Synapses change on multiple timescales, ranging from milliseconds to minutes, due to a combination of both short- and long-term plasticity. Here we develop an extension of the common generalized linear model to infer both short- and long-term changes in the coupling between a pre- and postsynaptic neuron based on observed spiking activity. We model short-term synaptic plasticity using additive effects that depend on the presynaptic spike timing, and we model long-term changes in both synaptic weight and baseline firing rate using point process adaptive smoothing. Using simulations, we first show that this model can accurately recover time-varying synaptic weights (1) for both depressing and facilitating synapses, (2) with a variety of long-term changes (including realistic changes, such as due to STDP), (3) with a range of pre and postsynaptic firing rates, and (4) for both excitatory and inhibitory synapses. We then apply our model to two experimentally recorded putative synaptic connections. We find that simultaneously tracking fast changes in synaptic weights, slow changes in synaptic weights, and unexplained variations in baseline firing is essential. Omitting any one of these factors can lead to spurious inferences for the others. Altogether, this model provides a flexible framework for tracking short- and long-term variation in spike transmission.

Many controlled, in vitro studies have demonstrated how postsynaptic responses to presynaptic spikes are not constant but depend on short-term synaptic plasticity (STP) and the detailed timing of presynaptic spikes.However, the effects of short-term plasticity (depression and facilitation) are not limited to short, sub-second timescales. The effects of STP appear on long timescales as changes in presynaptic firing rates lead to changes in steady-state synaptic transmission. Here we examine the relationship between natural variations in the presynaptic firing rates and spike transmission in vivo. Using large-scale spike recordings in awake mice from the Allen Institute Neuropixels dataset, we first detect putative excitatory synaptic connections based on cross-correlations between the spike trains of millions of pairs of neurons. For the subset of pairs where a transient, excitatory effect was detected, we use a model-based approach to track fluctuations in synaptic efficacy and find that efficacy varies substantially on slow (~1 minute) timescales over the course of these recordings. For many connections, the efficacy fluctuations are correlated with fluctuations in the presynaptic firing rate. To understand the potential mechanisms underlying this relationship, we then model the detailed probability of postsynaptic spiking on a millisecond timescale, including both slow changes in postsynaptic excitability and monosynaptic inputs with short-term plasticity. The detailed model reproduces the slow efficacy fluctuations observed with many putative excitatory connections, suggesting that these fluctuations can be both directly predicted based on the time-varying presynaptic firing rate and, at least partly, explained by the cumulative effects of STP..

In many areas of the brain, neural spiking activity covaries with features of the external world, such as sensory stimuli or an animal's movement. Experimental findings suggest that the variability of neural activity changes over time and may provide information about the external world beyond the information provided by the average neural activity. To flexibly track time-varying neural response properties, we developed a dynamic model with Conway-Maxwell Poisson (CMP) observations. The CMP distribution can flexibly describe firing patterns that are both under- and overdispersed relative to the Poisson distribution. Here we track parameters of the CMP distribution as they vary over time. Using simulations, we show that a normal approximation can accurately track dynamics in state vectors for both the centering and shape parameters (λ and ν). We then fit our model to neural data from neurons in primary visual cortex, “place cells” in the hippocampus, and a speed-tuned neuron in the anterior pretectal nucleus. We find that this method outperforms previous dynamic models based on the Poisson distribution. The dynamic CMP model provides a flexible framework for tracking time-varying non-Poisson count data and may also have applications beyond neuroscience.

Modern neural recording techniques allow neuroscientists to observe the spiking activity of many neurons simultaneously. Although previous work has illustrated how activity within and between known populations of neurons can be summarized by low-dimensional latent vectors, in many cases what determines a unique population may be unclear. Neurons differ in their anatomical location, but also, in their cell types and response properties. Moreover, multiple distinct populations may not be well described by a single low-dimensional, linear representation. To tackle these challenges, we develop a clustering method based on a mixture of dynamic Poisson factor analyzers (DPFA) model, with the number of clusters treated as an unknown parameter. To do the analysis of DPFA model, we propose a novel Markov chain Monte Carlo (MCMC) algorithm to efficiently sample its posterior distribution. Validating our proposed MCMC algorithm with simulations, we find that it can accurately recover the true clustering and latent states and is insensitive to the initial cluster assignments. We then apply the proposed mixture of DPFA model to multi-region experimental recordings, where we find that the proposed method can identify novel, reliable clusters of neurons based on their activity, and may, thus, be a useful tool for neural data analysis.

Many controlled, in vitro studies have demonstrated how postsynaptic responses to presynaptic spikes are not constant but depend on short-term synaptic plasticity (STP) and the detailed timing of presynaptic spikes. However, the effects of short-term plasticity (depression and facilitation) are not limited to short, sub-second timescales. The effects of STP appear on long timescales as changes in presynaptic firing rates lead to changes in steady-state synaptic transmission. Here we examine the relationship between natural variations in the presynaptic firing rates and spike transmission in vivo. Using large-scale spike recordings in awake mice from the Allen Institute Neuropixels dataset, we first detect putative excitatory synaptic connections based on cross-correlations between the spike trains of millions of pairs of neurons. For the subset of pairs where a transient, excitatory effect was detected, we use a model-based approach to track fluctuations in synaptic efficacy and find that efficacy varies substantially on slow (~1 minute) timescales over the course of these recordings. For many connections, the efficacy fluctuations are correlated with fluctuations in the presynaptic firing rate. To understand the potential mechanisms underlying this relationship, we then model the detailed probability of postsynaptic spiking on a millisecond timescale, including both slow changes in postsynaptic excitability and monosynaptic inputs with short-term plasticity. The detailed model reproduces the slow efficacy fluctuations observed with many putative excitatory connections, suggesting that these fluctuations can be both directly predicted based on the time-varying presynaptic firing rate and, at least partly, explained by the cumulative effects of STP.