Detecting synaptic connections using large-scale extracellular spike recordings presents a statistical challenge. While previous methods often treat the detection of each putative connection as a separate hypothesis test, here we develop a modeling approach that infers synaptic connections while incorporating circuit properties learned from the whole network. We use an extension of the Generalized Linear Model framework to describe the cross-correlograms between pairs of neurons and separate correlograms into two parts: a slowly varying effect due to background fluctuations and a fast, transient effect due to the synapse. We then use the observations from all putative connections in the recording to estimate two network properties: the presynaptic neuron type (excitatory or inhibitory) and the relationship between synaptic latency and distance between neurons. Constraining the presynaptic neuron's type, synaptic latencies, and time constants improves synapse detection. In data from simulated networks, this model outperforms two previously developed synapse detection methods, especially on the weak connections. We also apply our model to in vitro multielectrode array recordings from mouse somatosensory cortex. Here our model automatically recovers plausible connections from hundreds of neurons, and the properties of the putative connections are largely consistent with previous research.

8Detecting synaptic connections using large-scale extracellular spike recordings presents a statistical challenge. 9While previous methods often treat the detection of each putative connection as a separate hypothesis test, here 10 we develop a modeling approach that infers synaptic connections while incorporating circuit properties learned 11 from the whole network. We use an extension of the Generalized Linear Model framework to describe the cross-12 correlograms between pairs of neurons and separate correlograms into two parts: a slowly varying effect due to 13 background fluctuations and a fast, transient effect due to the synapse. We then use the observations from all 14 putative connections in the recording to estimate two network properties: the presynaptic neuron type (excitatory 15 or inhibitory) and the relationship between synaptic latency and distance between neurons. Constraining the 16 presynaptic neuron's type, synaptic latencies, and time constants improves synapse detection. In data from 17 simulated networks, this model outperforms two previously developed synapse detection methods, especially on 18 the weak connections. We also apply our model to in vitro multielectrode array recordings from mouse 19 somatosensory cortex. Here our model automatically recovers plausible connections from hundreds of neurons, 20 and the properties of the putative connections are largely consistent with previous research. 21 22 Using in vivo or in vitro multielectrode arrays, the extracellular spiking of hundreds of neurons can be recorded 24 simultaneously. These recordings are allowing new, large-scale studies of neuronal networks (Hahn et al. 2019; 25 Harris et al. 2003; Levenstein et al. 2019; Okun et al. 2015; Tingley and Buzsáki 2018), and the number of 26 neurons that can be simultaneously recorded is increasing approximately exponentially (Stevenson and Kording 27 2011). Depending on the species, brain area, and electrode configuration, these simultaneously recorded 28 neurons can have tens of thousands of potential synapses between them. Detecting and characterizing these 29 synapses represents a major challenge for neural data analysis. Here, we develop a model-based method 30 incorporating network-level constraints on 1) the presynaptic neuron type and 2) the synaptic latencies between 31 pre-and postsynaptic neurons. We examine whether these constraints can improve synapse detection using 32 simulated data and large-scale in vitro multielectrode array recordings. 33 34Detecting synaptic connections from extracellular spike observations is a difficult statistical problem. Since both 35 spiking and synapses themselves are sparse, it is often difficult to distinguish between changes in spike 36 probability that are due to a specific synaptic input, changes that are due other (typically unobserved) inputs, or 37 due to chance. Using extracellular spike data, researchers often identify putative monosynaptic connections by 38 examining cross-correlograms between the spiking of two neurons. If two neu...

Functional networks of cortical neurons contain highly interconnected hubs, forming a rich-club structure. However, the cell type composition within this distinct subnetwork and how it influences large-scale network dynamics is unclear. Using spontaneous activity recorded from hundreds of cortical neurons in orbitofrontal cortex of awake behaving mice we show that the rich-club is disproportionately composed of inhibitory neurons, and that inhibitory neurons within the rich-club are significantly more synchronous than other neurons. At the population level, Granger causality showed that neurons in the rich-club are the dominant drivers of overall population activity and do so in a frequency-specific manner. Moreover, early activity ofinhibitory neurons, along with excitatory neurons within the rich-club, synergistically predicts the duration of neuronal cascades. Together, these results reveal an unexpected role of a highly connected core of inhibitory neurons in driving and sustaining activity in local cortical networks.