Optogenetic feedback control of neural activity

Optogenetic techniques enable precise excitation and inhibition of firing in specified neuronal populations and artifact-free recording of firing activity. Several studies have suggested that optical stimulation provides the precision and dynamic range requisite for closed-loop neuronal control, but no approach yet permits feedback control of neuronal firing. Here we present the ‘optoclamp’, a feedback control technology that provides continuous, real-time adjustments of bidirectional optical stimulation in order to lock spiking activity at specified targets over timescales ranging from seconds to days. We demonstrate how this system can be used to decouple neuronal firing levels from ongoing changes in network excitability due to multi-hour periods of glutamatergic or GABAergic neurotransmission blockade in vitro as well as impinging vibrissal sensory drive in vivo. This technology enables continuous, precise optical control of firing in neuronal populations in order to disentangle causally related variables of circuit activation in a physiologically and ethologically relevant manner.National Science Foundation (U.S.). Graduate Research FellowshipNational Science Foundation (U.S.). Integrative Graduate Education and Research Traineeshi

Inference Algorithms and Sensorimotor Representations in Brains and Machines

Animals function in a 3D world in which survival depends on robust, well-controlled actions. Historically, researchers in Artificial Intelligence (AI) and neuroscience have explored sensory and motor systems independently. There is a growing body of literature in AI and neuroscience to suggest that they actually work in tandem. While there has been a great deal of work on vision and audition as sensory modalities in these fields, one could argue that a more fundamental modality in biology is haptics, or the sense of touch. In this thesis, we will look at building computational models that integrate tactile sensing with other sensory modalities to perform manipulation-like tasks in robots and discrimination tasks in mice. We will also explore the problem of inference through the lens of Markov Chain Monte Carlo methods (MCMC). We elaborate on the ideas discussed in this thesis in the introduction presented in Chapter 1. A challenging problem one often faces when applying probabilistic mathematical models to the study of sensory-motor systems and other problems involving learning of inference is sampling. Hamiltonian Markov Chain Monte Carlo (HMC) algorithms can efficiently draw representative samples from complex probabilistic models. Most MCMC methods rely on detailed balance to ensure that we can sample from the correct distribution. This constraint can be relaxed in discrete state spaces such as those employed by HMC type methods. In Chapter 2, we study HMC methods without detailed balance to explore faster convergence. Markov jump processes are stochastic processes on discrete state space but continuous in time. In Chapter 3, we use Markov Jump Processes to simulate waiting times along with generalized detailed balance. This waiting time ,we show, helps generate samples faster. Most MCMC methods are plagued by slow simulation times on discrete computing systems. In Chapter 4, we explore HMC in analog circuits where the problem of generating samples from a distribution is mapped to the problem of sampling charge in a capacitor.The second half of this dissertation focuses on the role of haptics in perception and action. Manipulation is a fundamental problem for artificial and biological agents. High dimensional actuators (say, fingers, trunks,etc) are really hard to control. In Chapter 5, we present an approach to learn to actuate dexterous manipulators to grasp objects in simulation. Haptics as a sensory modality is critical to many manipulation tasks. Employing haptics in high dimensional dextrous actuators is challenging. In Chapter 6, we explore how intrinsic curiosity and haptics can be used to learn exploration strategies for discrimination of objects with dextrous hands. A key component to make tactile sensing a possibility is the availability of cheap, efficient, scalable hardware. Chapter 7 presents results for tactile servoing using a physical gelsight sensor. Traditional neuroscience texts delineate sensory and motor systems as two independent systems yet recent results suggest that this may not be entirely complete. That is, there is evidence to suggest that the representations in the cortex is more distributed than is accepted. Finally in Chapter 8, we explore building a computational model of spiking neural data collected from both the barrel and motor cortices during free and active whisking. These works help towards understanding sensorimotor representations in the context of haptics and high dimensional controls. We conclude with a discussion on future directions in Chapter 9

Kinetic energy fluctuation-driven locomotor transitions on potential energy landscapes of beam obstacle traversal and ground self-righting

Animals’ physical interaction with their environment, although often difficult, is effective and enables them to move robustly by using and transitioning between different modes such as running and climbing. Although robots exhibit some of these transitions, we lack a principled approach to generating and controlling them using effective physical interaction. Bridging this knowledge gap, in addition to advancing our understanding of animal locomotion, will improve robotic mobility. Recent studies of physical interaction with environment discovered that during beam obstacle traversal and ground self-righting, discoid cockroaches use and transition between diverse locomotor modes probabilistically and via multiple pathways. To traverse beams, the animal first pushes against them, but eventually pitches up due to beam restoring forces, following which it either pushes across beams (pitch mode) or rolls into the gap (roll mode). To self-right, the animal opens and pushes its wings against the ground, which pitches its body forward (metastable mode), and then rolls sideways (roll mode). Here, we seek to begin to explain these observations by integrating biological, robotic, and physics studies. We focus on pitch-to-roll and metastable-to-roll transitions of cockroaches during escape and emergency responses and feedforward-controlled robots. We discovered that across both systems, physical interaction is stochastic, with animals showing more variability. Animal and robot system states are strongly attracted to basins of their potential energy landscape, resulting in stereotyped locomotor modes. Locomotor transitions are probabilistic barrier-crossing transitions between landscape basins. Whereas the animal and robot traversed stiff beams via roll mode, they pushed across flimsy beams, suggesting that modes with easier physical interaction are more probable to occur (more favorable). Varying potential energy barriers by changing beam torsional stiffness (in the animal and robot) and kinetic energy fluctuation by changing body oscillation (in the robot) in both beam traversal and self-righting revealed that kinetic energy fluctuation comparable to the barrier facilitates probabilistic transition to the more favorable mode. Changing the system configuration (self-righting robot's wing opening) facilitates transitions by lowering the barrier. The animal's pitch-to-roll transition during beam traversal occurred even with insufficient kinetic energy fluctuation, suggesting that sensory feedback may be involved. These discoveries support the use of potential energy landscapes as a framework to understand locomotor transitions. Finally, we implemented methods for tracking and 3-D reconstruction of small animal locomotion in an existing terrain treadmill