Distinct synaptic transfer functions in same-type photoreceptors

Many sensory systems use ribbon-type synapses to transmit their signals to downstream circuits. The properties of this synaptic transfer fundamentally dictate which aspects in the original stimulus will be accentuated or suppressed, thereby partially defining the detection limits of the circuit. Accordingly, sensory neurons have evolved a wide variety of ribbon geometries and vesicle pool properties to best support their diverse functional requirements. However, the need for diverse synaptic functions does not only arise across neuron types, but also within. Here we show that UV-cones, a single type of photoreceptor of the larval zebrafish eye, exhibit striking differences in their synaptic ultrastructure and consequent calcium to glutamate transfer function depending on their location in the eye. We arrive at this conclusion by combining serial section electron microscopy and simultaneous ‘dual-colour’ two-photon imaging of calcium and glutamate signals from the same synapse in vivo. We further use the functional dataset to fit a cascade-like model of the ribbon synapse with different vesicle pool sizes, transfer rates, and other synaptic properties. Exploiting recent developments in simulation-based inference, we obtain full posterior estimates for the parameters and compare these across different retinal regions. The model enables us to extrapolate to new stimuli and to systematically investigate different response behaviours of various ribbon configurations. We also provide an interactive, easy-to-use version of this model as an online tool. Overall, we show that already on the synaptic level of single-neuron types there exist highly specialised mechanisms which are advantageous for the encoding of different visual features

Training deep neural density estimators to identify mechanistic models of neural dynamics

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators-- trained using model simulations-- to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features, and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin-Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics

Experimental and theoretical analysis of multivesicular release at a sensory synapse

Traditionally, neurons were believed to transmit information between one another by action potentials and the release of synaptic vesicles, both of which thought to be binary processes. In this framework, an action potential (or change in membrane potential for graded neurons) elicits the release of at most a single vesicle per active zone (AZ), the site of vesicular release. Information, then, is conveyed in this system simply be altering the probability of a single vesicle being released, and thus altering the total rate of release. In recent years, however, evidence has demonstrated that many cells, particularly those in the early sensory systems, are capable of multivesicular release (MVR), wherein multiple synaptic vesicles are released nearly simultaneously, with precision of up to 10 microseconds. As most neurons have a temporal integration window on the millisecond range, it is thus likely the postsynaptic cell could represent these events as distinct symbols in a non-binary digital system, allowing for information transmission not only in the rate of vesicular events, but also their amplitude. While evidence for MVR has existed for decades, considerably less attention has been paid to its functional significance – how does MVR affect processing in intact circuits? Is it utilized to transmit information? How efficiently does it operate? In this work I attempt to answer these questions, first by developing a method with which one can decompose 2p glutamate recordings from intact zebrafish BC terminals into units of individual vesicles – essentially ‘counting’ vesicles. In doing so, I show that MVR is used in the early visual system to transmit temporal contrast information, one of the most basic aspects of the visual scene. Finally, I create a model of vesicle release and postsynaptic activity that allows for directly comparing how information transmission can be influenced by either rate or amplitude coding. Here, by systematically altering the parameters of the postsynaptic cell, I demonstrate in which circumstances either rate coding or amplitude coding are beneficial for transmission. MVR, rather than being some obscure phenomenon, plays a fundamental part in processing and transmitting information in early sensory systems

Training deep neural density estimators to identify mechanistic models of neural dynamics

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators—trained using model simulations—to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin–Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics

Connectivity of the Outer Plexiform Layer of the Mouse Retina

The retina has two synaptic layers: In the outer plexiform layer (OPL), signals from the photoreceptors (PRs) are relayed to the bipolar cells (BCs) with one type of horizontal cell (HC) as interneuron. In the inner plexiform layer (IPL), the retinal ganglion cells (RGCs) receive input from the bipolar cells, modulated by multiple types of amacrine cells. The axons of the retinal ganglion cells form the optic nerve which transmit the visual signal to the higher regions of the brain (Masland 2012). Studies of signal processing in the retina usually focus on the inner plexiform layer. Here, the main computations take place such as direction selectivity, orientation selectivity and object motion detection (Gollisch and Meister 2010). However, to fully understand how these computations arise, it is also important to understand how the input to the ganglion cells is computed and thus to understand the functional differences between BC signals. While these are shaped to some extent in the IPL through amacrine cell feedback (Franke et al. 2017), they are also influenced by computations in the OPL (Drinnenberg et al. 2018). Accordingly, it is essential to understand how the bipolar cell signals are formed and what the exact connectivity in the OPL is. This thesis project aims at a quantitative picture of the mouse outer retina connectome. It takes the approach of systematically analyzing connectivity between the cell types in the OPL based on available high-resolution 3D electron microscopy imaging data (Helmstaedter et al. 2013). We reconstructed photoreceptor axon terminals, horizontal cells and bipolar cells, and quantified their contact statistics. We identified a new structure on HC dendrites which likely defines a second synaptic layer in the OPL below the PRs. Based on the reconstructed morphology, we created a biophysical model of a HC dendrite to gain insights into potential functional mechanisms. Our results reveal several new connectivity patterns in the mouse OPL and suggest that HCs perform two functional roles at two distinct output sites at the same time. The project emphasizes how large-scale EM data can boost research on anatomical connectivity and beyond and highlights the value of the resulting data for detailed biophysical modeling. Moreover, it shows how the known amount of complexity increases with the level of detail with which we can study a subject. Beyond that, this thesis project demonstrates the benefits of data sharing and open science which only enabled our studies

Optimal information storage : nonsequential sources and neural channels

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.MIT Institute Archives copy: pages 101-163 bound in reverse order.Includes bibliographical references (p. 141-163).Information storage and retrieval systems are communication systems from the present to the future and fall naturally into the framework of information theory. The goal of information storage is to preserve as much signal fidelity under resource constraints as possible. The information storage theorem delineates average fidelity and average resource values that are achievable and those that are not. Moreover, observable properties of optimal information storage systems and the robustness of optimal systems to parameter mismatch may be determined. In this thesis, we study the physical properties of a neural information storage channel and also the fundamental bounds on the storage of sources that have nonsequential semantics. Experimental investigations have revealed that synapses in the mammalian brain possess unexpected properties. Adopting the optimization approach to biology, we cast the brain as an optimal information storage system and propose a theoretical framework that accounts for many of these physical properties. Based on previous experimental and theoretical work, we use volume as a limited resource and utilize the empirical relationship between volume anrid synaptic weight.(cont.) Our scientific hypotheses are based on maximizing information storage capacity per unit cost. We use properties of the capacity-cost function, e-capacity cost approximations, and measure matching to develop optimization principles. We find that capacity-achieving input distributions not only explain existing experimental measurements but also make non-trivial predictions about the physical structure of the brain. Numerous information storage applications have semantics such that the order of source elements is irrelevant, so the source sequence can be treated as a multiset. We formulate fidelity criteria that consider asymptotically large multisets and give conclusive, but trivialized, results in rate distortion theory. For fidelity criteria that consider fixed-size multisets. we give some conclusive results in high-rate quantization theory, low-rate quantization. and rate distortion theory. We also provide bounds on the rate-distortion function for other nonsequential fidelity criteria problems. System resource consumption can be significantly reduced by recognizing the correct invariance properties and semantics of the information storage task at hand.by Lav R. Varshney.S.M

Individual differences in supra-threshold auditory perception - mechanisms and objective correlates

Thesis (Ph.D.)--Boston UniversityTo extract content and meaning from a single source of sound in a quiet background, the auditory system can use a small subset of a very redundant set of spectral and temporal features. In stark contrast, communication in a complex, crowded scene places enormous demands on the auditory system. Spectrotemporal overlap between sounds reduces modulations in the signals at the ears and causes masking, with problems exacerbated by reverberation. Consistent with this idea, many patients seeking audiological treatment seek help precisely because they notice difficulties in environments requiring auditory selective attention. In the laboratory, even listeners with normal hearing thresholds exhibit vast differences in the ability to selectively attend to a target. Understanding the mechanisms causing these supra-threshold differences, the focus of this thesis, may enable research that leads to advances in treating communication disorders that affect an estimated one in five Americans. Converging evidence from human and animal studies points to one potential source of these individual differences: differences in the fidelity with which supra-threshold sound is encoded in the early portions of the auditory pathway. Electrophysiological measures of sound encoding by the auditory brainstem in humans and animals support the idea that the temporal precision of the early auditory neural representation can be poor even when hearing thresholds are normal. Concomitantly, animal studies show that noise exposure and early aging can cause a loss (cochlear neuropathy) of a large percentage of the afferent population of auditory nerve fibers innervating the cochlear hair cells without any significant change in measured audiograms. Using behavioral, otoacoustic and electrophysiological measures in conjunction with computational models of sound processing by the auditory periphery and brainstem, a detailed examination of temporal coding of supra-threshold sound is carried out, focusing on characterizing and understanding individual differences in listeners with normal hearing thresholds and normal cochlear mechanical function. Results support the hypothesis that cochlear neuropathy may reduce encoding precision of supra-threshold sound, and that this manifests as deficits both behaviorally and in subcortical electrophysiological measures in humans. Based on these results, electrophysiological measures are developed that may yield sensitive, fast, objective measures of supra-threshold coding deficits that arise as a result of cochlear neuropathy

Designing Scalable Biological Interfaces

This thesis presents the analysis and design of biological interfacing technologies in light of a need for radical improvements in scalability. It focuses primarily on structural and functional neural data acquisition, but also extends to other problems including genomic editing and nanoscale spatial control. Its main contributions include analysis of the physical limits of large-scale neural recording, experimental development of a screening platform for ion-dependent molecular recording devices, characterization of the design space for molecularly-annotated neural connectomics, and new designs for high-speed genome engineering and bio-nano-fabrication. Articulating governing principles and roadmaps for these domains has contributed to the initiation of multi-institutional projects that are strategically targeted towards scalability

Removal of antagonistic spindle forces can rescue metaphase spindle length and reduce chromosome segregation defects

Regular Abstracts - Tuesday Poster Presentations: no. 1925Metaphase describes a phase of mitosis where chromosomes are attached and oriented on the bipolar spindle for subsequent segregation at anaphase. In diverse cell types, the metaphase spindle is maintained at a relatively constant length. Metaphase spindle length is proposed to be regulated by a balance of pushing and pulling forces generated by distinct sets of spindle microtubules and their interactions with motors and microtubule-associated proteins (MAPs). Spindle length appears important for chromosome segregation fidelity, as cells with shorter or longer than normal metaphase spindles, generated through deletion or inhibition of individual mitotic motors or MAPs, showed chromosome segregation defects. To test the force balance model of spindle length control and its effect on chromosome segregation, we applied fast microfluidic temperature-control with live-cell imaging to monitor the effect of switching off different combinations of antagonistic forces in the fission yeast metaphase spindle. We show that spindle midzone proteins kinesin-5 cut7p and microtubule bundler ase1p contribute to outward pushing forces, and spindle kinetochore proteins kinesin-8 klp5/6p and dam1p contribute to inward pulling forces. Removing these proteins individually led to aberrant metaphase spindle length and chromosome segregation defects. Removing these proteins in antagonistic combination rescued the defective spindle length and, in some combinations, also partially rescued chromosome segregation defects. Our results stress the importance of proper chromosome-to-microtubule attachment over spindle length regulation for proper chromosome segregation.postprin

Psr1p interacts with SUN/sad1p and EB1/mal3p to establish the bipolar spindle

Regular Abstracts - Sunday Poster Presentations: no. 382During mitosis, interpolar microtubules from two spindle pole bodies (SPBs) interdigitate to create an antiparallel microtubule array for accommodating numerous regulatory proteins. Among these proteins, the kinesin-5 cut7p/Eg5 is the key player responsible for sliding apart antiparallel microtubules and thus helps in establishing the bipolar spindle. At the onset of mitosis, two SPBs are adjacent to one another with most microtubules running nearly parallel toward the nuclear envelope, creating an unfavorable microtubule configuration for the kinesin-5 kinesins. Therefore, how the cell organizes the antiparallel microtubule array in the first place at mitotic onset remains enigmatic. Here, we show that a novel protein psrp1p localizes to the SPB and plays a key role in organizing the antiparallel microtubule array. The absence of psr1+ leads to a transient monopolar spindle and massive chromosome loss. Further functional characterization demonstrates that psr1p is recruited to the SPB through interaction with the conserved SUN protein sad1p and that psr1p physically interacts with the conserved microtubule plus tip protein mal3p/EB1. These results suggest a model that psr1p serves as a linking protein between sad1p/SUN and mal3p/EB1 to allow microtubule plus ends to be coupled to the SPBs for organization of an antiparallel microtubule array. Thus, we conclude that psr1p is involved in organizing the antiparallel microtubule array in the first place at mitosis onset by interaction with SUN/sad1p and EB1/mal3p, thereby establishing the bipolar spindle.postprin