Genetically encoded voltage indicators (GEVIs) enable monitoring of neuronal activity at high spatial and temporal resolution. However, the utility of existing GEVIs has been limited by the brightness and photostability of fluorescent proteins and rhodopsins. We engineered a GEVI, called Voltron, that uses bright and photostable synthetic dyes instead of protein-based fluorophores, thereby extending the number of neurons imaged simultaneously in vivo by a factor of 10 and enabling imaging for significantly longer durations relative to existing GEVIs. We used Voltron for in vivo voltage imaging in mice, zebrafish, and fruit flies. In the mouse cortex, Voltron allowed single-trial recording of spikes and subthreshold voltage signals from dozens of neurons simultaneously over a 15-minute period of continuous imaging. In larval zebrafish, Voltron enabled the precise correlation of spike timing with behavior.

We present a unique, extensive, and open synaptic physiology analysis platform and dataset. Through its application, we reveal principles that relate cell type to synaptic properties and intralaminar circuit organization in the mouse and human cortex. The dynamics of excitatory synapses align with the postsynaptic cell subclass, whereas inhibitory synapse dynamics partly align with presynaptic cell subclass but with considerable overlap. Synaptic properties are heterogeneous in most subclass-to-subclass connections. The two main axes of heterogeneity are strength and variability. Cell subclasses divide along the variability axis, whereas the strength axis accounts for substantial heterogeneity within the subclass. In the human cortex, excitatory-to-excitatory synaptic dynamics are distinct from those in the mouse cortex and vary with depth across layers 2 and 3.

Generating a comprehensive description of cortical networks requires a large-scale, systematic approach. To that end, we have begun a pipeline project using multipatch electrophysiology, supplemented with two-photon optogenetics, to characterize connectivity and synaptic signaling between classes of neurons in adult mouse primary visual cortex (V1) and human cortex. We focus on producing results detailed enough for the generation of computational models and enabling comparison with future studies. Here, we report our examination of intralaminar connectivity within each of several classes of excitatory neurons. We find that connections are sparse but present among all excitatory cell classes and layers we sampled, and that most mouse synapses exhibited short-term depression with similar dynamics. Synaptic signaling between a subset of layer 2/3 neurons, however, exhibited facilitation. These results contribute to a body of evidence describing recurrent excitatory connectivity as a conserved feature of cortical microcircuits.

SUMMARY Feedforward inhibition sharpens the precision of neurons throughout ascending auditory pathways, including the binaural neurons of the medial superior olive (MSO). However, the biophysical influence of inhibition is poorly understood, particularly at higher frequencies where the relative phase of inhibition and excitation becomes ambiguous. Here, we show in gerbil MSO principal cells in vitro that feedforward inhibition precedes direct excitation, providing a concurrent hyperpolarization and conductance shunt during EPSP summation. We show with dual patch recordings and dynamic clamp that both the linearity and temporal fidelity of synaptic integration is improved by reducing Kv1 potassium channel conductance during inhibition, which counters membrane shunting even at high frequencies where IPSPs sum. The reduction of peak excitation by preceding inhibition lowers spike probability, narrowing but not shifting the window for detecting binaural coincidence. The interplay between inhibition and potassium conductances thus improves the consistency and resolution of ITD coding across different frequencies.

Classic in vitro studies have described spike-timing-dependent plasticity (STDP) at a synapse: the connection from neuron A to neuron B is strengthened (or weakened) when A fires before (or after) B within an optimal time window. Accordingly, more recent in vivo works have demonstrated behavioral effects consistent with an STDP mechanism; however, many relied on single-unit recordings. The ability to modify cortical connections becomes useful in the context of injury, when connectivity and associated behavior are compromised. To avoid the need for long-term, stable isolation of single units, one could control timed activation of two cortical sites with paired electrical stimulation. We tested the hypothesis that STDP could be induced via prolonged paired stimulation as quantified by cortical evoked potentials (EPs) in the sensorimotor cortex of awake, behaving monkeys. Paired simulation between two interconnected sites produced robust effects in EPs consistent with STDP, but only at 2/15 tested pairs. The stimulation protocol often produced increases in global network excitability or depression of the conditioned pair. Together, these results suggest that paired stimulation in vivo is a viable method to induce STDP between cortical populations, but that factors beyond activation timing must be considered to produce conditioning effects.

Understanding cortical function will require a detailed and comprehensive knowledge of local circuit properties. The Allen Institute for Brain Science is beginning a large-scale project using multipatch electrophysiology, supplemented with 2-photon optogenetics, to characterize local connectivity and synaptic signaling between major classes of neurons in the adult mouse primary visual cortex and neurosurgical samples from human frontal and temporal cortex. We focus on generating results that are detailed enough for the generation of computational models and enable rigorous comparison with future studies. Here we report our examination of the intralaminar connectivity within each of several classes of excitatory neurons. We find that connections are sparse but present among all excitatory cell types and layers we sampled, with the most sparse connections in layers 5 and 6. Almost all synapses in mouse exhibited short-term depression with similar dynamics. Synaptic signaling between a subset of layer 2/3 neurons, however, exhibited facilitation. These results contribute to a body of evidence describing recurrent excitatory connectivity as a conserved feature of cortical microcircuits.

We present a unique, extensive, public synaptic physiology dataset. The dataset contains over 20,000 neuron pairs probed with multipatch using standardized protocols to capture short-term dynamics. Recordings were made in the human temporal cortex and the adult mouse visual cortex. Our main purpose is to offer data and analyses that provide a more complete picture of the cortical microcircuit to the community. We also make several important findings that relate connectivity and synaptic properties to the major cell subclasses and cortical layer via the development of novel analysis methods for quantifying connectivity, synapse properties, and synaptic dynamics. We find that excitatory synaptic dynamics depend strongly on the postsynaptic cell subclass, whereas inhibitory synaptic dynamics depend on the presynaptic cell subclass. Despite these associations, short-term synaptic plasticity is heterogeneous in most subclass to subclass connections. We also find that intralaminar connection probability exhibits a strong layer dependence. In human cortex, we find that excitatory synapses are highly reliable, recover rapidly, and are distinct from mouse excitatory synapses.

The dimensionality of a network's collective activity is the number of modes into which it is organized. This quantity is of great interest in neural coding: small dimensionality suggests a compressed neural code and possibly high robustness and generalizability, while high dimensionality suggests expansion of input features to enable flexible downstream computation. Here, for recurrent neural circuits operating in the ubiquitous balanced regime, we show how dimensionality arises mechanistically via perhaps the most basic property of neural circuits: a single number characterizing the net strength of their connectivity. Our results combine novel theoretical approaches with new analyses of high-density neuropixels recordings and high-throughput synaptic physiology datasets. The analysis of electrophysiological recordings identifies bounds on the dimensionality of neural responses across brain regions, showing that it is on the order of hundreds -- striking a balance between high and low-dimensional codes. Furthermore, focusing on the visual stream, we show that dimensionality expands from primary to deeper visual areas and similarly within an area from layer 2/3 to layer 5. We interpret these results via a novel theoretical result which links dimensionality to a single measure of net connectivity strength. This requires calculations that extend beyond traditional mean-field approaches to neural networks. Our result suggests that areas across the brain operate in a strongly coupled regime where dimensionality is under sensitive control by net connectivity strength; moreover, we show how this net connectivity strength is regulated by local connectivity features, or synaptic motifs. This enables us to interpret changes in dimensionality in terms of changes in coupling among pairs and triplets of neurons. Analysis of large-scale synaptic physiology datasets from both mouse and human cortex then reveal the presence of synaptic coupling motifs capable of substantially regulating this dimensionality.