David Grant Colburn Hildebrand

SUMMARY: The ability to discriminate sensory stimuli is fundamental to sensory-guided behavior. Sensory discrimination by neural populations is constrained by the magnitude of trial-to-trial spiking variability, or noise, and its geometric alignment with the stimulus encoding directions of the population response. Computational models predict that cortical inhibition shapes the noise geometry, but causal evidence from primate cortex is lacking. We used optogenetic stimulation of parvalbumin-expressing inhibitory interneurons (PV-cells) combined with high-density extracellular recordings in primary visual cortex of awake marmoset monkeys to test this prediction. Stimulating PV-cells improved the discriminability of V1 population responses, without expanding the stimulus-related signal magnitude. Instead, PV-cell activation compressed shared trial-to-trial variability and rotated it away from the stimulus-coding direction. Our results establish a causal role for inhibition in shaping the geometry of neural population responses to improve sensory discrimination in the primate visual cortex.

The mosquito Aedes aegypti ’s human host-seeking behavior depends on the integration of multiple sensory cues. One of these cues, carbon dioxide (CO2), gates odorant and heat pathways and activates host-seeking behavior. The neuronal circuits underlying processing of CO2 information remain unclear. We used automated serial-section transmission electron microscopy (EM) to image and reconstruct the circuitry of the glomeruli that are innervated by the Ae. aegypti maxillary palp, including the glomerulus that responds to CO2. Notably, CO2-sensitive olfactory sensory neurons (OSNs) make high levels of recurrent synaptic connections with one another, while making a low density of feedforward synapses. At some of these contacts between CO2 OSNs, we observe ribbon- like presynaptic structures, which may further enhance recurrent signaling. We compared both feedforward and recurrent connectivity with all olfactory glomeruli in Drosophila melanogaster, and we found more recurrent connections between the Ae. aegypti CO2-responsive OSNs than in any D. melanogaster glomeruli. We developed a computational circuit model that demonstrates recurrent synapses are necessary for robust CO2 detection under normal physiological conditions. Together, elevated levels of recurrent connectivity and ribbon-like structures may amplify sensory information detected by CO2-sensitive OSNs to support mosquito activation and sensitization by CO2, even in the presence of high levels of other odorants in the environment. We propose that this circuit organization supports the salience of CO2 as a mosquito host cue. One Sentence Summary: Connectomic analysis of carbon dioxide circuitry in the disease-vector mosquito Aedes aegypti .

The posterior parietal cortex exhibits choice-selective activity during perceptual decision-making tasks^{1,2,3,4,5,6,7,8,9,10}. However, it is not known how this selective activity arises from the underlying synaptic connectivity. Here we combined virtual-reality behaviour, two-photon calcium imaging, high-throughput electron microscopy and circuit modelling to analyse how synaptic connectivity between neurons in the posterior parietal cortex relates to their selective activity. We found that excitatory pyramidal neurons preferentially target inhibitory interneurons with the same selectivity. In turn, inhibitory interneurons preferentially target pyramidal neurons with opposite selectivity, forming an opponent inhibition motif. This motif was present even between neurons with activity peaks in different task epochs. We developed neural-circuit models of the computations performed by these motifs, and found that opponent inhibition between neural populations with opposite selectivity amplifies selective inputs, thereby improving the encoding of trial-type information. The models also predict that opponent inhibition between neurons with activity peaks in different task epochs contributes to creating choice-specific sequential activity. These results provide evidence for how synaptic connectivity in cortical circuits supports a learned decision-making task.

Multiphoton microscopy can resolve fluorescent structures and dynamics deep in scattering tissue and has transformed neural imaging, but applying this technique in vivo can be limited by the mechanical and optical constraints of conventional objectives. Short working distance objectives can collide with compact surgical windows or other instrumentation and preclude imaging. Here we present an ultra-long working distance (20 mm) air objective called the Cousa objective. It is optimized for performance across multiphoton imaging wavelengths, offers a more than 4 mm² field of view with submicrometer lateral resolution and is compatible with commonly used multiphoton imaging systems. A novel mechanical design, wider than typical microscope objectives, enabled this combination of specifications. We share the full optical prescription, and report performance including in vivo two-photon and three-photon imaging in an array of species and preparations, including nonhuman primates. The Cousa objective can enable a range of experiments in neuroscience and beyond.

The cerebellum is thought to help detect and correct errors between intended and executed commands^1,2 and is critical for social behaviours, cognition and emotion^3,4,5,6. Computations for motor control must be performed quickly to correct errors in real time and should be sensitive to small differences between patterns for fine error correction while being resilient to noise⁷. Influential theories of cerebellar information processing have largely assumed random network connectivity, which increases the encoding capacity of the network’s first layer^{8,9,10,11,12,13}. However, maximizing encoding capacity reduces the resilience to noise⁷. To understand how neuronal circuits address this fundamental trade-off, we mapped the feedforward connectivity in the mouse cerebellar cortex using automated large-scale transmission electron microscopy and convolutional neural network-based image segmentation. We found that both the input and output layers of the circuit exhibit redundant and selective connectivity motifs, which contrast with prevailing models. Numerical simulations suggest that these redundant, non-random connectivity motifs increase the resilience to noise at a negligible cost to the overall encoding capacity. This work reveals how neuronal network structure can support a trade-off between encoding capacity and redundancy, unveiling principles of biological network architecture with implications for the design of artificial neural networks.

Motor circuits develop in sequence from those governing fast movements to those governing slow. Here we examine whether upstream sensory circuits are organized by similar principles. Using serial-section electron microscopy in larval zebrafish, we generated a complete map of the gravity-sensing (utricular) system spanning from the inner ear to the brainstem. We find that both sensory tuning and developmental sequence are organizing principles of vestibular topography. Patterned rostrocaudal innervation from hair cells to afferents creates an anatomically inferred directional tuning map in the utricular ganglion, forming segregated pathways for rostral and caudal tilt. Furthermore, the mediolateral axis of the ganglion is linked to both developmental sequence and neuronal temporal dynamics. Early-born pathways carrying phasic information preferentially excite fast escape circuits, whereas later-born pathways carrying tonic signals excite slower postural and oculomotor circuits. These results demonstrate that vestibular circuits are organized by tuning direction and dynamics, aligning them with downstream motor circuits and behaviors.

The posterior parietal cortex (PPC) exhibits choice-selective activity during perceptual decision-making tasks. However, it is not known how this selective activity arises from the underlying synaptic connectivity. Here, we combined virtual reality behavior, two-photon calcium imaging, high throughput electron microscopy, and circuit modeling to analyze how synaptic connectivity between neurons in PPC relates to their selective activity. We found that excitatory pyramidal neurons preferentially target inhibitory interneurons with the same selectivity. In turn, inhibitory interneurons preferentially target pyramidal neurons with opposite selectivity, forming an opponent inhibition motif. Using circuit models, we show that opponent inhibition amplifies selective inputs and induces competition between neural populations with opposite selectivity, thereby improving the encoding of trial-type information. These results provide evidence for how synaptic connectivity in cortical circuits supports a learned decision-making task.

Summary: The cerebellum is thought to detect and correct errors between intended and executed commands ^1–3 and is critical for social behaviors, cognition and emotion ^4–6 . Computations for motor control must be performed quickly to correct errors in real time and should be sensitive to small differences between patterns for fine error correction while being resilient to noise ⁷ . Influential theories of cerebellar information processing have largely assumed random network connectivity, which increases the encoding capacity of the network’s first layer ^8–13 . However, maximizing encoding capacity reduces resiliency to noise ⁷ . To understand how neuronal circuits address this fundamental tradeoff, we mapped the feedforward connectivity in the mouse cerebellar cortex using automated large-scale transmission electron microscopy (EM) and convolutional neural network-based image segmentation. We found that both the input and output layers of the circuit exhibit redundant and selective connectivity motifs, which contrast with prevailing models. Numerical simulations suggest these redundant, non-random connectivity motifs increase discriminability of similar input patterns at a minimal cost to the network’s overall encoding capacity. This work reveals how neuronal network structure can balance encoding capacity and redundancy, unveiling principles of biological network architecture with implications for artificial neural network design.

Marmoset vocal behavior shows high plasticity throughout development, a crucial precursor for human vocal development.