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Title: Efficient encoding of motion is mediated by gap junctions in the fly visual system}
Article:
Abstract:
Understanding the computational implications of specific synaptic connectivity patterns is a fundamental goal in neuroscience. In particular, the computational role of ubiquitous electrical synapses operating via gap junctions remains elusive. In the fly visual system, the cells in the vertical-system network, which play a key role in visual processing, primarily connect to each other via axonal gap junctions. This network therefore provides a unique opportunity to explore the functional role of gap junctions in sensory information processing. Our information theoretical analysis of a realistic VS network model shows that within 10 ms following the onset of the visual input, the presence of axonal gap junctions enables the VS system to efficiently encode the axis of rotation, θ, of the fly’s ego motion. This encoding efficiency, measured in bits, is near-optimal with respect to the physical limits of performance determined by the statistical structure of the visual input itself. The VS network is known to be connected to downstream pathways via a subset of triplets of the vertical system cells; we found that because of the axonal gap junctions, the efficiency of this subpopulation in encoding θ is superior to that of the whole vertical system network and is robust to a wide range of signal to noise ratios. We further demonstrate that this efficient encoding of motion by this subpopulation is necessary for the fly's visually guided behavior, such as banked turns in evasive maneuvers. Because gap junctions are formed among the axons of the vertical system cells, they only impact the system’s readout, while maintaining the dendritic input intact, suggesting that the computational principles implemented by neural circuitries may be much richer than previously appreciated based on point neuron models. Our study provides new insights as to how specific network connectivity leads to efficient encoding of sensory stimuli.
Introduction:
The principles governing synaptic connectivity may hold the key to understanding the functional organization of the brain. Gap junctions( GJs) underlying the operation of electrical synapses are found in both the central nervous system[1–4] and the sensory system[5, 6, 7]. However, their functional role still remains elusive despite extensive studies[8–13]. Here we address this problem in the vertical system( VS) network of the fly visual system, where recent studies[14–18] have identified that in this sensory network, individual neurons primarily use GJs to communicate with one another. This provides a unique opportunity to characterize the function of GJs in the context of sensory information processing. In the visual system of the blowfly Calliphora vicina, photoreceptor signals are processed in four consecutive layers of the neuropile: the lamina, medulla, lobula, and lobula plate, each of which is arranged in columnar, retinotopic fashion. In a striking parallel to the vertebrate retina[19, 20], the direction of visual motion is computed in parallel ON and OFF motion pathways[21]. Columnar T4 and T5 cells in the lobula and lobula plate, represent the output of the ON and the OFF pathway, respectively[22]. They synapse onto the dendrites of large-field tangential cells( LPTC) such as the horizontal system( HS) and vertical system( VS) cells[23, 24, 25]. Among these LPTCs, 20 different VS cells have been described. There are 10 VS cells in both the left and right compound eyes, ordered VS1 to VS10 along the anterior-posterior axis[26]. These were shown to encode the azimuth degree of the axis of rotation via their axonal voltages( these are non-spiking neurons). VS cells are connected to other LPTC cells within the lobula plate, as well as to downstream neurons such as the descending neurons( DNOVS1, 2[26, 27]) that are upstream to the neck motor center. Intriguingly, adjacent VS cells are connected to each other via axonal GJs, whose conductance is on the order of 1 μS[16, 18]. Surprisingly, the only identified output of the VS system to the downstream system is the connection between the left and the right VS 5-6-7 triplets to the DNOVS neurons. No downstream pathways are known to read out from the whole VS 1–10 network. The VS network is an early sensory network that encodes motion information from a complex environment under severe constraints. For example, the fly only requires a 30–40 ms visual-motor delay to elicit evasive maneuvers[28, 29] to escape from swats effectively. Previous work hypothesized that this is because neural coding of sensory information is efficient[30–37]. This principle has been used successfully to account for many observed properties in sensory systems, from the size and shape of receptive fields[30, 31, 38, 39], and the statistics of the spike train[40, 41], to the higher-order interaction of populations of neurons[42–46]. However, previous work on efficient coding with population of neurons has not considered whether the inclusion of a specific connectivity feature i. e., GJs, can contribute to the emergence of this encoding efficiency[47]. Here, we investigated whether the VS network encodes motion information efficiently using axonal GJs and if so, the implications of this efficient encoding of motion in behavioral contexts. However, because experimental studies that record the impact of the visual input impinging on VS cell dendrites usually use calcium imaging[48], the response of VS cell dendrites at millisecond precision is still unavailable. We therefore investigated this problem by using a physiologically realistic model of the VS network[16]. Because previous work had shown that GJs can only help encoding when they are located at the axons[17], here we contrast with and without axonal GJs in this VS network to quantify the effect of the axonal GJs. In addition, because this network connects to the neck motor center via descending neurons( DNOVS1, 2) and DNOVS2 has a maximum firing rate of 100Hz([27], DNOVS1 is a graded neuron), we used the first 10 ms after the stimulus onset to sample the VS axonal voltages. Based on our simulations of the model VS network with GJs and without GJs in two different stimulus conditions( natural and checkerboard), below we show that these axonal GJs enable VS cells to reduce the fluctuation in encoding individual axes of rotation, such that VS cells can jointly encode different axes of rotation with better separability. Furthermore, our information theoretic analysis showed that with GJs, motion encoding by the VS network, based on its joint axonal output voltage, extracts almost all the available motion information provided by dendritic input. We also found that the VS 5-6-7 triplet which is directly connected to downstream DNOVS1, 2 neurons encodes motion more efficiently than the network as a whole, and reaches at least 90% of the physical limit of performance as determined by the statistical structure of the visual input itself. This near-optimal encoding efficiency emerged as robust regardless of the signal to noise ratios. Because GJs are formed among the axons of VS cells, they only impact the system’s readout while maintaining the dendritic input intact, suggesting that the computational principles implemented by neural circuitries may be much richer than previously assumed from point neuron models[49, 50]. Finally, we demonstrate that this efficient encoding of motion enabled by the GJs is behaviorally important. Without GJs, the VS network cannot even encode some basic rotations correctly such as the up/down tilt of the head and body( pitch rotation). Considering that evasive maneuvers involve a banked turn[29, 51] which combines roll and pitch at the same time, this finding suggests that the presence of GJs is critical for the survival of the fly.
Discussion:
Only a few studies[56, 57] have attempted to use a probabilistic approach to investigate population coding of motion in the VS network. These studies have focused exclusively on how well the axonal voltage, with/without GJs, could be used to estimate the axis of rotation. Although this estimation can be generalized to a wide spectrum of tasks[58]), it is still not as parsimonious as the measure of motion information( in bits) that we provided in this study. Our measure of motion information quantifies the absolute amount of information available to perform all possible tasks. Unlike the previous approach( which hypothesized that reading out from the whole VS network is optimal in decoding), we used the information bottleneck method[53] that enabled us to evaluate the physical limit of encoding efficiency, which is solely determined by the statistical structure of the visual input itself. This meant we could assess how well the system performs with respect to its respective physical limit without making any assumptions. We were able quantitatively show that the VS 5-6-7 triplet outperforms the whole VS network in terms of encoding efficiency( Fig 3). We further shed light on the behavioral superiority of having GJs, by inspecting which errors they could correct on both estimation and discrimination tasks( Fig 5 and S3 Fig), which was not investigated in previous studies. The blowfly visual system is a classical model for studies on efficient encoding of motion. Most previous studies have been conducted on the H1 neuron, a LPTC neuron selective for horizontal inward motion, but without known direct connections to the VS network[40, 59, 60]. It was shown that the H1 neuron efficiently uses of its spiking capacity to transmit information and is highly adaptive for natural stimuli. Its encoding efficiency is close to the its physical limit[40] as we have shown for the VS system. These works claimed that a single neuron can generate efficient encoding of its input. We complement this view on the efficient encoding of motion by showing that the specific synaptic connectivity may also serve as a candidate mechanism for efficient encoding in a population-coding paradigm. In this perspective, our work broadens the applicability of a previously known computational principle to describe how circuitry features supports efficient encoding of sensory stimuli The VS neurons consist of an elaborated dendritic tree and an axon that is connected via GJs to nearby axons. Therefore, the VS cells in our model consist of a separate dendritic and axonal compartment( Fig 1). Importantly, this implies that GJs facilitate information transmission among axons while maintaining the information received by the dendrites intact. Thus, the efficient coding mechanism suggested in our work is fundamentally different from those observed in “point neuron” models, which can only interpret improving the mutual information between input and output as increasing of energy expenditure on single cells[61, 62]. To the best of our knowledge, this study is the first to demonstrate that the efficient encoding of sensory stimuli can be induced by axonal GJs. There are two natural extensions of the present study. The first is to extend the single cell model to include the elaborate dendritic morphology of real VS cells. It is known that local motion detectors( LMD) units impinge on distal small dendritic branchlets of VS cells[48]. Given that LMDs function on time scales that are about ten times slower than those of the membrane time constant of VS cells[18], the input to the dendrites is likely to be sparse. This may impact the encoding strategy implemented by the VS system. This leads to the intriguing question of whether and how the dendritic structure might influence the near optimality of the encoding efficiency as found in the present study. We hypothesize that including dendrites would illustrate how the “roll” motion( clockwise/counterclockwise turn) is encoded, since previous experiments have shown that the dendritic morphology can reduce fluctuations in the vertical direction[63]. Furthermore, the biological VS network receives input from both the compound eyes and the ocelli[64], whereas our simulation only considered how efficiently the VS network represents its input from the compound eyes. We predict that additional information from the ocelli should improve the motion representation of the VS network[64]. The second extension would be to investigate why the 10 ms following the stimulus onset is the relevant timescale for the efficient encoding of motion. As shown in S2A Fig, most motion information is obtained by the VS5-6-7 triplet within the first 10 ms. Considering that a change in rotation in response to a visual cue takes ~30–40 ms[29], it remains unclear why the saturation of motion information encoding should take place in the first 10 ms. In other words, how does the information available in the first 10 ms act predictively for the behavioral state of the fly at later times? Addressing this issue could shed light on whether the efficient encoding of motion in the fly visual system matches the efficient encoding of predictive information, a known principle that governs population coding in the retina[65]. Given the presence of gap junctions in many neuronal systems, we believe that our study will encourage the exploration of GJs as a common mechanism for improving information transmission in these systems. Initial evidence suggests that GJs improve information transmission in both the periphery( retina, shown in[7]) as well as in neocortical L2/3 inhibitory interneuron networks[66]. The discussion in[19] supports the idea that low-level motion detection follows a common circuit design in both fly and mammalian motion vision. However, the extent to which this commonality also holds throughout the central nervous system has yet to be studied.
Understanding sensory stimuli from the environment and deciding how best to respond to it behaviorally is essential for survival . What makes organisms efficient in encoding these sensory stimuli ? This study provides a novel view on this unresolved issue using the visual system of the fly . We show that a specific synaptic connectivity manifested via gap junctions ( GJs ) among axons in the Vertical System ( VS ) network leads to particularly high encoding efficiency of the axis of rotation of the fly’s ego motion . Due to these GJs , triplets of VS neurons ( the VS5-6-7 triplet ) , which connect to a downstream motor system , encode motion stimuli at an efficiency close to the physical limit; this efficient encoding is necessary for evasive maneuvers that are critical for the fly to escape predators . We then suggest why GJs in the VS network enable such high encoding efficiency .