Model-Based Inference of Synaptic Transmission

Bykowska, Ola; Gontier, Camille; Sax, Anne-Lene; Jia, David; Montero, Milton Llera; Bird, Alex D.; Houghton, Conor; Pfister, Jean-Pascal; Costa, Rui Ponte

doi:10.3389/fnsyn.2019.00021

Cited by 18 publications

(13 citation statements)

References 64 publications

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“…We developed a model of stochastic vesicle release and synaptic dynamics that expands upon standard models (Hennig, 2013) and is similar to some recent models (Barri et al, 2016;Bird et al, 2016;Bykowska et al, 2019). The model was designed to meet several criteria.…”

Section: Stochastic Quantal Release Modelmentioning

confidence: 99%

Local Connectivity and Synaptic Dynamics in Mouse and Human Neocortex

Campagnola

Seeman

Chartrand

et al. 2021

Preprint

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We present a unique, extensive, public synaptic physiology dataset. The dataset contains over 20,000 neuron pairs probed with multipatch using standardized protocols to capture short-term dynamics. Recordings were made in the human temporal cortex and the adult mouse visual cortex. Our main purpose is to offer data and analyses that provide a more complete picture of the cortical microcircuit to the community. We also make several important findings that relate connectivity and synaptic properties to the major cell subclasses and cortical layer via the development of novel analysis methods for quantifying connectivity, synapse properties, and synaptic dynamics. We find that excitatory synaptic dynamics depend strongly on the postsynaptic cell subclass, whereas inhibitory synaptic dynamics depend on the presynaptic cell subclass. Despite these associations, short-term synaptic plasticity is heterogeneous in most subclass to subclass connections. We also find that intralaminar connection probability exhibits a strong layer dependence. In human cortex, we find that excitatory synapses are highly reliable, recover rapidly, and are distinct from mouse excitatory synapses.

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Section: Stochastic Quantal Release Modelmentioning

confidence: 99%

Local Connectivity and Synaptic Dynamics in Mouse and Human Neocortex

Campagnola

Seeman

Chartrand

et al. 2021

Preprint

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“…For each of these 584 models, it is important to note there may be many possible parameter settings that are consistent 585 with the data, particularly when the recording time is limited (Costa et al, 2013). These 586 redundancies are present even in simple quantal analysis methods (Bykowska et al, 2019). Here, 587…”

mentioning

confidence: 99%

Modeling the short-term dynamics of in vivo excitatory spike transmission

Ghanbari

Ren

Keine

et al. 2018

Preprint

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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

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“…their movement direction) in analogy to a biological neurons' synapses that estimate the hidden state of the presynaptic neuron (e.g. presynaptic membrane potential) which could be represented by the local postsynaptic potential at an excitatory synapse [62][63][64][65] . Moreover, neurons are known to change their spiking responses to relevant signals with training 66 .…”

Section: Discussionmentioning

confidence: 99%

Human group coordination in a sensorimotor task with neuron-like decision-making

Schmid

Braun

2020

Sci Rep

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the formation of cooperative groups of agents with limited information-processing capabilities to solve complex problems together is a fundamental building principle that cuts through multiple scales in biology from groups of cells to groups of humans. Here, we study an experimental paradigm where a group of humans is joined together to solve a common sensorimotor task that cannot be achieved by a single agent but relies on the cooperation of the group. in particular, each human acts as a neuron-like binary decision-maker that determines in each moment of time whether to be active or not. inspired by the population vector method for movement decoding, each neuron-like decisionmaker is assigned a preferred movement direction that the decision-maker is ignorant about. from the population vector reflecting the group activity, the movement of a cursor is determined, and the task for the group is to steer the cursor into a predefined target. As the preferred movement directions are unknown and players are not allowed to communicate, the group has to learn a control strategy on the fly from the shared visual feedback. Performance is analyzed by learning speed and accuracy, action synchronization, and group coherence. We study four different computational models of the observed behavior, including a perceptron model, a reinforcement learning model, a Bayesian inference model and a Thompson sampling model that efficiently approximates Bayes optimal behavior. The Bayes and especially the thompson model excel in predicting the human group behavior compared to the other models, suggesting that internal models are crucial for adaptive coordination. We discuss benefits and limitations of our paradigm regarding a better understanding of distributed information processing.

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Model-Based Inference of Synaptic Transmission

Cited by 18 publications

References 64 publications

Local Connectivity and Synaptic Dynamics in Mouse and Human Neocortex

Local Connectivity and Synaptic Dynamics in Mouse and Human Neocortex

Modeling the short-term dynamics of in vivo excitatory spike transmission

Human group coordination in a sensorimotor task with neuron-like decision-making

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