The Ritt lab works in sensory systems neuroscience and neural engineering. The primary neuroscience focus is on neural function during active sensing, in which information is acquired from the environment by behavioral choice, and not simply passive reception of stimuli. A familiar example for humans is eye motions; we choose where to look in part based on what we are trying to see. Similar active choices are ubiquitous across organisms and sensory modalities. We develop and use advanced neurotechnology, especially building on control theory, to support future biomedical applications, in addition to providing state of the art tools to address basic questions of neural function.

Current projects employ electrophysiological, behavioral, optogenetic and computational approaches applied to the rodent whisker system, a highly refined tactile sensory system. Experiments combine multi-electrode recording of brain activity; high speed videography of behavior and development of automated image analysis algorithms; and optical stimulation of specific cell types (e.g., excitatory vs. inhibitory neurons) using genetically targeted expression of light sensitive ion channels. Parallel modeling uses tools from dynamical systems, control theory and decision theory. Augmenting experiments with model-driven, real-time feedback forms a basis for development of brain machine interfaces, with an emphasis on sensory neural prosthetics.

Active touch strategies. Using behavioral training in freely moving mice, in combination with high-speed videography, we assess sensing strategies mice use in tactile tasks, including exploration for reward and aperture width discrimination. For example, we have found that mice, known to coordinate head and whisker motions during tactile search, in fact exhibit sophisticated choices of whether to move whiskers in concert with or counter to their head motion, depending on immediate situation and goal. They moreover adapt their whisker placement towards probable object locations. Understanding the neural basis of sensing requires addressing both feedforward, stimulus-driven neural processing, and such high-level, expectation-driven supervision of sensory motions.

Neural recording and closed loop stimulation in freely moving animals. To understand the active sensing loop — from whisker contacts, through cortex, and back to whisker motions — we add an artificial sensory loop, having developed methods to record and optogenetically stimulate whisker cortex, driven in real time by the animal’s own behavior. We find fast changes in active sensing following somatosensory cortex stimulation, at timescales of individual whisks and in longer bouts of exploratory whisking. This influence of sensory cortex on motor behaviors likely plays a role both in guidance of sensing motions and interpretation of whisker contacts. Combined with other neurotechnology projects, such as a lensless “needle” optrode (with Jerome Mertz, BU) for implantable all-optical interfaces, we investigate the role of specific pathways in somatosensory cortex that process active touch.


Neurocontrol theory. The above experiments expose a major engineering challenge: the underactuation of ensemble-scale neural stimulation technologies, with local ratios of neurons to independent stimulation channels often 1000:1 or greater. With ShiNung Ching (WUSTL), building from collaboration started during his postdoc, and continuing with a BRAIN initiative supported project, we pursue novel adaptations of control theory to large, highly underactuated neural ensembles. While these neural ensembles are uncontrollable in a naive sense, exploitation of dynamics allows construction of controls possibly suitable in practice for neuroprostheses applications and discovery science experiments.


A. Nandi, H. M. Schattler, J. T. Ritt, and S. Ching, “Fundamental limits of forced asynchronous spiking with integrate and fire dynamics” (Accepted, Journal of Mathematical Neuroscience).

C. Ba, M. Palmiere, J. Ritt, J. Mertz (2016) “Dual-modality endomicroscopy with co-registered fluorescence and phase contrast”. Biomedical Optics Express 7(9) 3403-3411.
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D. S. Freedman, J. B. Schroeder, G. I. Telian, Z. Zhang, and J. T. Ritt (2016). “OptoZIF Drive: a 3D printed implant and assembly tool package for neural recording and optical stimulation in freely moving mice.” Journal of Neural Engineering 13(6):066013.
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J. B. Schroeder and J. T. Ritt (2016). “Selection of Head and Whisker Coordination Strategies During Goal Oriented Active Touch”. Journal of Neurophysiology 115: 1797–1809.
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J. T. Ritt and S.N. Ching (2015) “Neurocontrol: Methods, models and technologies for manipulating dynamics in the brain”. IEEE American Control Conference 2015: 3865-3780.
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J. A. Nandi, J. T. Ritt and S.-N. Ching (2014). “Non-negative Inputs for Underactuated Control of Spiking in Coupled Integrate-and-Fire Neurons”. IEEE 53rd Conf. Decision and Control 2014: 3041-3046.
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S.-N. Ching and J. T. Ritt, (2013). “Control strategies for underactuated neural ensembles driven by optogenetic stimulation”. Frontiers in Neural Circuits 7:1-16.
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J. B. Schroeder, V. J. Mariano, G. I. Telian, and J. T. Ritt (2013).  “Stimulation of Somatosensory Cortex Locked to Whisker Motions in a Mouse Model of Active Sensing”.  Conf. Proc. IEEE Eng. Med. Biol. Soc. Neural Eng. (NER) 2013: 637-640.
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J. B. Schroeder and J. T. Ritt (2013). “Extraction of Intended palpation times from facial EMGs in a mouse model of active sensing”.  Conf. Proc. IEEE Eng. Med. Biol. Soc. 2013: 2016-2019
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M. M. Halassa, J. H. Siegle, J. T. Ritt, J. T. Ting, G. Feng and C. I. Moore, (2011). “Selective optical drive of thalamic reticular nucleus generates thalamic bursts & cortical spindles”. Nat. Neurosci. 14(9): 1118-20.
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J. H. Siegle, M. Carlen, K. Meletis, L.-H. Tsai, C. I. Moore and J. T. Ritt, (2011). “Chronically Implanted Hyperdrive for Cortical Recording and Optogenetic Control in Behaving Mice”. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2011: 7529-7532.
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J. T. Ritt and C. I. Moore, (2008) “Response to Diamond, et al.” Neuron 60:745-747.
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J. T. Ritt, M. L. Andermann, C. I. Moore, (2008) “Embodied information processing: vibrissa mechanics and texture features shape micromotions in actively sensing rats”. Neuron 57: 599-613.
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M. A. Saito, T. Goepfert, J. T. Ritt, (2008) “Some thoughts on the concept of co-limitation: Three definitions and the importance of bioavailability”. Limnology and Oceanography 53(1): 276-290.
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M. L. Andermann, J. Ritt, M. A. Neimark, C. I. Moore, (2004) “Neural correlates of vibrissa resonance: band-pass and somatotopic representation of high frequency stimuli”. Neuron, 42(3):451-63.
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J. Ritt, (2003) “Evaluation of entrainment of a nonlinear neural oscillator to white noise”, Phys. Rev. E, 68: 041915.
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Y. Manor, F. Nadim, S. Epstein, J. Ritt, E. Marder and N. Kopell, (1999) “Network oscillations generated by balancing graded asymmetric reciprocal inhibition in passive neurons”. J. Neurosci., 19: 2765-2779.
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C. C. Chow, J. A. White, J. Ritt, and N. Kopell, (1998) “Frequency control in synchronous networks of inhibitory neurons.” J. Comput. Neurosci., 5: 407-420.
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J. A. White, C. C. Chow, J. Ritt, C. Soto-Trevino, and N. Kopell, (1998) “Synchronization and oscillatory dynamics in heterogeneous, mutually inhibited neurons.” J. Comput. Neurosci., 5: 5-16.
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