Title: Neural Circuit Mechanisms of Stimulus Selection Underlying Spatial Attention
Thesis Committee: Shreesh P. Mysore, Hynek Hermansky, Mounya Elhilali, Ralph Etienne-Cummings
Abstract: Humans and animals routinely encounter competing pieces of information in their environments, and must continually select the most salient in order to survive and behave adaptively. Here, using computational modeling, extracellular neural recordings, and focal, reversible silencing of neurons in the midbrain of barn owls, we uncovered how two essential computations underlying competitive selection are implemented in the brain: a) the ability to select the most salient stimulus among all pairs of stimulus locations, and b) the ability to signal the most salient stimulus categorically.
We first discovered that a key inhibitory nucleus in the midbrain attention network, called isthmi pars magnocellularis (Imc), encodes visual space with receptive fields that have multiple excitatory hotspots (‘‘lobes’’). Such (previously unknown) multilobed encoding of visual space is necessitated for selection at all location-pairs in the face of scarcity of Imc neurons. Although distributed seemingly randomly, the RF lobe-locations are optimized across the high-firing Imc neurons, allowing them to combinatorially solve selection across space. This combinatorially optimized inhibition strategy minimizes metabolic and wiring costs.
Next, we discovered that a ‘donut-like’ inhibitory mechanism in which each competing option suppresses all options except itself is highly effective at generating categorical responses. It surpasses motifs of feedback inhibition, recurrent excitation, and divisive normalization used commonly in decision-making models. We demonstrated experimentally not only that this mechanism operates in the midbrain spatial selection network in barn owls, but also that it is required for categorical signaling by it. Moreover, the pattern of inhibition in the midbrain forms an exquisitely structured ‘multi-holed’ donut consistent with this network’s combinatorial inhibitory function (computation 1).
Our work demonstrates that the vertebrate midbrain uses seemingly carefully optimized structural and functional strategies to solve challenging computational problems underlying stimulus selection and spatial attention at all location pairs. The neural motifs discovered here represent circuit-based solutions that are generalizable to other brain areas, other forms of behavior (such as decision-making, action selection) as well as for the design of artificial systems (such as robotics, self-driving cars) that rely on the selection of one among many options.