Peer review process
Not revised: This Reviewed Preprint includes the authors’ original preprint (without revision), an eLife assessment, public reviews, and a provisional response from the authors.
Read more about eLife’s peer review process.Editors
- Reviewing EditorUpinder BhallaNational Centre for Biological Sciences, Bangalore, India
- Senior EditorSonia SenTata Institute for Genetics and Society, Bangalore, India
Reviewer #1 (Public review):
Summary:
This study makes use of the EM reconstruction of the fly brain to investigate the morphology and topography of the synapses between retinotopic, loom-sensitive visual projection neurons (VPNs) and downstream descending neurons (DNs). The authors analyzed the distribution of synapses on the dendritic trees of DNs and performed multi-compartmental modelling to study the implications of the synaptic arrangements for neuronal integration of input signals.
Until recently, it has been unclear how spatial information is passed from retinotopic loom-sensitive neurons to descending neurons because the axons of the VPNs terminate in small optic glomeruli with no apparent topographic organization. It has recently been shown that synaptic weight gradients of VPNs connecting to DNs are the main mechanisms that allow for directed behavioral output (Dombrovski et al.). This study now goes one step further to determine if precise synapse location on the dendritic tree contributes further to the information processing. The study suggests that (1) none of the VPNs investigated show a retinotopic organization of synapses on DN dendrites. (2) Synapses of single VPNs are locally clustered. (3) Initial EPSPs at the synaptic location have, as expected, varying amplitudes but the amplitudes are passively normalized and only cover a small range when measured at the SIZ. (4) A near random distribution of synapses allows for linear integration of synaptic inputs when only a few VPNs are activated.
Strengths:
This study provides a detailed picture of the synapse distribution for a set of VPN and DN pairs, in combination with multi-compartmental modelling fitted to electrophysiological data. The data and methods are clear. The findings are overall interesting. The computational pipeline, which should ideally be made publicly available, will allow the community to make similar analyses on different neuronal classes, which will facilitate the detection of more general mechanisms of dendritic computation.
Weaknesses:
- In my opinion, we need more detail on the electrophysiological data and the fitting of the multi-compartmental model, which is the foundation of large parts of the study.
- The study shows that the synapses of an individual VPN are locally clustered and suggests this as evidence for clustering of synapses of similar tuning (as has been shown previously in other systems). I am not fully convinced by the arguments here, since synapses of a single neuron are by necessity not randomly distributed in space.
- As written, it was in parts unclear to me what the main hypotheses and conclusions were - e.g., how would a retinotopic distribution of synapses on dendritic trees contribute to information processing? Are the model predictions in line with the presumed behavioural role of these neurons?
Reviewer #2 (Public review):
Summary:
This article investigates the distribution of synapses on the dendritic arbors of descending neurons in the looming circuit of the fly visual system. The authors use publicly available EM reconstruction data of the adult fly brain to identify the positions of synapses from several types of visual projection neuron (VPN) to descending neuron (DN) connections. VPN dendrites are retinotopically organized, and axons from different VPN populations innervate distinct optic glomeruli. Yet the authors did not find any retinotopic organization of the synapses in the VPN-DN pairs they analyzed. They then constructed passive electrical models of the DNs with their structures extracted from the EM reconstructions. They focused on two specific DNs and parameterized their models by conducting whole-cell recordings within a voltage range below spiking threshold. Simulation of these passive models showed that irrespective of the location of a synapse, EPSPs became very similar at the spike initiation zone. This is consistent with the idea of synaptic democracy where EPSPs at far away synapses have higher amplitude compared to those nearer to the spike initiation zone so that they all attenuate to similar amplitudes while reaching there. The authors found that activating synapses from individual VPNs have the same effect as activating a random set of synapses. They conclude that despite some clustering of VPN synapses at small scale, they are distributed randomly over the dendritic arbor of DNs so that their EPSP amplitude encode the number of activated synapses, avoiding sublinearity from shunting effect.
Strengths:
- Experimental confirmation of the location of the spike initiation zone in the DN arbors is interesting and may provide better understanding of signal processing in these neurons.
- Passive parameters obtained through electrophysiological recordings are useful.
- These morphologically detailed single neuron models, if made available publicly, will be beneficial for building more complete models to understand the fly visual circuit.
- The authors have complemented the work of Dombrovski et al by analyzing the distribution of synapses in more detail from EM data for a different set of neurons.
Weaknesses:
DNs are upstream of motorneurons, and one would expect, as demonstrated by Dombrovski et al, that specific DNs being activated by input from specific regions of the visual field will activate motoneurons so that the fly moves away from a looming object.
The current work analyzed the synapse distribution on two DNs that do not seem to have such role, and emphasize the lack of retinotopy. However, it is not clear why one would expect retinotopy in synapse location on the dendritic arbor. The comparison with mammalian visual circuits is not appropriate because those layers are extracting more and more complex visual features, whereas Drosophila DNs are supposed to drive motoneurons to generate suitable escape behavior.
- The authors do not suggest the functional roles of these DNs in controlling the movement of the fly. They argue that the synapse distribution and the passive electrotonic structure of these neurons are optimized to make the composite EPSP encode the number of activated synapses, but do not explain why this is important.
- Although DNs are spiking neurons, the authors limit their work to the subthreshold passive domain. If the EPSP at the spike initiation zone crosses spiking threshold, will encoding the number of synapses in EPSP amplitude still matter? Will it matter either if the composite EPSP remains subthreshold?
- The temporal aspect of the input has been ignored by the authors in their simulations. First, it is not clear all the synapses from a single VPN should get activated together. One would expect a spike in a VPN to arrive at different synapses with different time delays depending on their electrotonic distance from the spike initiation zone and the signal propagation speed in the neurites.
A looming stimulus should be expanding with time, but from the description of the simulations it does not seem that the authors have tried to incorporate this aspect in their design of the synaptic activation.
- The suggestion in the abstract that linear encoding of synapse number is default strategy which is then tuned by active properties and plasticity seems strange. Developmentally active properties do not get inserted into passive neurons.
- Much of the analysis (Figures 4, 5, 12) show relationships with physical distance along dendrite. In studying passive neurons it is more informative to use electrotonic distance which provides better insight.
