Spatiotemporal Pattern Of Synaptic Inputs Research Articles

Different responses of mice and rats hippocampus CA1 pyramidal neurons to in vitro and in vivo-like inputs.

The fundamental role of any neuron within a network is to transform complex spatiotemporal synaptic input patterns into individual output spikes. These spikes, in turn, act as inputs for other neurons in the network. Neurons must execute this function across a diverse range of physiological conditions, often based on species-specific traits. Therefore, it is crucial to determine the extent to which findings can be extrapolated between species and, ultimately, to humans. In this study, we employed a multidisciplinary approach to pinpoint the factors accounting for the observed electrophysiological differences between mice and rats, the two species most used in experimental and computational research. After analyzing the morphological properties of their hippocampal CA1 pyramidal cells, we conducted a statistical comparison of rat and mouse electrophysiological features in response to somatic current injections. This analysis aimed to uncover the parameters underlying these distinctions. Using a well-established computational workflow, we created ten distinct single-cell computational models of mouse CA1 pyramidal neurons, ready to be used in a full-scale hippocampal circuit. By comparing their responses to a variety of somatic and synaptic inputs with those of rat models, we generated experimentally testable hypotheses regarding species-specific differences in ion channel distribution, kinetics, and the electrophysiological mechanisms underlying their distinct responses to synaptic inputs during the behaviorally relevant Gamma and Sharp-Wave rhythms.

Synaptic Plasticity Is Predicted by Spatiotemporal Firing Rate Patterns and Robust to In Vivo-like Variability.

Synaptic plasticity, the experience-induced change in connections between neurons, underlies learning and memory in the brain. Most of our understanding of synaptic plasticity derives from in vitro experiments with precisely repeated stimulus patterns; however, neurons exhibit significant variability in vivo during repeated experiences. Further, the spatial pattern of synaptic inputs to the dendritic tree influences synaptic plasticity, yet is not considered in most synaptic plasticity rules. Here, we investigate how spatiotemporal synaptic input patterns produce plasticity with in vivo-like conditions using a data-driven computational model with a plasticity rule based on calcium dynamics. Using in vivo spike train recordings as inputs to different size clusters of spines, we show that plasticity is strongly robust to trial-to-trial variability of spike timing. In addition, we derive general synaptic plasticity rules describing how spatiotemporal patterns of synaptic inputs control the magnitude and direction of plasticity. Synapses that strongly potentiated have greater firing rates and calcium concentration later in the trial, whereas strongly depressing synapses have hiring firing rates early in the trial. The neighboring synaptic activity influences the direction and magnitude of synaptic plasticity, with small clusters of spines producing the greatest increase in synaptic strength. Together, our results reveal that calcium dynamics can unify diverse plasticity rules and reveal how spatiotemporal firing rate patterns control synaptic plasticity.

Global and Multiplexed Dendritic Computations under In Vivo-like Conditions

SummaryDendrites integrate inputs nonlinearly, but it is unclear how these nonlinearities contribute to the overall input-output transformation of single neurons. We developed statistically principled methods using a hierarchical cascade of linear-nonlinear subunits (hLN) to model the dynamically evolving somatic response of neurons receiving complex, in vivo-like spatiotemporal synaptic input patterns. We used the hLN to predict the somatic membrane potential of an in vivo-validated detailed biophysical model of a L2/3 pyramidal cell. Linear input integration with a single global dendritic nonlinearity achieved above 90% prediction accuracy. A novel hLN motif, input multiplexing into parallel processing channels, could improve predictions as much as conventionally used additional layers of local nonlinearities. We obtained similar results in two other cell types. This approach provides a data-driven characterization of a key component of cortical circuit computations: the input-output transformation of neurons during in vivo-like conditions.

The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow.

Every neuron is part of a network, exerting its function by transforming multiple spatiotemporal synaptic input patterns into a single spiking output. This function is specified by the particular shape and passive electrical properties of the neuronal membrane, and the composition and spatial distribution of ion channels across its processes. For a variety of physiological or pathological reasons, the intrinsic input/output function may change during a neuron’s lifetime. This process results in high variability in the peak specific conductance of ion channels in individual neurons. The mechanisms responsible for this variability are not well understood, although there are clear indications from experiments and modeling that degeneracy and correlation among multiple channels may be involved. Here, we studied this issue in biophysical models of hippocampal CA1 pyramidal neurons and interneurons. Using a unified data-driven simulation workflow and starting from a set of experimental recordings and morphological reconstructions obtained from rats, we built and analyzed several ensembles of morphologically and biophysically accurate single cell models with intrinsic electrophysiological properties consistent with experimental findings. The results suggest that the set of conductances expressed in any given hippocampal neuron may be considered as belonging to two groups: one subset is responsible for the major characteristics of the firing behavior in each population and the other is responsible for a robust degeneracy. Analysis of the model neurons suggests several experimentally testable predictions related to the combination and relative proportion of the different conductances that should be expressed on the membrane of different types of neurons for them to fulfill their role in the hippocampus circuitry.

Biophysics of object segmentation in a collision-detecting neuron.

Collision avoidance is critical for survival, including in humans, and many species possess visual neurons exquisitely sensitive to objects approaching on a collision course. Here, we demonstrate that a collision-detecting neuron can detect the spatial coherence of a simulated impending object, thereby carrying out a computation akin to object segmentation critical for proper escape behavior. At the cellular level, object segmentation relies on a precise selection of the spatiotemporal pattern of synaptic inputs by dendritic membrane potential-activated channels. One channel type linked to dendritic computations in many neural systems, the hyperpolarization-activated cation channel, HCN, plays a central role in this computation. Pharmacological block of HCN channels abolishes the neuron's spatial selectivity and impairs the generation of visually guided escape behaviors, making it directly relevant to survival. Additionally, our results suggest that the interaction of HCN and inactivating K+ channels within active dendrites produces neuronal and behavioral object specificity by discriminating between complex spatiotemporal synaptic activation patterns.

Synaptic input patterns triggering local dendritic spikes in vivo

Theoretical modelling and experiments in vitro have shown that the computations performed by single neurons critically depend on the spatiotemporal patterns of synaptic input to the neuron [1,2]. While recording the spike output of neuronal populations in vivo is now routinely possible, the spatiotemporal pattern of synaptic inputs to an entire neuron is still largely inaccessible to experiment. However, recent experiments in which sensory-evoked local dendritic spikes were observed in vivo have provided valuable constraints on possible spatiotemporal patterns of synaptic input [3-5]. For example, the synaptic input to layer 2/3 pyramidal neurons in mouse primary visual cortex evokes dendritic spikes at high frequencies during visual stimulation at the preferred orientation, but not at non-preferred orientations [3]. Here, we use a biophysical model to explore which spatiotemporal patterns of synaptic inputs can drive the observed spikes, and in particular the high frequencies in dendritic spike bursts. First, we adapted a detailed active compartmental model of a neocortical layer 2/3 pyramidal neuron [3] to reproduce biophysical properties and firing statistics observed in vivo [6], such as firing threshold, mean membrane potentials for UP and DOWN states, and sparse action potential firing in the presence of Poisson distributed background synaptic input. The model generates fast dendritic spikes heterogeneous in amplitude, time course and spatial extent, as observed in in vivo experiments [3]. The dendritic spikes, together with somatic action potential firing, are abolished by blocking the NMDA receptor-mediated conductance, and their frequency is reduced by hyperpolarization via the dendritic recording site, again as observed in experiments [3]. Next we analyzed which spatiotemporal synaptic input patterns precede the generation of local dendritic events. We find that local spikes are preferentially triggered by excitatory synapses which are spatially and temporally clustered in the local dendritic branch and neighbouring branches. While there is great variability in the spatial distribution of synapses triggering local dendritic spikes, temporal patterns of excitatory synaptic input are stereotyped and tend to be sparse. High-frequency dendritic spikes similar to those observed in vivo occur preferentially when excitatory synaptic inputs are interspersed with inhibitory synaptic inputs. Furthermore, local spikes preceding backpropagating action potentials lead to the highest instantaneous dendritic spike frequencies. The highest sustained frequencies are generated by local spikes initiated sequentially in several neighbouring dendritic branches. Notably, only some of the high frequency dendritic events are effective in evoking action potential output, while others remain local. We use cluster analysis to establish the local and global conditions under which specific spatiotemporal patterns of synaptic input can influence neuronal output. In summary, we have derived spatial and temporal rules for synaptic input and identify input patterns that locally exploit the non-linear integration capacities of dendrites. Which computations can be implemented by spiking dendrites receiving such synaptic input patterns? These constraints on synaptic input patterns, together with the realistic intrinsic properties displayed by the model, put us in a position to investigate the role of dendritic excitability in shaping the input-output relation of the neuron.

Robustness of sensory-evoked excitation is increased by inhibitory inputs to distal apical tuft dendrites

Cortical inhibitory interneurons (INs) are subdivided into a variety of morphologically and functionally specialized cell types. How the respective specific properties translate into mechanisms that regulate sensory-evoked responses of pyramidal neurons (PNs) remains unknown. Here, we investigated how INs located in cortical layer 1 (L1) of rat barrel cortex affect whisker-evoked responses of L2 PNs. To do so we combined in vivo electrophysiology and morphological reconstructions with computational modeling. We show that whisker-evoked membrane depolarization in L2 PNs arises from highly specialized spatiotemporal synaptic input patterns. Temporally L1 INs and L2-5 PNs provide near synchronous synaptic input. Spatially synaptic contacts from L1 INs target distal apical tuft dendrites, whereas PNs primarily innervate basal and proximal apical dendrites. Simulations of such constrained synaptic input patterns predicted that inactivation of L1 INs increases trial-to-trial variability of whisker-evoked responses in L2 PNs. The in silico predictions were confirmed in vivo by L1-specific pharmacological manipulations. We present a mechanism-consistent with the theory of distal dendritic shunting-that can regulate the robustness of sensory-evoked responses in PNs without affecting response amplitude or latency.

Reading out a spatiotemporal population code by imaging neighbouring parallel fibre axons in vivo

The spatiotemporal pattern of synaptic inputs to the dendritic tree is crucial for synaptic integration and plasticity. However, it is not known if input patterns driven by sensory stimuli are structured or random. Here we investigate the spatial patterning of synaptic inputs by directly monitoring presynaptic activity in the intact mouse brain on the micron scale. Using in vivo calcium imaging of multiple neighbouring cerebellar parallel fibre axons, we find evidence for clustered patterns of axonal activity during sensory processing. The clustered parallel fibre input we observe is ideally suited for driving dendritic spikes, postsynaptic calcium signalling, and synaptic plasticity in downstream Purkinje cells, and is thus likely to be a major feature of cerebellar function during sensory processing.

A continuum approach to model neurites/dendrites with emerging subtrees

Neuronal morphology plays an important role in the processing of spatiotemporal synaptic input patterns. We formulate a new cable theoretic model to study the output in a dendritic cable with many subtrees, both analytically and computationally. The model is an extension of the continuum spine model derived by Baer and Rinzel [1] and like that model, there is no direct interaction between branches. Instead, all interactions occur through the primary dendrite. The continuum branch formulation provides a link between analytical and computational approaches and allows for detailed models of structural plasticity with the advantage that parameters like branch density can be varied dynamically. This is in contrast to multicompartment modeling approaches where different models, each having a static morphology, must be used. A multicompartment model with equally spaced discrete branches implemented in NEURON [2] is used to validate the continuum branch model. Then we demonstrate the approach using Drosophila Motoneuron 5 (MN5), a monopolar neuron, where soma and axon are connected via a long primary neurite from which dendritic subtrees emerge. Parameters for the model are based on 3D reconstructions [3] of that neuron and the subtrees are reduced to uniform cables. For a passive membrane and uniformly distributed branches, this formulation provides analytic estimates of electrical parameters, such as the input resistance. Transforming the equations to the frequency domain, we examine the steady state response properties for periodic forcing, where the impact of structural properties on transfer properties and the phase shift at different locations are calculated directly. Based on calculations of the voltage response along the primary neurite, the continuum formulation performs much better than a simple cable as an approximation of the morphologically realistic model. Simulations of the continuum model are in good agreement with the realistic model for frequencies up to 1000 Hz. In insects neurons the spike initiation zone (SIZ) is usually located far away from the recording site. We use this model to investigate the impact of the location of the SIZ and morphology on spike initiation and spike shape at the recording site as well as on activation and inactivation curves of ion channels obtained by voltage clamp recordings. Also we investigate how structural properties impact the results found in a point model of MN5, where variations of ion channel densities in physiological ranges lead to different observed firing properties [4].

Now single spines: monitoring neuronal membrane potential with submicron and submillisecond resolution

Since the spectacular and mysterious morphology of neurons was visualized by Cajal, scientists have wondered what happens when an action potential winds through the trajectory of the dendritic tree and reaches fine branches and synaptic spines. However, until recently we have not had a way to measure the time course, size and shape of a back-propagating action potential through a neuron's fine branches. Such capability is important to our understanding of how information is processed in the dendritic tree and stored at the synapses. Pioneered by Antic & Zecevic (1995), the ‘inside dye’ technique allows us to monitor membrane potential from hundred of locations on a single neuron. The method is to infuse a voltage-sensitive dye (VSD) into a neuron with a patch pipette, allowing the dye to bind to the cellular membrane from inside, and to measure the dye signals that reflect the transmembrane potentials at different locations on the neuron. Voltage-sensitive dyes work differently from calcium dyes; they have to bind to the cellular membrane in order to measure the voltage across the membrane. Any unbound intracellular dye only adds noise. Thus the signal-to-noise ratio increases in finer branches, because the ratio of membrane area to intracellular volume increases. This method works better in fine branches than in the soma, which is the opposite of the patch-electrode techniques, which work better on larger structures. In a recent issue of The Journal of Physiology, Holthoff et al. (2010) have demonstrated improved methods with greatly increased signal-to-noise ratio. Terminal dendritic branches and individual synaptic spines can be monitored with a sensitivity that was not imagined by the original founders of voltage imaging. As described in Holthoff et al. (2010), the magic is achieved with the help of lasers. At fine neuronal branches the low density of photons limits the signal-to-noise ratio (S/N). The illumination intensity from an arc lamp is limited mainly by factors related to focusability of the emitted light. Solid state lasers, on the other hand, are, in biology, a practically limitless source of intensity. A second advantage of a laser source is the ability to use the wavelength where the dye has the largest fractional fluorescence change. As the report shows, the overall effect of using a near optimal wavelength at high intensity resulted in a S/N improvement by a factor of 10–40. This improvement resulted in a method with sub-micrometer and sub-millisecond resolution (Fig. 1). Figure 1 Locations now accessible for membrane potential measurements What's next? Clearly, this new tool will enable neuroscientists to monitor electrical signals from axons, axon collaterals and axon terminals, as well as from terminal dendrites and individual dendritic spines, all important but tiny parts of neurons that are difficult to probe for electrical signals with any other measurement technique. There are a number of unresolved questions related to the physiology and pathology of these structures that can now be explored by direct measurement. One prominent example is the unresolved electrical role of dendritic spines in neuronal signal processing. Another area of research that will be greatly facilitated by high-sensitivity voltage imaging is the characterization of the currently unknown natural input to a dendritic tree (the spatio-temporal pattern of synaptic inputs). This information is critical for understanding the input–output function of any neuron. More ‘natural’ experiments should also be possible. Thus far, the action potentials in imaging experiments were evoked by some form of artificial stimulation of a neuron. It would be far more interesting to monitor action potentials generated naturally by the network. In a brain slice there are a variety of network events such as up–down states, theta oscillations and gamma oscillations. In these events it would be important to see if the shape of action potentials changes as well as where each action potential is initiated. In addition, most of the inside dye studies label and measure one neuron at a time. Can one inject a number of electrophysiologically identified pyramidal and fast-spiking neurons and watch their interactions during gamma oscillations? It would be possible to directly test the hypothesis that when oscillation frequency increases, the activations of pyramidal and GABAergic interneurons become more in phase (Mann & Mody, 2010). Finally, when the method is used in vivo, with the help of genetically encoded sensors and two-photon microscopy, one should be able to watch spike timing-dependent plasticity (Markram et al. 1997) at fine branches in which the temporal relationships can be largely different from location to location in the same neuronal pair. Several strategies for improving the fractional fluorescence change are under investigation. For example, the signal would be increased if unbound dye is quenched. Or a FRET mechanism or protein sensors may generate larger signals (Bradley et al. 2009). Thus the remarkable achievement of Holthoff et al. may not be the end of the line.

The role of motor neuron intrinsic properties in leech heartbeat fictive motor pattern generation

Event Abstract Back to Event The role of motor neuron intrinsic properties in leech heartbeat fictive motor pattern generation Terrence M. Wright1* and Ronald L. Calabrese1 1 Emory University, Biology Dep, United States The movement of blood through the circulatory system of the leech is driven by the bilaterally asymmetric constriction of two lateral heart tubes. One (the peristaltic heart) constricts with a rear to front progression while the other (the synchronous heart) constricts nearly synchronously along its length. The leech heartbeat central pattern generator (CPG) drives these constriction patterns by generating a bilaterally asymmetric synaptic input pattern to heart motor neurons (HE) which innervate these heart tubes in a segment-specific manner. Previous work in our lab developed a canonical model of the ensemble of motor neurons (heart motor neurons, HEs) responsible for the output of the leech heartbeat CPG. We then introduced the spatiotemporal pattern of synaptic inputs to this model that we derived from the living system. While the model captures the gross features of the living system, the phasing of the activity pattern of the model motor neurons with respect to their inputs was substantially different from that found in the living system. Because the input pattern to the model came from the living system, we hypothesize that the discrepancy between the model and the living system may be due to the intrinsic properties of the motor neurons in the living system. We attempt to address this hypothesis using the dynamic clamp technique. We introduce segment-specific patterns of inhibitory input to motor neurons and ask the question: what is the phasing acquired by the motor neurons in response to this input? We compared the phasing observed in the hybrid system to that observed in the living system and the model. We show that, in the hybrid system, motor neurons receiving the synchronous input pattern acquire phasing more consistent with the living system, and different from the model, while motor neurons receiving the peristaltic input pattern acquire phasing more like the model, yet show burst onsets and offsets consistent with the living system and different from the model. These results suggest that the intrinsic properties of the heart motor neurons contribute to the heartbeat fictive motor pattern, though for the peristaltic mode the impact is unclear. Conference: 2010 South East Nerve Net (SENN) and Georgia/South Carolina Neuroscience Consortium (GASCNC) conferences, Atlanta , United States, 5 Mar - 7 Mar, 2010. Presentation Type: Poster Presentation Topic: Posters Citation: Wright TM and Calabrese RL (2010). The role of motor neuron intrinsic properties in leech heartbeat fictive motor pattern generation. Front. Neurosci. Conference Abstract: 2010 South East Nerve Net (SENN) and Georgia/South Carolina Neuroscience Consortium (GASCNC) conferences. doi: 10.3389/conf.fnins.2010.04.00090 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 18 Mar 2010; Published Online: 18 Mar 2010. * Correspondence: Terrence M Wright, Emory University, Biology Dep, Atlanta, United States, twrigh6@emory.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Terrence M Wright Ronald L Calabrese Google Terrence M Wright Ronald L Calabrese Google Scholar Terrence M Wright Ronald L Calabrese PubMed Terrence M Wright Ronald L Calabrese Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Using a Model to Assess the Role of the Spatiotemporal Pattern of Inhibitory Input and Intrasegmental Electrical Coupling in the Intersegmental and Side-to-Side Coordination of Motor Neurons by the Leech Heartbeat Central Pattern Generator

Previously we presented a quantitative description of the spatiotemporal pattern of inhibitory synaptic input from the heartbeat central pattern generator (CPG) to segmental motor neurons that drive heartbeat in the medicinal leech and the resultant coordination of CPG interneurons and motor neurons. To begin elucidating the mechanisms of coordination, we explore intersegmental and side-to-side coordination in an ensemble model of all heart motor neurons and their known synaptic inputs and electrical coupling. Model motor neuron intrinsic properties were kept simple, enabling us to determine the extent to which input and electrical coupling acting together can account for observed coordination in the living system in the absence of a substantive contribution from the motor neurons themselves. The living system produces an asymmetric motor pattern: motor neurons on one side fire nearly in synchrony (synchronous), whereas on the other they fire in a rear-to-front progression (peristaltic). The model reproduces the general trends of intersegmental and side-to-side phase relations among motor neurons, but the match with the living system is not quantitatively accurate. Thus realistic (experimentally determined) inputs do not produce similarly realistic output in our model, suggesting that motor neuron intrinsic properties may contribute to their coordination. By varying parameters that determine electrical coupling, conduction delays, intraburst synaptic plasticity, and motor neuron excitability, we show that the most important determinant of intersegmental and side-to-side phase relations in the model was the spatiotemporal pattern of synaptic inputs, although phasing was influenced significantly by electrical coupling.

Modeling the output of a central pattern generator

Experimental analysis in our lab has provided a quantitative description of the spatiotemporal pattern of inhibitory synaptic input from the heartbeat central pattern generator (CPG) to segmental motor neurons that drive heartbeat in the medicinal leech. To begin the process of elucidating the relative roles of this pattern of input and motor neuron intrinsic properties and electrical coupling in the elaboration of the heartbeat fictive motor pattern, we constructed a conductance-based ensemble model of all the segmental heart motor neurons and their known synaptic inputs. Our focus was intersegmental and side-to-side coordination of the asymmetric motor pattern: motor neurons on one side fire nearly in synchrony (synchronous coordination), while on the other they fire in a rear-to-front progression (peristaltic coordination). The model reproduces the general trends of the two intersegmental phase relations among motor neurons, but the match with the living system is quantitatively poor, particularly for the peristaltic coordination mode where the phase progression among the segmental motor neurons in the model is only half that observed in the living system. Thus the realistic inputs (experimentally determined) do not produce similarly realistic output in our model. Modeling experiments, indicate that the most important determinant of the intersegmental and side-to-side phase relations among the heart motor neurons in the model was the spatiotemporal pattern of synaptic inputs, yet phasing was influenced by electrical coupling between the motor neurons in each segment, intersegmental conduction delays in the premotor interneurons, intra-burst synaptic plasticity, and intrinsic membrane currents of the motor neurons. Understanding the shortcomings of the model required that we establish experimentally the precise timing of motor neuron activity in each segment with respect to CPG activity. This analysis show quantitatively how motor neurons in the model fail to fire at the appropriate time with respect to their synaptic inputs and suggest that the intrinsic properties of the model motor neuron are simplistic.