Gaussian models. Simpler deterministic models, like ADT models [2], cannot demonstrate this phenomenon because they are limited in the set of channel gains they can model. This paper reinterprets the Niesen and Maddah-Ali models through the lens of carry-free operations. We further explore these carry-free models by considering i.i.d. unknown fading networks. In the unknown fading context, a carry-free model can be further simplified to a max-superposition model, where signals are superposed by a nonlinear max operation. Unlike in relay-networks with known fading and linear superposition, we find that decode-and-forward can perform arbitrarily better than compress-and-forward in max-superposition relay networks with unknown fading.

Comments on unknown channels

The idea of modeling an unknown channel using a broadcast channel was first introduced by Cover1 in 1972. This paper builds on his line of thought to consider priority encoding of communication over unknown channels without feedback, using fixed-length codes and from a single-shot, individual channel perspective. A ratio-regret metric is used to understand how well we can perform with respect to the actual channel realization.

LFP beta power predicts cursor stationarity in BMI task

Beta band oscillations in the local field potential (LFP) have been previously related to behavior in motor tasks. We investigated the correlations between beta oscillations and movement state for brain-machine interfaces. Two macaque monkeys were trained to perform a center-out motor task on a computer under normal movement control and under a ‘brain control’ paradigm, where neural firing directly controls the cursor. In both cases, LFP beta power decreased after movement onset. High beta power was predominantly observed with low movement velocity and vice versa. This observation was used to predict the stationarity of a computer cursor during brain control.

Side-information in control and estimation

As in portfolio theory, we can think of the value of side-information in a control system as the change in the “growth rate” due to side-information. A scalar counterexample (motivated by carry-free deterministic models) shows the value of side-information for control does not exactly parallel the value of side-information for portfolios. Mutual-information does not seem to be a bound here. The concept is further explored through a spinning vector control system that is re-oriented at each time so that the control or observation direction is partially unknown. The value of side-information can be calculated in this setup and it behaves quite differently in a control vs. estimation context. A second example considers the problem of vector control over a (scalar) erasure channel, the dual problem to the estimation problem of intermittent Kalman Filtering. The value of information here is measured through the change in the critical packet-drop probability for the system. While non-causal side-information regarding the packet arrivals does not affect the critical probability for the estimation problem, we find that it can generically be very valuable for the control problem - it seems to change the scaling behavior for the control counterpart to what would be considered the “high SNR limit” in communication problems.