Circumstantial evidence and explanatory models for synapses in large-scale spike recordings

TL;DR

This paper explores how circumstantial evidence and models can be used to infer synaptic connections from large-scale spike recordings, addressing the challenges of establishing causality in neural interactions.

Contribution

It systematically assesses circumstantial evidence for synapses and demonstrates how synaptic models can generate testable predictions from spike train data.

Findings

Models of synaptic effects can explain pair-wise spike statistics.

Circumstantial evidence can support inferences about synapses.

Large-scale recordings enable new data analytic approaches.

Abstract

Whether, when, and how causal interactions between neurons can be meaningfully studied from observations of neural activity alone are vital questions in neural data analysis. Here we aim to better outline the concept of functional connectivity for the specific situation where systems neuroscientists aim to study synapses using spike train recordings. In some cases, cross-correlations between the spikes of two neurons are such that, although we may not be able to say that a relationship is causal without experimental manipulations, models based on synaptic connections provide precise explanations of the data. Additionally, there is often strong circumstantial evidence that pairs of neurons are monosynaptically connected. Here we illustrate how circumstantial evidence for or against synapses can be systematically assessed and show how models of synaptic effects can provide testable predictions for pair-wise spike statistics. We use case studies from large-scale multi-electrode spike recordings to illustrate key points and to demonstrate how modeling synaptic effects using large-scale spike recordings opens a wide range of data analytic questions.