Figure 4 - uploaded by Randy M Bruno
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Complete synaptic distribution of an L4 spiny stellate neuron. Shown is a highresolution 3D reconstruction of a spiny stellate neuron illustrating individual spines apposed to synaptophysin-EGFP signal, denoted as TC (filled green circles) and unapposed spines assumed to be CC (empty red circles). This cell corresponds to cell 3 in Figure 6A.
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Thalamus is a potent driver of cortical activity even though cortical synapses onto excitatory layer 4 neurons outnumber thalamic synapses 10 to 1. Previous in vitro studies have proposed that thalamocortical (TC) synapses are stronger than corticocortical (CC) synapses. Here, we investigated possible anatomical and physiological differences betwee...
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... reconstructed the complete dendritic arbors, colabeled with synaptophysin-EGFP or VGluT2, of six spiny L4 neurons in so- matosensory cortex (Fig. 4), scoring each spine as TC or CC (n 17863 spines). Neurons were randomly sampled with regard to location within the barrel (Fig. 5). Our sample contains three spiny stellate neurons and three star pyramid neurons, including one in which the soma was located in the septum between neigh- boring barrels (cell 1). We included this septal ...
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... First, the amplitudes of postsynaptic potentials (PSPs) are determined (Table 3). EPSPs of intracortical connections are firstly set to 0.5 mV, which is consistent with the range of in vivo recordings known to us (Jouhanneau et al., 2015(Jouhanneau et al., , 2018Pala and Petersen, 2015;Schoonover et al., 2014), and IPSPs are set 5 . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. ...
... EPSPs of the thalamic input follow the values of thalamocortical connections in vivo reported by Bruno and Sakmann (2006) (Table 4), with the exception of SOM cells, for which we use 50% of the others to better reflect the reported weaker thalamocortical connections in this group of neurons (Ji et al., 2016). The intracortical EPSPs and IPSPs and thalamic EPSPs are log-normally distributed in the model to be consistent with data from in vivo (Jouhanneau et al., 2015(Jouhanneau et al., , 2018Pala and Petersen, 2015;Schoonover et al., 2014) and in vitro (Song et al., 2005) experiments. For intracortical EPSPs and IPSPs, the standard deviations are set to the same magnitude as the means (e.g., 0.5±0.5 mV), which is also consistent with the in vivo data (Jouhanneau et al., 2015(Jouhanneau et al., , 2018Pala and Petersen, 2015;Schoonover et al., 2014), where the standard deviations are 62 to 172% of the magnitude of the means. ...
... The intracortical EPSPs and IPSPs and thalamic EPSPs are log-normally distributed in the model to be consistent with data from in vivo (Jouhanneau et al., 2015(Jouhanneau et al., , 2018Pala and Petersen, 2015;Schoonover et al., 2014) and in vitro (Song et al., 2005) experiments. For intracortical EPSPs and IPSPs, the standard deviations are set to the same magnitude as the means (e.g., 0.5±0.5 mV), which is also consistent with the in vivo data (Jouhanneau et al., 2015(Jouhanneau et al., , 2018Pala and Petersen, 2015;Schoonover et al., 2014), where the standard deviations are 62 to 172% of the magnitude of the means. EPSP amplitudes of the background inputs are fixed to a value of 0.5 mV. ...
Three major types of GABAergic interneurons, the parvalbumin- (PV), somatostatin- (SOM) and vasoactive intestinal peptide-expressing (VIP) cells, play critical but distinct roles in the cortical microcircuitry. Their inhibitory functions are shaped by their specific electrophysiology and connectivity. To study how this diversity contributes to the dynamics and signal processing in the cerebral cortex, we developed a multi-layer model incorporating biologically realistic parameters of these interneurons from rodent somatosensory cortex. With in vivo data as criteria, the model shows plausible resting-state activity and responses to sensory stimulation. With a protocol of cell-type-specific stimulation, network responses when activating different neuron types are examined. The model reproduces the experimentally observed inhibitory effects of PV and SOM cells and disinhibitory effect of VIP cells on excitatory cells. In addition, responses of VIP cells to cell-type-specific stimulation provide predictions for future experiments. We further create a version of the model incorporating cell-type-specific short-term synaptic plasticity (STP). While the ongoing activity with and without STP is similar, STP modulates the responses of SOM and VIP cells to cell-type-specific stimulation, presumably by changing the dominant inhibitory pathways. Our model can serve to explore the computational roles of inhibitory interneurons and short-term synaptic plasticity in sensory functions.
... Furthermore, even the largest EPSP amplitudes provide only a fraction of the depolarizing charge necessary to drive the membrane potential of a cortical neuron through the spike threshold. Thus, temporal coincidence in presynaptic spike trains must necessarily be an important factor for information coding in neocortex [7,[16][17][18][19]. Finally, neurons in vivo operate in the presence of continuous bombardment with synaptic background activity. ...
... [12,13,17,[28][29][30], and the temporal structure within synaptic inputs, e.g. [16][17][18][19], it remains much less studied how these parameters interact to shape information transfer in sensory areas. ...
... We found that synaptic background activity carried through the weak synapses contributed critically to information transfer of strong inputs by depolarizing V m and through a stochastic resonance-type effect [54]: while being incapable of evoking spiking by itself, the weak inputs enabled the model neuron to operate in a regime in which the cell became sensitive and responsive to coincident strong inputs [17,43,[55][56][57][58][59]. Even then, the high firing rates and the synchronous activity of multiple strong synapses were needed to evoke spiking in the model neuron [16][17][18][19]60]. Notably, synaptic strength alone did not determine which presynaptic cells could evoke spikes [7]. ...
Neurons integrate from thousands of synapses whose strengths span an order of magnitude. Intriguingly, in mouse neocortex, the few 'strong' synapses are formed between similarly tuned cells, suggesting they determine spiking output. This raises the question of how other computational primitives, including 'background' activity from the many 'weak' synapses, short-term plasticity, and temporal factors contribute to spiking. We used paired recordings and extracellular stimulation experiments to map excitatory postsynaptic potential (EPSP) amplitudes and paired-pulse ratios of synaptic connections formed between pyramidal neurons in layer 2/3 (L2/3) of barrel cortex. While net short-term plasticity was weak, strong synaptic connections were exclusively depressing. Importantly, we found no evidence for clustering of synaptic properties on individual neurons. Instead, EPSPs and paired-pulse ratios of connections converging onto the same cells spanned the full range observed across L2/3, which critically constrains theoretical models of cortical filtering. To investigate how different computational primitives of synaptic information processing interact to shape spiking, we developed a computational model of a pyramidal neuron in the excitatory L2/3 circuitry, which was constrained by our experiments and published in vivo data. We found that strong synapses were substantially depressed during ongoing activation and their ability to evoke correlated spiking primarily depended on their high temporal synchrony and high firing rates observed in vivo. However, despite this depression, their larger EPSP amplitudes strongly amplified information transfer and responsiveness. Thus, our results contribute to a nuanced framework of how cortical neurons exploit synergies between temporal coding, synaptic properties, and noise to transform synaptic inputs into spikes.
... This direct thalamic drive onto neocortical L2/3 is becoming increasingly recognized as relevant to sensory processing 13,14 , yet remains largely unexplored. In L4 of somatosensory cortex, the primary neocortical recipient layer for thalamic innervation, thalamocortical (TC) synapses account for 5-15% of synapses onto spiny neurons [15][16][17][18][19] , with comparable density two-dimensional EM cannot provide a map of all thalamic inputs onto a given cortical neuron, and efforts to combine EM with genetic labels that can classify cell and synapse type have not yet been successful. To date, there has been no anatomical characterization of where thalamic inputs map onto the dendritic arbors of individual cortical pyramidal neurons, or distinction of which thalamic nuclei give rise to these projections. ...
... We do however show that both TC and CC synapses are more clustered among themselves than expected from a random distribution (see also refs. 18,38,39 ). The average nearest-neighbor distance for TC synapses is 9.55 µm as compared to 13.25 µm in the respective random case, and 2.34 µm for CC synapses as compared to 2.48 µm in the respective random case. ...
The thalamus is the main gateway for sensory information from the periphery to the mammalian cerebral cortex. A major conundrum has been the discrepancy between the thalamus’s central role as the primary feedforward projection system into the neocortex and the sparseness of thalamocortical synapses. Here we use new methods, combining genetic tools and scalable tissue expansion microscopy for whole-cell synaptic mapping, revealing the number, density and size of thalamic versus cortical excitatory synapses onto individual layer 2/3 (L2/3) pyramidal cells (PCs) of the mouse primary visual cortex. We find that thalamic inputs are not only sparse, but remarkably heterogeneous in number and density across individual dendrites and neurons. Most surprising, despite their sparseness, thalamic synapses onto L2/3 PCs are smaller than their cortical counterparts. Incorporating these findings into fine-scale, anatomically faithful biophysical models of L2/3 PCs reveals how individual neurons with sparse and weak thalamocortical synapses, embedded in small heterogeneous neuronal ensembles, may reliably ‘read out’ visually driven thalamic input.
... Cerebellar signals participate in shaping motor cortical commands and play a specific role in regulation of motor timing (Holmes, 1917(Holmes, , 1939Ivry and Keele, 1989;Nashef et al., 2018aNashef et al., , 2019. This means that, despite the large distance, cerebellar information needs to travel, and the limited number of synapses onto motor cortical neurons (Bopp et al., 2017;Schoonover et al., 2014) this pathway exerts online affects motor output (Nashef et al., 2021). In sensory (Cruikshank et al., 2007;Gabernet et al., 2005) and frontal (Delevich et al., 2015) cortical areas of rodents, thalamocortical inputs produce feedforward inhibition (FFI), which dictates a temporal window for information processing (Pouille and Scanziani, 2001). ...
Voluntary movements are driven by coordinated activity across a large population of motor cortical neurons. Formation of this activity is controlled by local interactions and long-range inputs. How remote areas of the brain communicate with motor cortical neurons to effectively drive movement remains unclear. We address this question by studying the cerebellar-thalamocortical system. We find that thalamic input to the motor cortex triggers feedforward inhibition by contacting inhibitory cells via highly effective GluR2-lacking AMPA receptors and that, during task performance, the activity of parvalbumin (PV) and pyramidal cells exhibits relations comparable with movement parameters. We also find that the movement-related activity of PV interneurons precedes firing of pyramidal cells. This counterintuitive sequence of events, where inhibitory cells are recruited more strongly and before excitatory cells, may amplify the cortical effect of cerebellar signals in a way that exceeds their sheer synaptic efficacy by suppressing other inputs.
... Note that the derivation of Eq. (22) assumes only a to be small and does not depend on the scaling relation between a and K. On the other hand, the fact that CV K * increases linearly with a makes the state emerging in networks of conductance-based neurons with a ~ 1/ log(K) significantly more robust to connection fluctuations than that emerging with a ~ K −α , for which CV K * K −α , and with current-based neurons, where CV K * 1/ K [56]. Note that, while in randomly connected networks CV K 1/ K, a larger degree of heterogeneity is observed in cortical networks [50,[56][57][58][59][60][61][62]. Our results show that networks of conductancebased neurons could potentially be much more robust to such heterogeneities than networks of current-based neurons. ...
... However, in networks with structural heterogeneity, with connection heterogeneity larger than 1/ K, the variability in mean input currents produces significant departures from the asynchronous irregular state, with large fractions of neurons that become silent or fire regularly [56]. This problem is relevant in cortical networks [56], where significant heterogeneity of in-degrees has been reported [50,[57][58][59][60][61][62], and different mechanisms have been proposed to solve it [56]. Here, we showed that networks of conductance-based neurons also generate irregular activity without any need for fine-tuning and, furthermore, can support irregular activity with substantial structural heterogeneity, up to the order of 1/ log(K). ...
Cortical neurons are characterized by irregular firing and a broad distribution of rates. The balanced state model explains these observations with a cancellation of mean excitatory and inhibitory currents, which makes fluctuations drive firing. In networks of neurons with current-based synapses, the balanced state emerges dynamically if coupling is strong, i.e., if the mean number of synapses per neuron K is large and synaptic efficacy is of the order of 1/sqrt[K]. When synapses are conductance-based, current fluctuations are suppressed when coupling is strong, questioning the applicability of the balanced state idea to biological neural networks. We analyze networks of strongly coupled conductance-based neurons and show that asynchronous irregular activity and broad distributions of rates emerge if synaptic efficacy is of the order of 1/log(K). In such networks, unlike in the standard balanced state model, current fluctuations are small and firing is maintained by a drift-diffusion balance. This balance emerges dynamically, without fine-tuning, if inputs are smaller than a critical value, which depends on synaptic time constants and coupling strength, and is significantly more robust to connection heterogeneities than the classical balanced state model. Our analysis makes experimentally testable predictions of how the network response properties should evolve as input increases.
... We note, however, that the existing experimental data in the mouse and ferret visual cortex do not provide information about the relationship between synapse proximity and location on basal versus apical dendrites. A similar synaptic organization can be observed in the mouse barrel cortex, where proximal (distal) synapses tend to respond (not respond) to stimulation of the primary whisker of the corresponding barrel (Jia et al., 2014;Schoonover et al., 2014), and in the hippocampus, where individual branches respond to specific locations in space (Rashid et al., 2020). We term this type of global organization dendritic maps (Kirchner and Gjorgjieva, 2021) to highlight the similarity with cortical maps (White and Fitzpatrick, 2007). ...
Single neurons in the brain exhibit astounding computational capabilities, which gradually emerge throughout development and enable them to become integrated into complex neural circuits. These capabilities derive in part from the precise arrangement of synaptic inputs on the neurons’ dendrites. While the full computational benefits of this arrangement are still unknown, a picture emerges in which synapses organize according to their functional properties across multiple spatial scales. In particular, on the local scale (tens of microns), excitatory synaptic inputs tend to form clusters according to their functional similarity, whereas on the scale of individual dendrites or the entire tree, synaptic inputs exhibit dendritic maps where excitatory synapse function varies smoothly with location on the tree. The development of this organization is supported by inhibitory synapses, which are carefully interleaved with excitatory synapses and can flexibly modulate activity and plasticity of excitatory synapses. Here, we summarize recent experimental and theoretical research on the developmental emergence of this synaptic organization and its impact on neural computations.
... Furthermore, even the largest EPSP amplitudes provide only a small fraction of the depolarizing charge necessary to drive the membrane potential of a cortical neuron through the spike threshold. Thus, temporal coincidence in presynaptic spike trains must necessarily be an important factor for information coding in neocortex (Bruno and Sakmann, 2006;Banitt et al., 2007;Wang et al., 2010;Schoonover et al., 2014;Scholl et al., 2020). Finally, neurons in vivo operate in the presence of significant synaptic background activity. ...
... (Abbott et al., 1997;Castro-Alamancos and Oldford, 2002;Chung et al., 2002;Banitt et al., 2007;Rothman et al., 2009;Díaz-Quesada et al., 2014)], and the temporal structure within synaptic inputs [e.g. (Bruno and Sakmann, 2006;Banitt et al., 2007;Wang et al., 2010;Schoonover et al., 2014)], it remains much less studied how these parameters act together to shape information transfer in sensory areas. ...
... We found that synaptic background activity carried through the weak synapses contributed critically to information transfer of strong inputs through a stochastic resonance-type effect (Faisal et al., 2008): while being incapable of evoking spiking by itself, the weak inputs enabled the model neuron to operate in a regime in which the cell became sensitive and responsive to coincident strong inputs (Bulsara et al., 1991;Hô and Destexhe, 2000;Chapeau-Blondeau and Rousseau, 2002;London et al., 2002;McDonnell and Abbott, 2009;Durand et al., 2013). Even then, the high firing rates and the synchronous activity of multiple strong synapses were needed to evoke spiking in the model neuron (Bruno and Sakmann, 2006;Banitt et al., 2007;Wang et al., 2010;Schoonover et al., 2014;Martin and Schröder, 2016). Notably, synaptic strength alone did not determine which presynaptic cells could evoke spikes (Scholl et al., 2020). ...
Neurons integrate from thousands of synapses whose strengths span an order of magnitude. Intriguingly, in mouse neocortex, the few "strong" synapses are formed between similarly tuned cells, suggesting they determine neuronal spiking output. This raises the question of how other computational primitives, including "background" activity from the many "weak" synapses, short-term plasticity, and temporal factors contribute to spiking. We combined extracellular stimulation and whole-cell recordings in mouse barrel cortex to map excitatory postsynaptic potential (EPSP) amplitudes and paired-pulse ratios of excitatory synaptic connections converging onto individual layer 2/3 (L2/3) neurons. While net short-term plasticity was weak, connections with EPSPs > 2 mV were exclusively depressing. There was no evidence for clustering of synaptic properties on individual neurons. Instead, EPSPs and paired-pulse ratios of connections converging onto the same cells spanned the full range observed across L2/3, which critically constrains theoretical models of cortical filtering. To investigate how different computational primitives of synaptic information processing interact to shape spiking, we developed a computational model of a pyramidal neuron in the rodent L2/3 circuitry, which was constrained by our own experiments and published in vivo data. We found that the ability of strong inputs to evoke spiking depended on their high temporal synchrony and high firing rates observed in vivo and on synaptic background activity - and not primarily on synaptic strength, which further amplified information transfer. Our results provide a framework of how cortical neurons exploit complex synergies between temporal coding, synaptic properties, and noise to transform synaptic inputs into output firing.
... Various light microscopic imaging techniques have been developed for the sake of circuit reconstruction, each with its pros and cons (Kim et al., 2012;Wickersham and Feinberg, 2012;Schoonover et al., 2014;Chen et al., 2015). One promising method with which to enhance synapse detection accuracy using conventional FM is array tomography (AT), which is an imaging technique based on iterative FM on a serially sectioned volume of tissue followed by computational reconstruction (Micheva and Smith, 2007). ...
Critical determinants of synaptic functions include subcellular locations, input sources, and specific molecular characteristics. However, there is not yet a reliable and efficient method that can detect synapses. Electron microscopy is a gold-standard method to detect synapses due to its exceedingly high spatial resolution. However, it requires laborious and time-consuming sample preparation and lengthy imaging time with limited labeling methods. Recent advances in various fluorescence microscopy methods have highlighted fluorescence microscopy as a substitute for electron microscopy in reliable synapse detection in a large volume of neural circuits. In particular, array tomography has been verified as a useful tool for neural circuit reconstruction. To further improve array tomography, we developed a novel imaging method, called “structured illumination microscopy on the putative region of interest on ultrathin sections”, which enables efficient and accurate detection of synapses-of-interest. Briefly, based on low-magnification conventional fluorescence microscopy images, synapse candidacy was determined. Subsequently, the coordinates of the regions with candidate synapses were imaged using super-resolution structured illumination microscopy. Using this system, synapses from the high-order thalamic nucleus, the posterior medial nucleus in the barrel cortex were rapidly and accurately imaged.
... Auditory fear conditioning (AFC), a common paradigm of associative learning, increases formation of presynaptic boutons and postsynaptic spines in the auditory cortex (A1) 3 . However, although boutons and spines can be visualized using light microscopy, the width of the synaptic cleft is below the diffraction limit and therefore synapses are difficult to discern using light microscopy images 4 . The serial-section electron microscopy (ssEM) technique 5 overcomes the resolution problem and enables large-scale three-dimensional (3D) reconstruction of brain tissue with nanometer-scale resolution, which is sufficient to resolve the ultrastructural features of synapses, such as presynaptic vesicles, the synaptic cleft, and the postsynaptic density (PSD). ...
Reconstruction of serial section electron microscopy (ssEM) data greatly facilitates neuroscience research, but such reconstruction is computationally expensive. Informative data about physiological functions can in theory be obtained from ssEM datasets by extracting distinct cellular structures without large-scale reconstruction, but an efficient method is needed to accomplish this. Here, we developed a Region-CNN (R-CNN) based deep learning method to identify, segment, and reconstruct synapses and mitochondria from ssEM data. We applied this method to explore the changes in synaptic and mitochondrial configuration in the auditory cortex of mice subjected to auditory fear conditioning. Upon reconstructing over 135,000 mitochondria and 160,000 synapses, we found that fear conditioning significantly increases the number—while decreasing the size—of mitochondria, and also noted that it promoted the formation of multi-contact synapses comprising a single axonal bouton and multiple postsynaptic sites from different dendrites. Combinatorial modeling indicated that such multi-dendritic synapses increased information storage capacity of new synapses by over 50%, representing a synaptic memory engram. Our method achieved high accuracy and speed in synapse and mitochondrion extraction, and its application revealed structural and functional insights about cellular engrams associated with fear conditioning.
... Throughout this discussion of barrel cortex microcircuits, some dendritic processing effects have been implied via mention of where presynaptic neurons synapse on postsynaptic cells (soma, perisoma, basal or distal dendrites, etc.). It is important to explicitly note that dendroarchitecture plays an important role in synapse (and, thus, postsynaptic cell) function (Araya, 2014;Bar-Ilan, Gidon & Segev, 2013;Jia et al., 2014;Kurotani et al., 2008;Lavzin et al., 2012;Schoonover et al., 2014;Stuart, 2012;Varga et al., 2002). For example, GABA B receptors work by different biochemical mechanisms in the soma than in dendrites; mostly this means that they have the same resulting effect on postsynaptic firing (Breton & Stuart, 2012) however exceptions can and do arise due to these differences (Stuart, 2012). ...
The classical view of sensory information mainly flowing into barrel cortex at layer IV, moving up for complex feature processing and lateral interactions in layers II and III, then down to layers V and VI for output and corticothalamic feedback is becoming increasingly undermined by new evidence. We review the neurophysiology of sensing and processing whisker deflections, emphasizing the general processing and organisational principles present along the entire sensory pathway—from the site of physical deflection at the whiskers to the encoding of deflections in the barrel cortex. Many of these principles support the classical view. However, we also highlight the growing number of exceptions to these general principles, which complexify the system and which investigators should be mindful of when interpreting their results. We identify gaps in the literature for experimentalists and theorists to investigate, not just to better understand whisker sensation but also to better understand sensory and cortical processing.
