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

2018 ◽  
Author(s):  
Abed Ghanbari ◽  
Naixin Ren ◽  
Christian Keine ◽  
Carl Stoelzel ◽  
Bernhard Englitz ◽  
...  
Keyword(s):  
Synaptic Plasticity ◽  
Large Scale ◽  
Auditory Brainstem ◽  
Postsynaptic Neuron ◽  
Intracellular Recordings ◽  
Short Term ◽  
Functional Connection ◽  
Synaptic Dynamics ◽  
Synaptic Effects

AbstractInformation transmission in neural networks is influenced by both short-term synaptic plasticity (STP) as well as non-synaptic 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 non-synaptic effects based only on observed pre- 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 thalamothalamic connection 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 multi-electrode recordings to illustrate how such an approach has the potential to reveal cell-type specific differences in spike transmission in vivo. Although short-term synaptic plasticity parameters estimated from ongoing pre- and postsynaptic spiking are highly uncertain, our results are partially consistent with previous intracellular observations in these synapses.Significance StatementAlthough synaptic dynamics have been extensively studied and modeled using intracellular recordings of post-synaptic currents and potentials, inferring synaptic effects from extracellular spiking is challenging. Whether or not a synaptic current contributes to postsynaptic spiking depends not only on the amplitude of the current, but also on many other factors, including the activity of other, typically unobserved, synapses, the overall excitability of the postsynaptic neuron, and how recently the postsynaptic neuron has spiked. Here we developed a model that, using only observations of pre- and postsynaptic spiking, aims to describe the dynamics of in vivo spike transmission by modeling both short-term synaptic plasticity and non-synaptic effects. This approach may provide a novel description of fast, structured changes in spike transmission.

scholarly journals Factors Influencing Short-term Synaptic Plasticity in the Avian Cochlear Nucleus Magnocellularis

2015 ◽  
Vol 9s2 ◽  
pp. JEN.S25472 ◽  
Author(s):  
Jason Tait Sanchez Quinones ◽  
Quinones Karla ◽  
Otto-Meyer Sebastian
Keyword(s):  
Synaptic Plasticity ◽  
Cochlear Nucleus ◽  
Time Course ◽  
Synaptic Depression ◽  
Auditory Brainstem ◽  
Short Term ◽  
Nucleus Magnocellularis ◽  
Developmental Shift ◽  
Acid Type ◽  
Synaptic Dynamics

Defined as reduced neural responses during high rates of activity, synaptic depression is a form of short-term plasticity important for the temporal filtering of sound. In the avian cochlear nucleus magnocellularis (NM), an auditory brainstem structure, mechanisms regulating short-term synaptic depression include pre-, post-, and extrasynaptic factors. Using varied paired-pulse stimulus intervals, we found that the time course of synaptic depression lasts up to four seconds at late-developing NM synapses. Synaptic depression was largely reliant on exogenous Ca2+-dependent probability of presynaptic neurotransmitter release, and to a lesser extent, on the desensitization of postsynaptic α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid-type glutamate receptor (AMPA-R). Interestingly, although extrasynaptic glutamate clearance did not play a significant role in regulating synaptic depression, blocking glutamate clearance at early-developing synapses altered synaptic dynamics, changing responses from depression to facilitation. These results suggest a developmental shift in the relative reliance on pre-, post-, and extrasynaptic factors in regulating short-term synaptic plasticity in NM.

scholarly journals Estimating short-term synaptic plasticity from pre- and postsynaptic spiking

2017 ◽  
Author(s):  
Abed Ghanbari ◽  
Aleksey Malyshev ◽  
Maxim Volgushev ◽  
Ian H. Stevenson
Keyword(s):  
Information Processing ◽  
Synaptic Plasticity ◽  
Intracellular Recordings ◽  
Coupling Term ◽  
Short Term ◽  
Short Term Plasticity ◽  
Postsynaptic Spike ◽  
Model Based ◽  
Different Types ◽  
Synaptic Dynamics

AbstractShort-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.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 presynaptic neurons with defined STP rules. Our results show that both new models can accurately recover the synaptic dynamics underlying spiking. These new methods will allow us to study STP using extracellular recordings, and therefore on a much larger scale than previously possible in behaving animals.

scholarly journals Event-Driven Simulation Scheme for Spiking Neural Networks Using Lookup Tables to Characterize Neuronal Dynamics

2006 ◽  
Vol 18 (12) ◽  
pp. 2959-2993 ◽  
Author(s):  
Eduardo Ros ◽  
Richard Carrillo ◽  
Eva M. Ortigosa ◽  
Boris Barbour ◽  
Rodrigo Agís
Keyword(s):  
Synaptic Plasticity ◽  
High Speed ◽  
Large Scale ◽  
Computational Cost ◽  
Brain Structures ◽  
Spike Timing ◽  
Neuronal Dynamics ◽  
Event Driven ◽  
Synaptic Dynamics

Nearly all neuronal information processing and interneuronal communication in the brain involves action potentials, or spikes, which drive the short-term synaptic dynamics of neurons, but also their long-term dynamics, via synaptic plasticity. In many brain structures, action potential activity is considered to be sparse. This sparseness of activity has been exploited to reduce the computational cost of large-scale network simulations, through the development of event-driven simulation schemes. However, existing event-driven simulations schemes use extremely simplified neuronal models. Here, we implement and evaluate critically an event-driven algorithm (ED-LUT) that uses precalculated look-up tables to characterize synaptic and neuronal dynamics. This approach enables the use of more complex (and realistic) neuronal models or data in representing the neurons, while retaining the advantage of high-speed simulation. We demonstrate the method's application for neurons containing exponential synaptic conductances, thereby implementing shunting inhibition, a phenomenon that is critical to cellular computation. We also introduce an improved two-stage event-queue algorithm, which allows the simulations to scale efficiently to highly connected networks with arbitrary propagation delays. Finally, the scheme readily accommodates implementation of synaptic plasticity mechanisms that depend on spike timing, enabling future simulations to explore issues of long-term learning and adaptation in large-scale networks.

Short-term synaptic plasticity in the rat geniculo-cortical pathway during development in vivo

2006 ◽  
Vol 398 (1-2) ◽  
pp. 73-77 ◽  
Author(s):  
Fan Jia ◽  
Haiyang Wei ◽  
Xiangrui Li ◽  
Xiaoqiao Xie ◽  
Yifeng Zhou
Keyword(s):  
Synaptic Plasticity ◽  
Short Term

scholarly journals Synaptic Recruitment Enhances Gap Termination Responses in Auditory Cortex

2020 ◽  
Vol 30 (8) ◽  
pp. 4465-4480 ◽  
Author(s):  
Bshara Awwad ◽  
Maciej M Jankowski ◽  
Israel Nelken
Keyword(s):  
Auditory Cortex ◽  
Temporal Resolution ◽  
Perceptual Task ◽  
Gap Detection ◽  
Intracellular Recordings ◽  
Short Term ◽  
Mere Existence ◽  
Onset Response ◽  
Synaptic Mechanisms

Abstract The ability to detect short gaps in noise is an important tool for assessing the temporal resolution in the auditory cortex. However, the mere existence of responses to temporal gaps bounded by two short broadband markers is surprising, because of the expected short-term suppression that is prevalent in auditory cortex. Here, we used in-vivo intracellular recordings in anesthetized rats to dissect the synaptic mechanisms that underlie gap-related responses. When a gap is bounded by two short markers, a gap termination response was evoked by the onset of the second marker with minimal contribution from the offset of the first marker. Importantly, we show that the gap termination response was driven by a different (potentially partially overlapping) synaptic population than that underlying the onset response to the first marker. This recruitment of additional synaptic resources is a novel mechanism contributing to the important perceptual task of gap detection.

scholarly journals A Role for Short-Term Synaptic Facilitation and Depression in the Processing of Intensity Information in the Auditory Brain Stem

2007 ◽  
Vol 97 (4) ◽  
pp. 2863-2874 ◽  
Author(s):  
K. M. MacLeod ◽  
T. K. Horiuchi ◽  
C. E. Carr
Keyword(s):  
Synaptic Plasticity ◽  
Brain Stem ◽  
Time Constant ◽  
Cochlear Nucleus ◽  
Auditory Nerve ◽  
Brain Slices ◽  
Fast Time ◽  
Short Term ◽  
Intensity Information

The nature of the synaptic connection from the auditory nerve onto the cochlear nucleus neurons has a profound impact on how sound information is transmitted. Short-term synaptic plasticity, by dynamically modulating synaptic strength, filters information contained in the firing patterns. In the sound-localization circuits of the brain stem, the synapses of the timing pathway are characterized by strong short-term depression. We investigated the short-term synaptic plasticity of the inputs to the bird's cochlear nucleus angularis (NA), which encodes intensity information, by using chick embryonic brain slices and trains of electrical stimulation. These excitatory inputs expressed a mixture of short-term facilitation and depression, unlike those in the timing nuclei that only depressed. Facilitation and depression at NA synapses were balanced such that postsynaptic response amplitude was often maintained throughout the train at high firing rates (>100 Hz). The steady-state input rate relationship of the balanced synapses linearly conveyed rate information and therefore transmits intensity information encoded as a rate code in the nerve. A quantitative model of synaptic transmission could account for the plasticity by including facilitation of release (with a time constant of ∼40 ms), and a two-step recovery from depression (with one slow time constant of ∼8 s, and one fast time constant of ∼20 ms). A simulation using the model fit to NA synapses and auditory nerve spike trains from recordings in vivo confirmed that these synapses can convey intensity information contained in natural train inputs.

Unsupervised Formation of Vocalization-Sensitive Neurons: A Cortical Model Based on Short-Term and Homeostatic Plasticity

2012 ◽  
Vol 24 (10) ◽  
pp. 2579-2603 ◽  
Author(s):  
Tyler P. Lee ◽  
Dean V. Buonomano
Keyword(s):  
Synaptic Plasticity ◽  
Direction Selectivity ◽  
Homeostatic Plasticity ◽  
Cortical Model ◽  
Short Term ◽  
Temporal Asymmetry ◽  
Complex Stimuli ◽  
Spike Patterns

The discrimination of complex auditory stimuli relies on the spatiotemporal structure of spike patterns arriving in the cortex. While recordings from auditory areas reveal that many neurons are highly selective to specific spatiotemporal stimuli, the mechanisms underlying this selectivity are unknown. Using computer simulations, we show that selectivity can emerge in neurons in an entirely unsupervised manner. The model is based on recurrently connected spiking neurons and synapses that exhibit short-term synaptic plasticity. During a developmental stage, spoken digits were presented to the network; the only type of long-term plasticity present was a form of homeostatic synaptic plasticity. From an initially unresponsive state, training generated a high percentage of neurons that responded selectively to individual digits. Furthermore, units within the network exhibited a cardinal feature of vocalization-sensitive neurons in vivo: differential responses between forward and reverse stimulus presentations. Direction selectivity deteriorated significantly, however, if short-term synaptic plasticity was removed. These results establish that a simple form of homeostatic plasticity is capable of guiding recurrent networks into regimes in which complex stimuli can be discriminated. In addition, one computational function of short-term synaptic plasticity may be to provide an inherent temporal asymmetry, thus contributing to the characteristic forward-reverse selectivity.

scholarly journals Self-Tuning of Neural Circuits Through Short-Term Synaptic Plasticity

2007 ◽  
Vol 97 (6) ◽  
pp. 4079-4095 ◽  
Author(s):  
David Sussillo ◽  
Taro Toyoizumi ◽  
Wolfgang Maass
Keyword(s):  
Synaptic Plasticity ◽  
Activity Level ◽  
Cortical Circuits ◽  
Short Term ◽  
Cortical Networks ◽  
Short Term Plasticity ◽  
Numerous Experimental Data ◽  
Self Tuning ◽  
Synaptic Dynamics ◽  
Low Activity

Numerous experimental data show that cortical networks of neurons are not silent in the absence of external inputs, but rather maintain a low spontaneous firing activity. This aspect of cortical networks is likely to be important for their computational function, but is hard to reproduce in models of cortical circuits of neurons because the low-activity regime is inherently unstable. Here we show—through theoretical analysis and extensive computer simulations—that short-term synaptic plasticity endows models of cortical circuits with a remarkable stability in the low-activity regime. This short-term plasticity works as a homeostatic mechanism that stabilizes the overall activity level in spite of drastic changes in external inputs and internal circuit properties, while preserving reliable transient responses to signals. The contribution of synaptic dynamics to this stability can be predicted on the basis of general principles from control theory.

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