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What is in the Synaptic Physiology Dataset

A major focus of this project is to explore the relationship between cell types and synaptic connectivity. Which cell types are more or less likely to be synaptically connected? How are the functional properties of synaptic connections related to cell type? The Synaptic Physiology Dataset describes 1368 chemical synapses from mouse primary visual cortex and 363 from human cortex obtained via simultaneous patch clamp recordings.

 

Chemical and Electrical Synaptic Connections

In the mouse dataset, about 22,000 cell pairs were "probed" for connectivity, meaning that we attempted to detect a synaptic connection from one cell onto another. Of those probed pairs, 1368 had detectable synapses, giving an overall probability of about 6% that two cells are synaptically connected. However, this estimate is derived from a mixture of cell types that are known to have different levels of connectivity. We can begin to group synapses together based on their pre- and postsynaptic cell subclasses. In the mouse tissue, this usually means classifying cells based on expression of transgenic reporters as well as their laminar position in cortex:

 

Probability of chemical synaptic connectivity between cell subclasses in mouse primary visual cortex.

 

The figure above provides an overview of the range of cell subclasses that were tested in the mouse dataset: a focus on intralaminar connectivity between five different excitatory cell subclasses and three inhibitory interneuron subclasses.

 

In human tissue, we do not have access to transgenic reporters as a means of classifying cells. Instead, we rely more heavily on morphological features to separate excitatory from inhibitory classes, and laminar position to separate subclasses:

Probability of chemical synaptic connectivity between pyramidal cells in human temporal cortex.

 

 

In addition to cell type, the distance between two cell bodies is strongly correlated with the probability of connectivity. Connection distances are not carefully controlled in this dataset, so any sampling bias can cause misleading differences in connectivity to appear. To account for this bias, we can look at the relationship between connection probability and intersomatic distance:

Probability of recurrent interconnectivity for three inhibitory interneuron subclasses as a function of intersomatic distance. Blue: distribution of intersomatic distances sampled; red: distribution of intersomatic distances for connected pairs; grey: 95% confidence interval.

 

Synaptic Strength and Kinetics

For each synapse identified in our dataset we measure the latency, strength, and rise / decay kinetics. These values are mainly derived from curve fits to the averaged responses from both current and voltage clamp recordings. With these results one can make comparisons of kinetic parameters across cell types or construct models with biophysically realistic synapse properties.

Four kinetic parameters -- latency, PSC rise time, PSC decay tau, and resting state PSP amplitude -- are compared for excitatory input onto the three major inhibitory interneuron subclasses.

The kinetic parameters shown above are available in all three database versions. For a customized characterization of synaptic response properties, the raw time series data for every stimulus/response is available in the "full" database.

 

Synaptic Short-term Plasticity

Synapses dynamically vary their strength of signaling over time in a way that is highly stochastic and also depends on the prior history of activity at the synapse. This dynamic nature of synapses increases their computational diversity and is believed to be a major determinant in the behavior of neuronal networks in the brain. The stimuli in our dataset were designed to explore a range of synaptic temporal dynamics.

Per-spike response properties are recorded for every synapse in our dataset, but we have also generated three simple metrics that describe some of the most basic features of synaptic short-term plasticity.

A comparison of short-term plasticity between pyramidal cells and three interneuron subclasses.

What's coming in the next release

  • More human and mouse synapses
  • Random-interval stimulus trains
  • Characterization of electrical synapses
  • Characterization of intrinsic cell properties