CNS*2013 Workshop on
Methods of System Identification for Studying Information Processing in Sensory Systems
Wednesday, July 17, 2013
A functional characterization of an unknown system typically begins by making observations about the response of that system to input signals. The knowledge obtained from such observations can then be used to derive a quantitative model of the system in a process called system identification. The goal of system identification is to use a given input/output data set to derive a function that maps an arbitrary system input into an appropriate output.
In neurobiology, system identification has been applied to a variety of sensory systems, ranging from insects to vertebrates. Depending on the level of abstraction, the identified neural models vary from detailed mechanistic models to purely phenomenological models.
The workshop will provide a state of the art forum for discussing methods of system identification applied to the visual, auditory, olfactory and somatosensory systems in insects and vertebrates.
The lack of a deeper understanding of how sensory systems encode stimulus information has hindered the progress in understanding sensory signal processing in higher brain centers. Evaluations of various systems identification methods and a comparative analysis across insects and vertebrates may reveal common neural encoding principles and future research directions.
The workshop is targeted towards systems, computational and theoretical neuroscientists with interest in the representation and processing of stimuli in sensory systems in insects and vertebrates.
- Vasilis Z. Marmarelis (2004). Nonlinear Dynamic Modeling of Physiological Systems. Wiley-IEEE Press, Hoboken, NJ, 2004.
- Wu, M., David, S., & Gallant, J. (2006). Complete Functional Characterization of Sensory Neurons by System Identification. Annual Review of Neuroscience, 29, 477–505.
- Ljung, L. (2010). Perspectives on System Identification , Annual Reviews in Control, 34 (2010), 1-12.
- Aurel A. Lazar, Department of Electrical Engineering, Columbia University.
- Mikko I. Juusola , Department of Biomedical Science, University of Sheffield.
Neural Circuits for Fly Visual Course Control.
Alex Borst , Max Planck Institute for Neurobiology, Martinsried.
Visual navigation has been studied extensively in flies, both in tethered as well as in freely flying animals. As neural control elements, the tangential cells of the lobula plate seem to play a key role: they are sensitive to visual motion, have large receptive fields, and, with their spatial distribution of preferred directions, match the optic flow as elicited during certain types of flight maneuvers. However, several key questions have remained unanswered for long: 1. What is the neural circuit presynaptic to the tangential cells responsible for extracting the local direction of motion? 2. Do the lobula plate tangential cells indeed control turning responses of the fly? 3. Is there a separate visual course control system allowing the fly to detect and track individual objects?
By combining whole-cell patch recording and behavioral studies with silencing , optogenetic stimulation and optical recording from genetically targeted candidate neurons in Drosophila, the following progress has been made towards answering these questions: 1. Using apparent motion stimuli on flies with lamina neurons L1 or L2 silenced, we find that L1 and L2 feed into separate motion pathways specialized for the detection of ON (L1) and OFF (L2) signals. Optical recording from T4 and T5 cells reveals that these cells carry the directionally selective output of ON (T4) and OFF (T5) motion detectors. 2. Optogenetic stimulation of lobula plate HS-cells via a switchable variant of channelrhodopsin2 elicits head and body turns of Drosophila in tethered flight, thus demonstrating that HS-cells indeed are involved in flight control. 3. Behavioral studies of flies with T4/T5 cells blocked show that these flies, although being completely blind to motion of gratings and small objects, are still able to fixate objects under closed-loop conditions. Fixation behavior is based on a system, implemented in parallel to the motion pathway, which uses local temporal brightness changes to detect the position of an object.
Sonja Gruen, Forschungszentrum Juelich.
Vivek Jayaraman, Janelia Farm, Ashburn, VA.
Mikko I. Juusola, Department of Biomedical Science, University of Sheffield.
Holger G. Krapp, Department of Bioengineering, Imperial College.
Aurel A. Lazar, Department of Electrical Engineering, Columbia University.
Sensorimotor Circuit Dysfunction as the Origin of a Motor Neuron Disease
Brian D. McCabe, Department of Pathology and Cell Biology, Columbia University.
Spinal Muscular Atrophy (SMA) is a neurodegenerative motor system disease caused by mutations in Survival Motor Neuron (SMN). It is the most common inherited cause of childhood mortality and currently there is no treatment. To study SMA we employed Drosophila SMN mutants which have defective muscle growth, locomotion and motor neuron function similar to human disease phenotypes. Surprisingly, we found that none of these defects were corrected by transgenic restoration of SMN in either muscles or motor neurons. Instead, we discovered that SMN must be restored in both proprioceptive sensory and central interneurons in the motor circuit in order to rescue disease. We established that SMN-deficient motor circuit neurons had defective neurotransmission and that this was sufficient to non-autonomously perturb motor neuron function. Furthermore, we found that genetic inhibition of K+ channels or administration of an FDA approved small molecule K+ channel inhibitor, 4AP, restored motor circuit activity and provided benefit in the Drosophila SMA model. Based on this data, 4AP is now in human clinical trials for SMA.
To establish the molecular mechanisms that lead to selective motor circuit dysfunction in SMA, we investigated SMN-dependent gene expression. From a genome-wide functional analysis of Drosophila genes regulated by SMN, we identified a novel evolutionarily conserved protein, Stasimon, required for sensory-motor circuit function in both Drosophila and vertebrates. Our results illuminated a cohesive chain of molecular events linking SMN-depletion to motor circuit dysfunction, establishing a mechanistic framework to understand the selective vulnerability of neurons in SMA and demonstrating that neurodegenerative disease can be induced by the dysfunction of neuronal circuits.
Dynamics of Thalamocortical Circuits Underlying Sensorimotor Integration
Karim G. Oweiss, Department of Electrical and Computer Engineering and Department of Neuroscience, Michigan State University.
The highly sophisticated interaction between the somatosensory and motor systems underlies our ability to seamlessly control our movements. Loss of this interaction results in a devastating quality of life. We have proposed to restore somatosensory feedback in motor impaired subjects by stimulating the somatosensory pathway to deliver information about limb state. The thalamus plays an indispensible role in relaying information from the periphery to the cerebral cortex through multiple transformations of afferent inputs. To deliver somatosensory feedback that can be naturally perceived by the subject, it is necessary to first use system identification techniques to quantify the extent to which artificial stimulation can successfully recruit existing thalamocortical circuits. It is also necessary to do so during passive as well as active sensing, where in the latter case motor cortex influence on somatosensory cortex becomes significant.
I will present a general framework for using system identification techniques to characterize the properties of the thalamocortical system during sensorimotor integration. I will present data demonstrating how thalamic neurons influence the dynamics of primary somatosensory cortex (S1) neurons in the rat during whisker deflection. I will further demonstrate how S1 response dynamics can be manipulated by thalamic optogenetic stimulation in the absence and presence of congruent primary motor cortex (M1) stimulation. Results suggest that S1 responses to thalamic stimulation have fast dynamics and are frequency-dependent. In contrast, S1 responses to M1 stimulation exhibit much slower dynamics. These results suggest that M1 circuits may be programmed to modulate S1 circuits over long time scales to facilitate sensory expectation of evolving motor commands. Thalamic input, on the other hand, may work to engage or disengage S1 circuits to sudden perturbations from the outside world that may interfere with movement goals during task execution. These findings suggest the need to more systematically characterize how information is integrated between the two areas, and pave the way to improve the design of bi-directional sensorimotor prosthesis where microstimulation of the afferent pathway is optimized based on the underlying system properties.
Jean-Pierre Rospars, INRA Versailles.