Control Theory ePosters

The neural control of movement is a fundamental aspect of understanding how the nervous system coordinate and regulate motor behavior. Procedural safety is a critical concern for movement control, especially for online tasks in high-dimensional control spaces. Current safe exploration algorithms exhibit inefficiency…

Adaptation is essential for biological motor control, enabling rapid modifications of control strategies as the environment changes. However, we still lack a unified network-level theory of coordinated motor adaptation. In previous work, we have proposed a multi-area RNN capable of rapid motor adaptation. The network…

Nervous system enables flexible and adaptive control of behavior, allowing us to modify actions according to task demands. The associated neural population activity is often characterized by low-dimensional manifolds, orthogonal null and potent spaces, and rotations in neural trajectories. These features are…

Cortical circuits are widely thought to operate in the so-called ``inhibition-stabilized network (ISN) regime’‘, whereby the excitatory (E) subcircuit is unstable in isolation but dynamically stabilized by feedback inhibition (I). So far, this regime has been studied both theoretically and experimentally in its…

Throughout evolution the brain has optimized the control of the body across species, and in order to understand the relationship between brain and body it is imperative that we model the sensorimotor transformations underlying embodied control. As part of a larger coordinated effort, we are in the process of…

We propose a novel dual-model framework that integrates reinforcement learning (RL) and adaptive control to model cerebellar function in motor learning and adaptation. Our approach distinguishes between the roles of the basal ganglia and cerebellum, addressing limitations of current models in complex motor control…

Numerous biologically inspired models for neurons have been developed over the years, from the Hodgkin-Huxley equations to the McCulloch-Pitts model, spanning varying degrees of computational complexity. In particular, Rosenblatt's perceptron model has endured as a standard neuron model used in machine learning. In…

Animals learn complex cognitive maps to navigate through physical environments or reason about abstract concepts. These maps are believed to be encoded by mammalian hippocampal grid and place cells, forming low-dimensional neural manifolds that support flexible navigation. However, beyond simple line and ring…

Animals readily make value-based decisions between discrete and static items, with decades of work elucidating neural circuits involved in such choices (Rangel et al., 2015). Much less is known about naturalistic decision-making contexts such as goal pursuit (e.g., hunting). Dynamic choices require determining the…

The sensory experience of an organism is shaped by movement, either of the entire body or of individual sensors. Many organisms can exploit sensor motion in ways that enrich the quality of sensory cues and enhance their ability to estimate unknown quantities. For instance, mantids perform peering head movements before…

The ease with which two-legged dogs can run illustrates the versatility and agility of mammals in learning arbitrary motor skills, an ability currently unrivaled by artificial systems. How the brain supports these capacities is unclear, as control mechanisms are distributed and interdependent between many brain…

Complex brain functions are realized through the coordinated activity of populations of neurons. To manipulate brain function, it is crucial for brain stimulation techniques to achieve targeted control at the level of neural populations. Electrical microstimulation (uStim) has the potential to create targeted activity…

Animals constantly decide how to allocate mental resources to thrive in their environments. Managing learning is a key challenge, and recent experiments show that animals often incur attentional costs for the potential of greater future rewards, or disengage when a task appears unlearnable, exhibiting nuanced…

A prevailing theory in goal-directed decision-making is that the brain accumulates evidence over time to make perceptual decisions. However, even after two decades of studies on evidence accumulation, it is still unclear how neuronal populations cooperate to dynamically represent decision variables. This is because of…

Energy efficiency and robustness to noise for long-distance communication are often cited as reasons to the question of why the brain uses digital communication in the form of spiking neurons [1,2]. However, a quantitative and systematic analysis of the advantages and disadvantages of spikes in terms of communication,…

Across species, neurons possess numerous ion channel types, with a single cell typically expressing tens of channel genes, which translate to hundreds or even thousands of channel protein types, each with different kinetics [1]. Moreover, maximal conductance densities across individuals are also highly variable and…

We adapt nonlinear optimal control theory (OCT) to control oscillations and network synchrony and apply it to models of neural population dynamics. Conventionally, OCT requires a target trajectory. This requirement may be overly restrictive for oscillatory targets since the exact trajectory shape including amplitude…

Contrastive Hebbian Learning (CHL) has recently emerged as a new biologically plausible paradigm to explain the mechanisms of plasticity in the brain. In classical CHL, developed for artificial networks, neurons learn in a two-step procedure where their free response to a given input is contrasted to their clamped…

In autonomous navigation, an agent's knowledge of its environment is represented as sensory inputs (states) linked together by actions. A popular approach for acquiring this knowledge is reinforcement learning (RL), where learning is stereotyped, i.e. an agent learns to react to a state with the action associated with…

Learning in neuronal circuits requires coordinated modifications of synaptic strengths across hierarchical networks to refine computation and improve behavioral outcomes. This coordination requires solving the credit assignment problem. Yet, how neuronal circuits achieve credit assignment remains a central unsolved…

Synaptic changes driven by Hebbian plasticity are unstable and require compensatory mechanisms to homeostatically control neuronal activity. Synaptic scaling regulates the synaptic efficacies to bring individual neurons to a set firing regime [1]. However, the timescales of synaptic scaling are too slow to…

For motor control, the activities of local neural nets need to be coordinated, which can be attained via direct synaptic connections. Phylogenetically, the main purpose of the brain is the active interaction with the environment which suggests the presence of an alternative neural coordination mechanism, namely one…

The success of Artificial Neural Networks (ANNs) trained with error-backpropagation algorithms relies on the combination of offline training, vast amounts of data, and energy-intensive simulations on dedicated hardware accelerators. In contrast, biological neurons can learn quickly in a self-supervised manner by…

In the brain, the collective activity of neurons form trajectories on low dimensional neural manifolds. The neural computation underlying flexible cognition and behavior relies on dynamic control of these structures. For example, different tasks or behaviors are represented on different subspaces, requiring fast…