In:
Science, American Association for the Advancement of Science (AAAS), Vol. 375, No. 6585 ( 2022-03-11)
Abstract:
The mammalian neocortex is believed to act as the computational substrate for our highest cognitive abilities, particularly the ability to model the world around us and predict the effects of our actions. Many aspects of cortical structure are repeated across brain regions and conserved across species, suggesting a general-purpose approach to cognition. Within this repeating structure, neurons influence the formation of synaptic connections based on their cell type-specific biases. This results in a stereotyped network architecture in which synapse properties and connectivity are strongly influenced by cell type. Synapses between cell types transmit information in a way that is highly stochastic and depends on the prior history of activity. The dynamic properties of synapses are also strongly dependent on both the pre- and postsynaptic cell types, suggesting an important role in cortical function. This provides a major source of computational diversity that is often absent in neuroscience modeling studies as well as modern machine-learning architectures. Neurons are broadly grouped into excitatory and inhibitory classes, each of which can be divided into more specific subclasses. Cortical inhibitory neurons, for example, are commonly divided into Pvalb, Sst, and Vip subclasses and are distributed broadly across cortical layers. In contrast, most excitatory cell subclasses occupy narrower regions across cortical layers. RATIONALE Understanding the connectivity among cell subclasses and the computations performed at their synapses is an essential step to understanding cortical circuit function. This has led to experiments in different species, brain regions, ages, etc. that focus on one or a few circuit elements. These efforts offer an excellent depth of insight to isolated regions of the circuit but offer a fragmented view of the circuit as a whole. Further, the difficulty of accessing these historical data discourages reuse and reanalysis. We saw an opportunity to expand upon this history and conduct a more comprehensive and standardized survey than has been attempted in the past. By publishing the analyses, tools, and data that characterize cortical connection properties, we encourage a more unified approach to describing cortical function. RESULTS We used microelectrodes to record the activity of 1731 synaptic connections across diverse cell types in living tissue samples from mouse and human neocortex. We characterized these connections with the aid of a synaptic release model and found that excitatory dynamics aligned with postsynaptic cell subclass, whereas inhibitory dynamics aligned with the presynaptic subclass in ways that were subclass specific. Synaptic variability was a primary driver of these cross-subclass differences in mouse cortex. Compared with the mouse, human excitatory connections were tuned toward stability and reliability pointing toward species differences in cortical function. We further introduced a method to estimate the rate of connectivity between cell types that accounts for differences between experimental preparations. With this approach, we compared connection probabilities across layer, cell subclass, and species. For instance, connectivity between excitatory cells and Vip inhibitory cells was present in layer 2/3 and absent in layer 5/6 of mouse cortex. Likewise, connection probability among layer 4 excitatory cells was high in mouse cortex and nearly absent in human cortex. Overall, we found that layer-specific circuit representations are necessary to capture the diversity of intralaminar connectivity among cortical cell subclasses. CONCLUSION We have generated a comprehensive dataset describing synaptic connections within each layer in the mouse and human cortex. Our deep characterization of synapses points toward important principles of cortical organization that relate to current topics in computational neuroscience and machine learning. The open distribution of our data, analyses, and tools enables greater realism in constraining network and synapse models. Intralaminar circuit diagram among major excitatory (Pyr) and inhibitory (Pvalb, Sst, and Vip) cell subclasses aggregated from all layers of mouse primary visual cortex. Line (axon) thickness depicts the relative weight (strength and probability of connection) of connections between subclasses. Black dots indicate connections that are stronger in layer 2/3 compared with layer 5. Axon color shows the spike-to-spike variance in amplitude of synaptic signaling, which is strongly cell subclass dependent. Excitatory synapse variance depends on the postsynaptic subclass. Pvalb cells project low-variance connections, whereas Sst and Vip project high-variance connections. More saturated axon colors indicate higher confidence measurements.
Type of Medium:
Online Resource
ISSN:
0036-8075
,
1095-9203
DOI:
10.1126/science.abj5861
Language:
English
Publisher:
American Association for the Advancement of Science (AAAS)
Publication Date:
2022
detail.hit.zdb_id:
128410-1
detail.hit.zdb_id:
2066996-3
detail.hit.zdb_id:
2060783-0
SSG:
11
Permalink
