Electrical Engineering seminars

The use of electronic circuits to model neural systems goes back to C. Mead and is present in models, from leaky-integrate-and-fire to Hodking-Huxley. Simulating neural network with analog hardware is attractive: it allows to implement neurocomputations in real time without discretization approximations, it has perfect simulation-time scaling with system size, and it provides ready-to-deploy neuromorphic circuit for applications. There are implementations in CMOS technology, however, they are complex, require sophisticated fabrication facilities and, most important, suffer from significant device mismatch. In a radically different approach, based on the concept of memristors, we introduce a neuro-synaptic circuit of unprecedented simplicity, with readily available cheap off-the-shelf electronic components, that can quantitatively reproduce textbook theoretical neuron and synaptic current models. Our neuron circuits can avoid the mismatch problem and are easily tuneable at bio-compatible time-scales. We first introduce a voltage-gated conductance bursting neuron model that produces spike traces that bare striking similarity to experimental recordings. We then introduce synaptic current circuits and show the modularity of our method implementing neurocomputing primitives of basic network motifs, including CPGs. With this "theoretical hardware" approach we show: (i) that neuron adaptation and self-excitation can be viewed as a self-consistent dynamical problem; (ii) that a dynamical memory can be minimally implemented with a single recursive spiking neuron; (iii) that an adaptive membrane current reveals a connection between bursting and the driven harmonic oscillator, perhaps pointing to a neural correlate of the pendular limb motion. Finally we discuss the limitation of the approach to networks of mid-size and its potential application for brain-machine-interfaces, robotics and AI. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2025-03-19. Recording duration: 00:43:46.

Wearable sensors that detect and quantify biomarkers in retrievable biofluids (e.g., interstitial fluid, sweat, tears) provide information on human dynamic physiological and psychological states. This information can transform health and wellness by providing actionable feedback. Due to outdated and insufficiently sensitive technologies, current on-body sensing systems have capabilities limited to pH, and a few high-concentration electrolytes, metabolites, and nutrients. As such, wearable sensing systems cannot detect key low-concentration biomarkers indicative of stress, inflammation, metabolic, and reproductive status. We are revolutionizing sensing. Our electronic biosensors detect virtually any signaling molecule or metabolite at ultra-low levels. We have monitored serotonin, dopamine, cortisol, phenylalanine, estradiol, progesterone, and glucose in blood, sweat, interstitial fluid, and tears. The sensors are based on modern nanoscale semiconductor transistors that are straightforwardly scalable for manufacturing. We are developing sensors for >40 biomarkers for personalized continuous monitoring (e.g., smartwatch, wearable patch) that will provide feedback for treating chronic health conditions (e.g., perimenopause, stress disorders, phenylketonuria). Moreover, our sensors will enable female fertility monitoring and the adoption of more healthy lifestyles to prevent disease and improve physical and cognitive performance.

We will highlight recent technological and methodological advances in deploying miniaturized technologies that can monitor the spatial electrophysiologic patterns of the visceral nervous system. As an example, we will discuss recent developments of thin, stretchable, wireless biosensor patches that can be embedded within routinely used medical adhesives for recording electrophysiologic patterns of the GI tract. We will also showcase recent developments in array signal processing that enable non-invasive tracking, and source localization, of the slow wave patterns associated with the GI tract. We will illustrate how such systems can also be used in tandem with novel miniaturized pacing devices to can enable closed-loop neuromodulation of the enteric nervous system. We will conclude with a summary of the knowns and unknowns in how multi-organ physiology research, technology miniaturization, and data science may create unique opportunities for the intersection of electrical engineering and neuroscience.

The story of event cameras starts from the very beginnings of neuromorphic engineering with Misha Mahowald and Carver Mead. The chip design of these “silicon retina” cameras is the most crucial aspect that might enable them to come to mass production and widespread use. Once we have a usable camera is just the beginning, because now we need to think of our use of the data as though we were some type of artificial “silicon cortex”. That step has just started but the last few years have brought some remarkable results from the computer vision community. This talk will have a lot of live demonstrations.