Distinct in vivo dynamics of excitatory synapses onto cortical pyramidal neurons and parvalbumin-positive interneurons

Melander, Joshua B.; Nayebi, Aran; Jongbloets, Bart C.; Fortin, Dale A.; Qin, Maozhen; Ganguli, Surya; Mao, Tianyi; Zhong, Haining

doi:10.1016/j.celrep.2021.109972

Cited by 13 publications

(7 citation statements)

References 74 publications

(106 reference statements)

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“…Functional impacts of such a mechanism have been described, as release probability from CA1 neurons onto different interneuron types is influenced by postsynaptic expression of neuroligin-3 (52) or ELFN1 (36). Beyond synapse strength, Ex→PV synapses are more stable (53), and we speculate that augmented or specific CAM content could lead both to enhanced alignment and decreased synaptic turnover.…”

Section: Discussionmentioning

confidence: 94%

Differential nanoscale organization of excitatory synapses onto excitatory vs inhibitory neurons

Dharmasri,

Levy,

Blanpied

2023

Preprint

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A key feature of excitatory synapses is the existence of subsynaptic protein nanoclusters whose precise alignment across the cleft in a trans-synaptic nanocolumn influences the strength of synaptic transmission. However, whether nanocolumn properties vary between excitatory synapses functioning in different cellular contexts is unknown. We used a combination of confocal and DNA-PAINT super-resolution microscopy to directly compare the organization of shared scaffold proteins at two important excitatory synapses - those forming onto excitatory principal neurons (Ex→Ex synapses) and those forming onto parvalbumin-expressing interneurons (Ex→PV synapses). As in Ex→Ex synapses, we find that in Ex→PV synapses presynaptic Munc13-1 and postsynaptic PSD-95 both form nanoclusters that demonstrate alignment, underscoring synaptic nanostructure and the trans-synaptic nanocolumn as conserved organizational principles of excitatory synapses. Despite the general conservation of these features, we observed specific differences in the characteristics of pre- and postsynaptic Ex→PV nanostructure. Ex→PV synapses contained larger PSDs with fewer PSD-95 NCs when accounting for size than Ex→Ex synapses. Furthermore, the PSD-95 NCs were larger and denser. The identity of the postsynaptic cell also had a retrograde impact on Munc13-1 organization, as Ex→PV synapses hosted larger Munc13-1 puncta that contained less dense but larger and more numerous Munc13-1 NCs. Moreover, we measured the spatial variability of trans- synaptic alignment in these synapse types, revealing protein alignment in Ex→PV synapses over a distinct range of distances compared to Ex→Ex synapses. We conclude that while general principles of nanostructure and alignment are shared, cell-specific elements of nanodomain organization likely contribute to functional diversity of excitatory synapses. Understanding the rules of synapse nanodomain assembly, which themselves are cell-type specific, will be essential for illuminating brain network dynamics.

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Section: Discussionmentioning

confidence: 94%

Differential nanoscale organization of excitatory synapses onto excitatory vs inhibitory neurons

Dharmasri,

Levy,

Blanpied

2023

Preprint

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“…Chapter 6 contains work from two publications [164,174] related to the problem of extracting dynamical rules from observations related to synaptic plasticity. The first publication [164] analyzes the dynamics of hundreds of synaptic weights in-vivo over the course of approximately a month.…”

Section: Overviewmentioning

confidence: 99%

Recurrent Connections in the Primate Ventral Visual Stream Mediate a Tradeoff Between Task Performance and Network Size During Core Object Recognition

Nayebi

Sagastuy-Brena

Bear

et al. 2021

Preprint

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The ventral visual stream (VVS) is a hierarchically connected series of cortical areas known to underlie core object recognition behaviors, enabling humans and non-human primates to effortlessly recognize objects across a multitude of viewing conditions. While recent feedforward convolutional neural networks (CNNs) provide quantitatively accurate predictions of temporally-averaged neural responses throughout the ventral pathway, they lack two ubiquitous neuroanatomical features: local recurrence within cortical areas and long-range feedback from downstream areas to upstream areas. As a result, such models are unable to account for the temporally-varying dynamical patterns thought to arise from recurrent visual circuits, nor can they provide insight into the behavioral goals that these recurrent circuits might help support. In this work, we augment CNNs with local recurrence and long-range feedback, developing convolutional RNN (ConvRNN) network models that more correctly mimic the gross neuroanatomy of the ventral pathway. Moreover, when the form of the recurrent circuit is chosen properly, ConvRNNs with comparatively small numbers of layers can achieve high performance on a core recognition task, comparable to that of much deeper feedforward networks. We then compared these models to temporally fine-grained neural and behavioral recordings from primates to thousands of images. We found that ConvRNNs better matched these data than alternative models, including the deepest feedforward networks, on two metrics: 1) neural dynamics in V4 and inferotemporal (IT) cortex at late timepoints after stimulus onset, and 2) the varying times at which object identity can be decoded from IT, including more challenging images that take longer to decode. Moreover, these results differentiate within the class of ConvRNNs, suggesting that there are strong functional constraints on the recurrent connectivity needed to match these phenomena. Finally, we find that recurrent circuits that attain high task performance while having a smaller network size as measured by number of units, rather than another metric such as the number of parameters, are overall most consistent with these data. Taken together, our results evince the role of recurrence and feedback in the ventral pathway to reliably perform core object recognition while subject to a strong total network size constraint.

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“…For inhibitory inputs to LSO neurons, in P1-5 mice there are many small and no large inputs, but by P9-14 the input sizes are highly skewed, and some inputs are very large (Gjoni et al, 2018;Kim and Kandler, 2003). In the cortex, input sizes are highly variable, and are often approximated by a lognormal distribution (Buzsáki and Mizuseki, 2014;Dorkenwald et al, 2022;Melander et al, 2021). Thus, the distribution of PC to CbN input sizes we observe in P23-32 mice, is similar in many ways to the distribution of input sizes of other types of synapses.…”

Section: The Variable Distribution Of Pc-cbn Input Sizesmentioning

confidence: 99%

Implications of variable synaptic weights for rate and temporal coding of cerebellar outputs

Wardak

Khan

et al. 2023

Preprint

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Purkinje cell (PC) synapses onto cerebellar nuclei (CbN) neurons convey signals from the cerebellar cortex to the rest of the brain. PCs are inhibitory neurons that spontaneously fire at high rates, and many uniform sized PC inputs are thought to converge onto each CbN neuron to suppress or eliminate firing. Leading theories maintain that PCs encode information using either a rate code, or by synchrony and precise timing. Individual PCs are thought to have limited influence on CbN neuron firing. Here, we find that single PC to CbN synapses are highly variable in size, and using dynamic clamp and modelling we reveal that this has important implications for PC-CbN transmission. Individual PC inputs regulate both the rate and timing of CbN firing. Large PC inputs strongly influence CbN firing rates and transiently eliminate CbN firing for several milliseconds. Remarkably, the refractory period of PCs leads to a briefly elevation of CbN firing prior to suppression. Thus, PC-CbN synapses are suited to concurrently convey rate codes, and generate precisely-timed responses in CbN neurons. Variable input sizes also elevate the baseline firing rates of CbN neurons by increasing the variability of the inhibitory conductance. Although this reduces the relative influence of PC synchrony on the firing rate of CbN neurons, synchrony can still have important consequences, because synchronizing even two large inputs can significantly increase CbN neuron firing. These findings may be generalized to other brain regions with highly variable sized synapses.

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Distinct in vivo dynamics of excitatory synapses onto cortical pyramidal neurons and parvalbumin-positive interneurons

Cited by 13 publications

References 74 publications

Differential nanoscale organization of excitatory synapses onto excitatory vs inhibitory neurons

Differential nanoscale organization of excitatory synapses onto excitatory vs inhibitory neurons

Recurrent Connections in the Primate Ventral Visual Stream Mediate a Tradeoff Between Task Performance and Network Size During Core Object Recognition

Implications of variable synaptic weights for rate and temporal coding of cerebellar outputs

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