Control Theory seminars

Seminar by Morten Kjaergaard (Niels Bohr Institute, University of Copenhagen) on rapid FPGA-based feedback for superconducting qubits, including sparse-sampling techniques and on-FPGA inference enabling millisecond-timescale T1 estimation, ~100 ms readout optimization, and over 74,000 consecutive recalibrations in closed-loop operation.

Navid Azizan presents methods that make neural models obey physical, safety and operational constraints at deployment. Hard-constrained neural networks, or HardNets, enforce input-dependent constraints by construction while preserving universal approximation within the feasible function class. Applications include models of chaotic dynamics with bounded trajectories, energy-constrained operator learning, safe reinforcement learning and control with formal guarantees. The talk then turns to enforcing constraints during sampling from pretrained diffusion and flow-matching models. Formulating generation as trajectory optimization allows receding-horizon control to guide outputs toward feasibility without retraining or imposing excessive restrictions on the sampling process. Examples from fluid dynamics, robot planning and control, PDE control and language-guided image editing illustrate how constraints can define admissible behavior while retaining expressive learning and generation.

On Thursday February 15th, we will host Victoria Peterson and Julian Neumann. Victoria will tell us about “Spatial filtering to enhance signal processing in invasive neurophysiology”. Besides his scientific presentation on “Closed-loop deep brain stimulation as a neuroprosthetic of dopaminergic circuits – Current evidence and future opportunities”, Julian will give us a glimpse at the person behind the science. The talks will be followed by a shared discussion. Note: The talks will exceptionally be held at 10 ET / 4PM CET. You can register via talks.stimulatingbrains.org to receive the (free) Zoom link!

We build upon and expand the efficient coding and predictive information models of neurons, presenting a novel perspective that neurons not only predict but also actively influence their future inputs through their outputs. We introduce the concept of neurons as feedback controllers of their environments, a role traditionally considered computationally demanding, particularly when the dynamical system characterizing the environment is unknown. By harnessing a novel data-driven control framework, we illustrate the feasibility of biological neurons functioning as effective feedback controllers. This innovative approach enables us to coherently explain various experimental findings that previously seemed unrelated. Our research has profound implications, potentially revolutionizing the modeling of neuronal circuits and paving the way for the creation of alternative, biologically inspired artificial neural networks.

On Friday, August 31st we will host Stephanie Cernera & Doris Wang! Stephanie Cernera, PhD, is a postdoctoral research fellow in the Starr lab at University of California San Francisco. She will tell us about “Personalized chronic adaptive deep brain stimulation outperforms conventional stimulation in Parkinson’s Disease”. Doris Wang, MD, PhD, is a neurosurgeon and assistant professor at the University of California San Francisco. Apart from her scientific presentation about “Adaptive Deep Brain Stimulation to Treat Gait Disorders in Parkinson’s Disease”, she will give us a glimpse at the “Person behind the science”. The talks will be followed by a shared discussion. You can register via talks.stimulatingbrains.org to receive the (free) Zoom link!

The traditional view of a neuron as a feature detector or an efficient encoder has difficulties in explaining the function of motor neurons and experimentally observed variable and context-dependent response properties of neurons. We put forward an alternative perspective, modeling each neuron as a feedback controller within a closed loop that includes other neurons and the external environment. Based on the recently developed Direct Data-Driven Control (DD-DC) approach, we propose a biologically plausible controller which implicitly identifies the dynamics of the rest of the loop, infers its latent state and optimizes control. The DD-DC model of a neuron accounts for multiple neurophysiological observations, including the switch from potentiation to depression in Spike-Timing-Dependent Plasticity (STDP) and its asymmetry; temporally extended feedforward and feedback neuronal filters and their adaptation to input statistics; imprecision of the neuronal spike-generation mechanism under constant input; as well as the prevalence of variability and/or noise in brain operation. The DD-DC neuron offers an alternative to the feedforward, instantaneously responding McCulloch-Pitts-Rosenblatt unit as a primitive for constructing biologically-inspired neural networks. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2023-06-28. Recording duration: 00:52:20.

The nervous system is fundamentally a closed loop control device: the output of actions continually influences the internal state and subsequent actions. This is true at the single cell and even the molecular level, where “actions” take the form of signals that are fed back to achieve a variety of functions, including homeostasis, excitability and various kinds of multistability that allow switching and storage of memory. It is also true at the behavioural level, where an animal’s motor actions directly influence sensory input on short timescales, and higher level information about goals and intended actions are continually updated on the basis of current and past actions. Studying the brain in a closed loop setting requires a multidisciplinary approach, leveraging engineering and theory as well as advances in measuring and manipulating the nervous system. I will describe our recent attempts to achieve this fusion of approaches at multiple levels in the nervous system, from synaptic signalling to closed loop brain machine interfaces.

Cortical variations in cytoarchitecture form a sensory-fugal axis that shapes regional profiles of extrinsic connectivity and is thought to guide signal propagation and integration across the cortical hierarchy. While neuroimaging work has shown that this axis constrains local properties of the human connectome, it remains unclear whether it also shapes the asymmetric signaling that arises from higher-order topology. Here, we used network control theory to examine the amount of energy required to propagate dynamics across the sensory-fugal axis. Our results revealed an asymmetry in this energy, indicating that bottom-up transitions were easier to complete compared to top-down. Supporting analyses demonstrated that asymmetries were underpinned by a connectome topology that is wired to support efficient bottom-up signaling. Lastly, we found that asymmetries correlated with differences in communicability and intrinsic neuronal time scales and lessened throughout youth. Our results show that cortical variation in cytoarchitecture may guide the formation of macroscopic connectome topology.

This session is a double feature of the Cologne Theoretical Neuroscience Forum and the Institute of Neuroscience and Medicine (INM-6) Computational and Systems Neuroscience of the Jülich Research Center.

Inhibition of seizures can come from expected – and surprising – sources. In this talk I will explore circuit elements, both within and external to the temporal lobe, which may be able inhibit hippocampal seizures, and how specific aspects of intervention strategies can be critical for outcomes. We’ll discuss novel sources of inhibition within the hippocampus, the cerebellum as a potential target, and closed-loop optimization of stimulation parameters

The basal ganglia are a collection of brain areas that are connected by a variety of synaptic pathways and are a site of significant reward-related dopamine release. These properties suggest a possible role for the basal ganglia in action selection, guided by reinforcement learning. In this talk, I will discuss a framework for how this function might be performed and computational results using an upward mapping to identify putative low-dimensional control ensembles that may be involved in tuning decision policy. I will also present some recent experimental results and theory – related to effects of extracellular ion dynamics -- that run counter to the classical view of basal ganglia pathways and suggest a new interpretation of certain aspects of this framework. For those not so interested in the basal ganglia, I hope that the upward mapping approach and impact of extracellular ion dynamics will nonetheless be of interest!

Ali Weber (University of Washington, USA) uses the the hawkmoth as a model system, to investigate how information from a small number of mechanoreceptors on the wings are used in flight control. She employs a combination of experimental and computational techniques to study how these sensors respond during flight and how one might optimally array a set of these sensors to best provide feedback during flight.

Neural circuits exhibit complex activity patterns, both spontaneously and in response to external stimuli. Information encoding and learning in neural circuits depend on the ability of time-varying stimuli to control spontaneous network activity. In particular, variability arising from the sensitivity to initial conditions of recurrent cortical circuits can limit the information conveyed about the sensory input. Spiking and firing rate network models can exhibit such sensitivity to initial conditions that are reflected in their dynamic entropy rate and attractor dimensionality computed from their full Lyapunov spectrum. I will show how chaos in both spiking and rate networks depends on biophysical properties of neurons and the statistics of time-varying stimuli. In spiking networks, increasing the input rate or coupling strength aids in controlling the driven target circuit, which is reflected in both a reduced trial-to-trial variability and a decreased dynamic entropy rate. With sufficiently strong input, a transition towards complete network state control occurs. Surprisingly, this transition does not coincide with the transition from chaos to stability but occurs at even larger values of external input strength. Controllability of spiking activity is facilitated when neurons in the target circuit have a sharp spike onset, thus a high speed by which neurons launch into the action potential. I will also discuss chaos and controllability in firing-rate networks in the balanced state. For these, external control of recurrent dynamics strongly depends on correlations in the input. This phenomenon was studied with a non-stationary dynamic mean-field theory that determines how the activity statistics and the largest Lyapunov exponent depend on frequency and amplitude of the input, recurrent coupling strength, and network size. This shows that uncorrelated inputs facilitate learning in balanced networks. The results highlight the potential of Lyapunov spectrum analysis as a diagnostic for machine learning applications of recurrent networks. They are also relevant in light of recent advances in optogenetics that allow for time-dependent stimulation of a select population of neurons.

The richness of active matter's spatiotemporal patterns continues to capture our imagination. Shaping these emergent dynamics into pre-determined forms of our choosing is a grand challenge in the field. To complicate matters, multiple dynamical attractors can coexist in such systems, leading to initial condition-dependent dynamics. Consequently, non-trivial spatiotemporal inputs are generally needed to access these states. Optimal control theory provides a general framework for identifying such inputs and represents a promising computational tool for guiding experiments and interacting with various systems in soft active matter and biology. As an exemplar, I first consider an extensile active nematic fluid confined to a disk. In the absence of control, the system produces two topological defects that perpetually circulate. Optimal control identifies a time-varying active stress field that restructures the director field, flipping the system to its other attractor that rotates in the opposite direction. As a second, analogous case, I examine a small network of coupled Belousov-Zhabotinsky chemical oscillators that possesses two dominant attractors, two wave states of opposing chirality. Optimal control similarly achieves the task of attractor switching. I conclude with a few forward-looking remarks on how the same model-based control approach might come to bear on problems in biology.

Over the last few decades, the use of deep brain stimulation (DBS) to improve the treatment of those with neurological movement disorders represents a critical success story in the development of invasive neurotechnology and the promise of brain-computer interfaces (BCI) to improve the lives of those suffering from incurable neurological disorders. In the last decade, investigational devices capable of recording and streaming neural activity from chronically implanted therapeutic electrodes has supercharged research into clinical applications of BCI, enabling in-human studies investigating the use of adaptive stimulation algorithms to further enhance therapeutic outcomes and improve future device performance. In this talk, Dr. Herron will review ongoing clinical research efforts in the field of adaptive DBS systems and algorithms. This will include an overview of DBS in current clinical practice, the development of bidirectional clinical-use research platforms, ongoing algorithm evaluation efforts, a discussion of current adoption barriers to be addressed in future work.