Image 01 Descending control of neural bias and selectivity in a spatial attention network: Rules and mechanisms
Mysore SP and Knudsen EI (2014), Neuron, 84(1):214-26 

The brain integrates stimulus-driven (exogenous) activity with internally generated (endogenous) activity to compute the highest priority stimulus for gaze and attention. Little is known about how this computation is accomplished neurally. We explored the underlying functional logic in a critical component of the spatial attention network, the optic tectum (OT, superior colliculus in mammals), in awake barn owls. We found that space-specific endogenous influences, evoked by activating descending forebrain pathways, bias competition among exogenous influences, and substantially enhance the quality of the categorical neural pointer to the highest priority stimulus. These endogenous influences operate across sensory modalities. Biologically grounded modeling revealed that the observed effects on network bias and selectivity require a simple circuit mechanism: endogenously driven gain modulation of feedback inhibition among competing channels. Our findings reveal fundamental principles by which internal and external information combine to guide selection of the next target for gaze and attention.

Image 02(17) Spatially reciprocal inhibition of inhibition within a stimulus selection network in the avian midbrain.
Goddard CA, Mysore SP, Bryant AS, Huguenard JR, Knudsen EI (2014), PLoS One, 9(1):e85865.

Reciprocal inhibition between inhibitory projection neurons has been proposed as the most efficient circuit motif to achieve the flexible selection of one stimulus among competing alternatives. However, whether such a motif exists in networks that mediate selection is unclear. Here, we study the connectivity within the nucleus isthmi pars magnocellularis (Imc), a GABAergic nucleus that mediates competitive selection in the midbrain stimulus selection network. Using laser photostimulation of caged glutamate, we find that feedback inhibitory connectivity is global within the Imc. Unlike typical lateral inhibition in other circuits, intra-Imc inhibition remains functionally powerful over long distances. Anatomically, we observed long-range axonal projections and retrograde somatic labeling from focal injections of bi-directional tracers in the Imc, consistent with spatial reciprocity of intra-Imc inhibition. Together, the data indicate that spatially reciprocal inhibition of inhibition occurs throughout the Imc. Thus, the midbrain selection circuit possesses the most efficient circuit motif possible for fast, reliable, and flexible selection.

Image 01(16) A shared inhibitory circuit for both exogenous and endogenous control of stimulus selection.
Mysore SP and Knudsen EI (2013), Nat Neurosci, 16(4):473-8.

The mechanisms by which the brain suppresses distracting stimuli to control the locus of attention are unknown. We found that focal, reversible inactivation of a single inhibitory circuit in the barn owl midbrain tegmentum, the nucleus isthmi pars magnocellularis (Imc), abolished both stimulus-driven (exogenous) and internally driven (endogenous) competitive interactions in the optic tectum (superior colliculus in mammals), which are vital to the selection of a target among distractors in behaving animals. Imc neurons transformed spatially precise multisensory and endogenous input into powerful inhibitory output that suppressed competing representations across the entire tectal space map. We identified a small, but highly potent, circuit that is employed by both exogenous and endogenous signals to exert competitive suppression in the midbrain selection network. Our findings reveal, to the best of our knowledge, for the first time, a neural mechanism for the construction of a priority map that is critical for the selection of the most important stimulus for gaze and attention. Preview in NRN

Image 02(15) Reciprocal inhibition of inhibition: a circuit motif for flexible categorization in stimulus selection.
Mysore SP and Knudsen EI (2012), Neuron, 73(1):193-205.

As a precursor to the selection of a stimulus for gaze and attention, a midbrain network categorizes stimuli into "strongest" and "others." The categorization tracks flexibly, in real time, the absolute strength of the strongest stimulus. In this study, we take a first-principles approach to computations that are essential for such categorization. We demonstrate that classical feedforward lateral inhibition cannot produce flexible categorization. However, circuits in which the strength of lateral inhibition varies with the relative strength of competing stimuli categorize successfully. One particular implementation--reciprocal inhibition of feedforward lateral inhibition--is structurally the simplest, and it outperforms others in flexibly categorizing rapidly and reliably. Strong predictions of this anatomically supported circuit model are validated by neural responses measured in the owl midbrain. The results demonstrate the extraordinary power of a remarkably simple, neurally grounded circuit motif in producing flexible categorization, a computation fundamental to attention, perception, and decision making. Preview

(14r) Mysore SP, Knudsen EI (2011). The role of a midbrain network in competitive stimulus selection.
Curr Opin Neurobiol 21(4): 653-60.
(13) Mysore SP, Knudsen EI (2011). Flexible categorization of relative stimulus strength by the optic tectum.
J Neurosci 31:7745-52.
(12) Asadollahi A, Mysore SP, Knudsen EI (2011). Rules of competitive stimulus selection in a cholinergic isthmic nucleus of the owl midbrain.
J Neurosci 31: 6088-6097.
(11) Mysore SP, Asadollahi A, Knudsen EI (2011). Signaling of the strongest stimulus in the owl optic tectum.
J Neurosci 31: 5186-5196. [Cover article] [Covered in Nature News]
(10) Asadollahi A, Mysore SP, Knudsen EI (2010). Stimulus-driven competition in a cholinergic midbrain nucleus.
Nat Neurosci 13: 889-895.
(9) Mysore SP*, Asadollahi A*, Knudsen EI (2010). Global inhibition and stimulus competition in the owl optic tectum.
J Neurosci 30: 1727-1738. (* co-authorship)
(8r) Mysore SP, Tai C-Y, Schuman EM (2008). N-cadherin, spine dynamics, and synaptic function
Frontiers in Neuroscience 2: 168-175.
(7) Mysore SP, Tai C-Y, Schuman EM (2007). Effects of N-cadherin disruption on spine morphological dynamics.
Frontiers in Cellular Neuroscience 1: 1-14.
(6) Tai C-Y, Mysore SP, Chiu C, Schuman EM (2007). Activity-regulated N-cadherin endocytosis.
Neuron 54(5):771-785.
(5b) Shultz TR, Mysore SP, Quartz SR (2007). Why let networks grow?
in Constructing Cognition: How the Brain Develops Representations Vol II. Perspectives and Prospects 65-98, Oxford University Press.
(4) Mysore SP, Quartz SR (2005). Modeling structural plasticity in the barn owl auditory localization system with a spike-time dependent Hebbian learning rule
Proc. Intl. Joint Conf. on Neural Networks, Montreal 5: 2766-2771.
(3) Goebel K, Mysore SP (2002). Factoring in a-priori classier performance into decision fusion
Proc. SPIE, Sensor Fusion: Architectures, Algorithms, and Applications VI10-21.
(2) Goebel K, Mysore SP (2001). Taking advantage of misclassifications to boost classification rate in decision fusion
Proc. SPIE, Sensor Fusion: Architectures, Algorithms, and Applications V 11-20.
(1) Kumara SRT, Suh J, Mysore SP (1999). Machinery fault diagnosis and prognosis: application of advanced signal processing techniques
CIRP Annals Vol. 48/1, 317-320.