Short-term synaptic plasticity (STP) critically affects the processing of information in neuronal circuits by reversibly changing the effective strength of connections between neurons on time scales from milliseconds to a few seconds. STP is traditionally studied using intracellular recordings of postsynaptic potentials or currents evoked by presynaptic spikes. However, STP also affects the statistics of postsynaptic spikes. Here we present two model-based approaches for estimating synaptic weights and short-term plasticity from pre- and postsynaptic spike observations alone. We extend a generalized linear model (GLM) that predicts postsynaptic spiking as a function of the observed pre- and postsynaptic spikes and allow the connection strength (coupling term in the GLM) to vary as a function of time based on the history of presynaptic spikes. Our first model assumes that STP follows a Tsodyks-Markram description of vesicle depletion and recovery. In a second model, we introduce a functional description of STP where we estimate the coupling term as a biophysically unrestrained function of the presynaptic inter-spike intervals. To validate the models, we test the accuracy of STP estimation using the spiking of pre- and postsynaptic neurons with known synaptic dynamics. We first test our models using the responses of layer 2/3 pyramidal neurons to simulated presynaptic input with different types of STP, and then use simulated spike trains to examine the effects of spike-frequency adaptation, stochastic vesicle release, spike sorting errors, and common input. We find that, using only spike observations, both model-based methods can accurately reconstruct the time-varying synaptic weights of presynaptic inputs for different types of STP. Our models also capture the differences in postsynaptic spike responses to presynaptic spikes following short vs long inter-spike intervals, similar to results reported for thalamocortical connections. These models may thus be useful tools for characterizing short-term plasticity from multi-electrode spike recordings in vivo.

Cell type diversity in the neocortex The ability to link molecularly identified neurons with their function during behavior requires monitoring the activity of these cell types in vivo. Condylis et al . developed a platform for population functional imaging with spatial transcriptomics. Using newly acquired transcriptomic cell census data from the Allen Institute for Brain Science, the authors studied the function of cell types in primary somatosensory cortex in mice performing a tactile working memory task. Task-related properties of both excitatory and inhibitory neurons continued to differentiate as they were segregated into increasingly discrete molecular types. A new excitatory cell type, Baz1a, formed a sensory-driven circuit hub that orchestrates local sensory processing in superficial layers of the neocortex. This approach opens new venues for exploring information processing in the brain. —PRS

Short-term synaptic plasticity (STP) critically affects the processing of information in neuronal circuits by reversibly changing the effective strength of connections between neurons on time scales from milliseconds to a few seconds. STP is traditionally studied using intracellular recordings of postsynaptic potentials or currents evoked by presynaptic spikes. However, STP also affects the statistics of postsynaptic spikes. Here we present two model-based approaches for estimating synaptic weights and short-term plasticity from pre-and postsynaptic spike observations alone. We extend a generalized linear model (GLM) that predicts postsynaptic spiking as a function of the observed pre-and postsynaptic spikes and allow the connection strength (coupling term in the GLM) to vary as a function of time based on the history of presynaptic spikes. Our first model assumes that STP follows a TsodyksMarkram description of vesicle depletion and recovery. In a second model, we introduce a functional description of STP where we estimate the coupling term as a biophysically unrestrained function of the presynaptic inter-spike intervals. To validate the models, we test the accuracy of STP estimation using the spiking of pre-and postsynaptic neurons with known synaptic dynamics. We first test our models using the responses of layer 2/3 pyramidal neurons to simulated presynaptic input with different types of STP, and then use simulated spike trains to examine the effects of spike-frequency adaptation, stochastic vesicle release, spike sorting errors, and common input. We find that, using only spike observations, both model-based methods can accurately reconstruct the time-varying synaptic weights of presynaptic inputs for different types of STP. Our models also capture the differences in postsynaptic spike responses to presynaptic spikes following short vs long inter-spike intervals, similar to results reported for thalamocortical connections. These models may thus be useful tools for characterizing short-term plasticity from multi-electrode spike recordings in vivo. Author summaryInformation processing in the nervous system critically depends on dynamic changes in the strength of connections between neurons. Short-term synaptic plasticity (STP), changes that occur on timescales from milliseconds to a few seconds, is thought to play a role in tasks such as speech recognition, motion detection, and working memory. Although intracellular recordings in slices of neural tissue have identified synaptic mechanisms of STP and have demonstrated its potential role in information processing, studying STP in intact animals, especially during behavior, is experimentally difficult. Unlike intracellular recordings, extracellular spiking of hundreds of neurons simultaneously can be recorded even in behaving animals. Here we developed two models that allow estimation of STP from extracellular spike recordings. We validate these models using results from in vitro experiments which simulate a realistic synaptic input from a population of ...

Information transmission in neural networks is influenced by both short-term synaptic plasticity (STP) as well as nonsynaptic factors, such as after-hyperpolarization currents and changes in excitability. Although these effects have been widely characterized in vitro using intracellular recordings, how they interact in vivo is unclear. Here, we develop a statistical model of the short-term dynamics of spike transmission that aims to disentangle the contributions of synaptic and nonsynaptic effects based only on observed presynaptic and postsynaptic spiking. The model includes a dynamic functional connection with short-term plasticity as well as effects due to the recent history of postsynaptic spiking and slow changes in postsynaptic excitability. Using paired spike recordings, we find that the model accurately describes the short-term dynamics of in vivo spike transmission at a diverse set of identified and putative excitatory synapses, including a pair of connected neurons within thalamus in mouse, a thalamocortical connection in a female rabbit, and an auditory brainstem synapse in a female gerbil. We illustrate the utility of this modeling approach by showing how the spike transmission patterns captured by the model may be sufficient to account for stimulus-dependent differences in spike transmission in the auditory brainstem (endbulb of Held). Finally, we apply this model to large-scale multielectrode recordings to illustrate how such an approach has the potential to reveal cell type-specific differences in spike transmission in vivo. Although STP parameters estimated from ongoing presynaptic and postsynaptic spiking are highly uncertain, our results are partially consistent with previous intracellular observations in these synapses.

Neural responses to repeated presentations of an identical stimulus often show substantial trial-to-trial variability. How the mean firing rate varies in response to different stimuli or during different movements (tuning curves) has been extensively modeled in a wide variety of neural systems. However, the variability of neural responses can also have clear tuning independent of the tuning in the mean firing rate. This suggests that the variability could contain information regarding the stimulus/movement beyond what is encoded in the mean firing rate. Here we demonstrate how taking variability into account can improve neural decoding. In a typical neural coding model spike counts are assumed to be Poisson with the mean response depending on an external variable, such as a stimulus or movement. Bayesian decoding methods then use the probabilities under these Poisson tuning models (the likelihood) to estimate the probability of each stimulus given the spikes on a given trial (the posterior). However, under the Poisson model, spike count variability is always exactly equal to the mean (Fano factor = 1). Here we use two alternative models -the Conway-Maxwell-Poisson (CMP) model and Negative Binomial (NB) model -to more flexibly characterize how neural variability depends on external stimuli. These models both contain the Poisson distribution as a special case but have an additional parameter that allows the variance to be greater than the mean (Fano factor >1) or, for the CMP model, less than the mean (Fano factor <1). We find that neural responses in primary motor (M1), visual (V1), and auditory (A1) cortices have diverse tuning in both their mean firing rates and response variability. Across cortical areas, we find that Bayesian decoders using the CMP or NB models improve stimulus/movement estimation accuracy by 4-12% compared to the Poisson model. Moreover, the uncertainty of the non-Poisson decoders more accurately reflects the magnitude of estimation errors. In addition to tuning curves that reflect average neural responses, stimulus-dependent response variability may be an important aspect of the neural code. Modeling this structure could, potentially, lead to improvements in brain machine interfaces.

250 words) 14 Information transmission in neural networks is influenced by both short-term synaptic plasticity 15(STP) as well as non-synaptic factors, such as after-hyperpolarization currents and changes in 16 excitability. Although these effects have been widely characterized in vitro using intracellular 17 recordings, how they interact in vivo is unclear. Here we develop a statistical model of the short-18 term dynamics of spike transmission that aims to disentangle the contributions of synaptic and 19 non-synaptic effects based only on observed pre-and postsynaptic spiking. The model includes a 20 dynamic functional connection with short-term plasticity as well as effects due to the recent history 21 of postsynaptic spiking and slow changes in postsynaptic excitability. Using paired spike 22recordings, we find that the model accurately describes the short-term dynamics of in vivo spike 23 transmission at a diverse set of identified and putative excitatory synapses, including a 24 thalamothalamic connection in mouse, a thalamocortical connection in a female rabbit, and an 25 auditory brainstem synapse in a female gerbil. We illustrate the utility of this modeling approach 26by showing how the spike transmission patterns captured by the model may be sufficient to account 27for stimulus-dependent differences in spike transmission in the auditory brainstem (endbulb of 28 Held). Finally, we apply this model to large-scale multi-electrode recordings to illustrate how such 29 an approach has the potential to reveal cell-type specific differences in spike transmission in vivo. 30Although short-term synaptic plasticity parameters estimated from ongoing pre-and postsynaptic 31 spiking are highly uncertain, our results are partially consistent with previous intracellular 32 observations in these synapses. 33Significance Statement (120 words) 34 Although synaptic dynamics have been extensively studied and modeled using intracellular 35 recordings of post-synaptic currents and potentials, inferring synaptic effects from extracellular 36 spiking is challenging. Whether or not a synaptic current contributes to postsynaptic spiking 37 depends not only on the amplitude of the current, but also on many other factors, including the 38 activity of other, typically unobserved, synapses, the overall excitability of the postsynaptic 39 neuron, and how recently the postsynaptic neuron has spiked. Here we developed a model that, 40using only observations of pre-and postsynaptic spiking, aims to describe the dynamics of in vivo 41 spike transmission by modeling both short-term synaptic plasticity and non-synaptic effects. This 42 approach may provide a novel description of fast, structured changes in spike transmission. 43

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..

Neural responses to repeated presentations of an identical stimulus often show substantial trial-totrial variability. Although the mean firing rate in response to different stimuli or during different movements (tuning curves) have been extensively modeled, the variability of neural responses can also have clear tuning independent of the tuning in the firing rate. This suggests that the variability carries information regarding the stimulus/movement beyond what is encoded in the mean firing rate. Here we demonstrate how taking variability into account can improve neural decoding.In a typical neural coding model spike counts are assumed to be Poisson with the mean response depending on an external variable, such as a stimulus/movement direction. Bayesian decoding methods then use the probabilities under these Poisson tuning models (the likelihood) to estimate the probability of each stimulus given the spikes on a given trial (the posterior). However, under the Poisson model, spike count variability is always exactly equal to the mean (Fano Factor = 1). Here we use the Conway-Maxwell-Poisson (COM-Poisson) model to more flexibly model how neural variability depends on external stimuli. This model contains the Poisson distribution as a special case, but has an additional parameter that allows both over-and underdispersed data, where the variance is greater than (Fano Factor >1) or less than (Fano Factor <1) the mean, respectively.We find that neural responses in both primary motor cortex (M1) and primary visual cortex (V1) have diverse tuning in both their mean firing rates and response variability. These tuning patterns can be accurately described by the COM-Poisson model, and, in both cortical areas, we find that a Bayesian decoder using the COM-Poisson models improves stimulus/movement estimation by 4-8% compared to the Poisson model. The additional layer of information in response variability thus appears to be an important part of the neural code. .For spike counts the distribution is a function of the intensity and dispersion parameters and with < 1 describing over-dispersion and > 1 describing under-dispersed data. Note that with = 1 the COM-Poisson is exactly the Poisson distribution. Here we use the COM-Poisson distribution as the noise model for a GLM [1] to estimate both the mean and variance of neural responses to varying stimuli/movement. In particular, we estimate parameters and γ that map stimulus/movement covariates ( ) and ( ) to neural responses using the link functions log ( ) = ( ) and log( ( )) = ( ) . This framework is in effect a dual-link GLM where both the mean and the variance depend on the stimulus/movement direction θ.Here we estimate the tuning curves using spline bases and maximum a posteriori (MAP) estimation with L2 regularization. Importantly, this approach allows us to model neural responses that are under-dispersed, overdispersed, or that contain intermingled under-and over-dispersed counts [2]. Using the tuning curve models to describe the likelihood of spike responses, ...