Biology subjects

Carr, N.

Publications and source records attributed to Carr, N..

4 recordsLinked to original sources

bioRxiv2025

High-resolution laminar recordings reveal structure-function relationships in monkey V1

The relationship between structural properties of diverse neuronal populations in monkey primary visual cortex (V1) and their in vivo functional responses is not fully understood. We combined high-density Neuropixels recordings across cortical layers of macaque V1 with non-linear dimensionality reduction on waveform shape to delineate nine putative cell classes: 4 narrow-spiking (NS), 4 broad-spiking (BS) and 1 tri-phasic (TP). Using targeted analyses of laminar organization, spike amplitude, multichannel waveforms, functional properties, and network connectivity of these cell classes, we demonstrate four aspects of the V1 microcircuit predicted by anatomical studies but never fully demonstrated in vivo. First, NS neurons were concentrated in layer 4. Second, a large-amplitude NS cell class in layer 4B showed strong direction selectivity. Third, another layer 4B NS class exhibited robust bursting and orientation selectivity. Finally, cross-correlation analysis revealed functional interactions between cells in different layers. Our results highlight how high-resolution electrophysiology can reveal novel relationships between in vivo function of neurons and the underlying circuit. TeaserHigh-resolution electrophysiology used with machine learning reveals links between function and the underlying neural circuitry.

neuroscience↗

bioRxiv2025

Synaptic vesicles that store monoamines and glutamate differ in protein composition.

Neuromodulators such as the monoamines are known to differ from classical neurotransmitters like glutamate in the time scale of signaling due to activation of slower G protein-coupled receptors. Recent work has suggested that the mode of release also differs between classical and modulatory transmitters. Although many components of neurotransmitter release machinery have been identified, we still understand little about the mechanisms responsible for differences in release. In this study, we address the differences between release of dopamine and glutamate by comparing the composition of synaptic vesicles (SVs) that contain the vesicular monoamine transporter 2 (VMAT2) versus vesicular glutamate transporter 2 (VGLUT2). Previous work has shown that these SV populations differ in frequency dependence, recycling kinetics and biogenesis. Taking advantage of a CRISPR-generated knock-in mouse with a cytoplasmic hemagglutinin (HA) tag at the N-terminus of VMAT2 to immunoisolate monoamine SVs, we find differences in the abundance and isoform expression of many SV protein families. Validation in primary neurons and in brain tissue confirms these differences in SV protein abundance between dopamine and glutamate release sites. Functional analysis reveals that the loss of differentially expressed SCAMP5 selectively impairs the recycling of VGLUT2 SVs, sparing VMAT2 vesicles in the same neuronal population. These findings provide new insights into the molecular diversity of SVs and the mechanisms that regulate the release of dopamine and glutamate, with implications for the physiological role of these transmitters and behavior.

neuroscience↗

bioRxiv2023

A cortical information bottleneck during decision-making

Decision-making emerges from distributed computations across multiple brain areas, but it is unclear why the brain distributes the computation. In deep learning, artificial neural networks use multiple areas (or layers) and form optimal representations of task inputs. These optimal representations are sufficient to perform the task well, but minimal so they are invariant to other irrelevant variables. We recorded single neurons and multiunits in dorsolateral prefrontal cortex (DLPFC) and dorsal premotor cortex (PMd) in monkeys during a perceptual decision-making task. We found that while DLPFC represents task-related inputs required to compute the choice, the downstream PMd contains a minimal sufficient, or optimal, representation of the choice. To identify a mechanism for how cortex may form these optimal representations, we trained a multi-area recurrent neural network (RNN) to perform the task. Remarkably, DLPFC and PMd resembling representations emerged in the early and late areas of the multi-area RNN, respectively. The DLPFC-resembling area partially orthogonalized choice information and task inputs and this choice information was preferentially propagated to downstream areas through selective alignment with inter-area connections, while remaining task information was not. Our results suggest that cortex uses multi-area computation to form minimal sufficient representations by preferential propagation of relevant information between areas. SignificanceThe brain uses multiple areas for cognition, decision-making, and action, but it is unclear why cortical activity differs by brain area. Machine learning and information theory suggests that one benefit of multiple areas is that it provides an "information bottleneck" that compresses inputs into an optimal representation that is minimal and sufficient to solve the task. Combining experimental recordings from behaving animals and computational simulations, we show that later brain areas have a tendency to form such minimal sufficient representations of task inputs through preferential propagation of task-relevant information present in earlier areas. Our results thus provide insight into one possible reason why the brain uses multiple brain areas for supporting decision-making and action.

neuroscience↗

bioRxiv2023

Using sentinel nodes to evaluate changing connectivity in a protected areas network

It has been recognized that well-connected networks of protected areas are needed to halt the continued loss of global biodiversity. The recently signed Kunming-Montreal biodiversity agreement commits countries to protecting 30% of terrestrial lands in well-connected networks of protected areas by 2030. To meet these ambitious targets, land-use planners and conservation practitioners will require tools to identify areas important for connectivity and track future changes. In this study we present methods using circuit theoretic models with a subset of sentinel park nodes to evaluate connectivity for a protected areas network. We assigned a lower cost to natural areas within protected areas, under the assumption that animal movement within parks should be less costly given the regulation of activities. We found that by using mean pairwise effective resistance (MPER) as an indicator of overall network connectivity, we were able to detect changes in a parks network in response to simulated land-use changes. As expected, MPER increased with the addition of high-cost developments and decreased with the addition of new, low-cost protected areas. We tested our sentinel node method by evaluating connectivity for the protected areas network in the province of Ontario, Canada. Our method can help provide protected areas ecologists and planners with baseline estimates of connectivity for a given protected areas network and an indicator that can be used to track changes in connectivity in the future.

ecology↗