Cochlear inner hair cells (IHCs) transmit acoustic information to spiral ganglion neurons through ribbon synapses. Here we have used morphological and physiological techniques to ask whether synaptic mechanisms differ along the tonotopic axis and within IHCs in the mouse cochlea. We show that the number of ribbon synapses per IHC peaks where the cochlea is most sensitive to sound. Exocytosis, measured as membrane capacitance changes, scaled with synapse number when comparing apical and midcochlear IHCs. Synapses were distributed in the subnuclear portion of IHCs. High-resolution imaging of IHC synapses provided insights into presynaptic Ca(2+) channel clusters and Ca(2+) signals, synaptic ribbons and postsynaptic glutamate receptor clusters and revealed subtle differences in their average properties along the tonotopic axis. However, we observed substantial variability for presynaptic Ca(2+) signals, even within individual IHCs, providing a candidate presynaptic mechanism for the divergent dynamics of spiral ganglion neuron spiking.

The mechanisms underlying the large amplitudes and heterogeneity of excitatory postsynaptic currents (EPSCs) at inner hair cell (IHC) ribbon synapses are unknown. Based on electrophysiology, electron and superresolution light microscopy, and modeling, we propose that uniquantal exocytosis shaped by a dynamic fusion pore is a candidate neurotransmitter release mechanism in IHCs. Modeling indicated that the extended postsynaptic AMPA receptor clusters enable large uniquantal EPSCs. Recorded multiphasic EPSCs were triggered by similar glutamate amounts as monophasic ones and were consistent with progressive vesicle emptying during pore flickering. The fraction of multiphasic EPSCs decreased in absence of Ca(2+) influx and upon application of the Ca(2+) channel modulator BayK8644. Our experiments and modeling did not support the two most popular multiquantal release interpretations of EPSC heterogeneity: (1) Ca(2+)-synchronized exocytosis of multiple vesicles and (2) compound exocytosis fueled by vesicle-to-vesicle fusion. We propose that IHC synapses efficiently use uniquantal glutamate release for achieving high information transmission rates.

Mammalian cochlear spiral ganglion neurons (SGNs) encode sound with microsecond precision. Spike triggering relies upon input from a single ribbon-type active zone of a presynaptic inner hair cell (IHC). Using patch-clamp recordings of rat SGN postsynaptic boutons innervating the modiolar face of IHCs from the cochlear apex, at room temperature, we studied how spike generation contributes to spike timing relative to synaptic input. SGNs were phasic, firing a single short-latency spike for sustained currents of sufficient onset slope. Almost every EPSP elicited a spike, but latency (300 -1500 s) varied with EPSP size and kinetics. When current-clamp stimuli approximated the mean physiological EPSC (Ϸ300 pA), several times larger than threshold current (rheobase, Ϸ50 pA), spikes were triggered rapidly (latency, Ϸ500 s) and precisely (SD, Ͻ50 s). This demonstrated the significance of strong synaptic input. However, increasing EPSC size beyond the physiological mean resulted in less-potent reduction of latency and jitter. Differences in EPSC charge and SGN baseline potential influenced spike timing less as EPSC onset slope and peak amplitude increased. Moreover, the effect of baseline potential on relative threshold was small due to compensatory shift of absolute threshold potential. Experimental first-spike latencies in response to a broad range of stimuli were predicted by a two-compartment exponential integrate-and-fire model, with latency prediction error of Ͻ100 s. In conclusion, the close anatomical coupling between a strong synapse and spike generator along with the phasic firing property lock SGN spikes to IHC exocytosis timing to generate the auditory temporal code with high fidelity.

One major question in neuroscience is how to relate connectomes to neural activity, circuit function, and learning. We offer an answer in the peripheral olfactory circuit of the Drosophila larva, composed of olfactory receptor neurons (ORNs) connected through feedback loops with interconnected inhibitory local neurons (LNs). We combine structural and activity data and, using a holistic normative framework based on similarity-matching, we propose a biologically plausible mechanistic model of the circuit. Our model predicts the ORN → LN synaptic weights found in the connectome and demonstrate that they reflect correlations in ORN activity patterns. Additionally, our model explains the relation between ORN → LN and LN - LN synaptic weight and the arising of different LN types. This global synaptic organization can autonomously arise through Hebbian plasticity, and thus allows the circuit to adapt to different environments in an unsupervised manner. Functionally, we propose LNs extract redundant input correlations and dampen them in ORNs, thus partially whitening and normalizing the stimulus representations in ORNs. Our work proposes a comprehensive framework to combine structure, activity, function, and learning, and uncovers a general and potent circuit motif that can learn and extract significant input features and render stimulus representations more efficient.

No abstract

I hereby ensure that the thesis has been written independently and with no other sources and aids than quoted.

It has been known for more than 30 years that the sensitivity of auditory nerve fibers in mammals is correlated to their spontaneous firing rate and that the variability of these two properties is linked to the auditory system's capability to reliably encode sounds with intensities spanning about 4 orders of magnitude [1]. However, neither the origin nor the coupling of these properties is fully understood. The key players at this early level of the auditory system are the presynaptic active zone of inner hair cells, the postsynaptic spiral ganglion neurons and efferent synapses onto spiral ganglion neurons.With experiments, we demonstrate pronounced variability of presynaptic Ca 2+ signals among different ribbon synapses within single hair cells, e.g. in terms of microdomain amplitude and voltage-dependence. We use modeling to investigate how each of these components could contribute to modify the spontaneous rate and sensitivity. On the presynaptic side, the effect of varying Ca 2+ -channel number, gating properties and density has been explored. On the postsynaptic side, we vary active and passive membrane properties in the afferent fiber, as well as the strength of a tonically active or intensity-driven feedback inhibition by the efferent fiber.Parameters for the modeling of the hair cell ribbon synapse are restrained by measurements from in-vivo and invitro experiments, including fast confocal imaging of synaptic calcium signals, current fluctuation analysis derived calcium channel counts, STED-microscopy based calcium channel cluster size estimates, as well as auditory nerve firing properties from single unit recordings. Although not frequently considered in the literature, presynaptic parameters are found capable of strongly influence spontaneous rate and sensitivity. References 1.Liberman MC: Auditory-nerve response from cats raised in a low-noise chamber.

One major question in neuroscience is how to relate connectomes to neural activity, circuit function, and learning. We offer an answer in the peripheral olfactory circuit of the Drosophila larva, composed of olfactory receptor neurons (ORNs) connected through feedback loops with interconnected inhibitory local neurons (LNs). We combine structural and activity data and, using a holistic normative framework based on similarity-matching, we formulate biologically plausible mechanistic models of the circuit. In particular, we consider a linear circuit model, for which we derive an exact theoretical solution, and a nonnegative circuit model, which we examine through simulations. The latter largely predicts the ORN → LN synaptic weights found in the connectome and demonstrates that they reflect correlations in ORN activity patterns. Furthermore, this model accounts for the relationship between ORN → LN and LN–LN synaptic counts and the emergence of different LN types. Functionally, we propose that LNs encode soft cluster memberships of ORN activity, and partially whiten and normalize the stimulus representations in ORNs through inhibitory feedback. Such a synaptic organization could, in principle, autonomously arise through Hebbian plasticity and would allow the circuit to adapt to different environments in an unsupervised manner. We thus uncover a general and potent circuit motif that can learn and extract significant input features and render stimulus representations more efficient. Finally, our study provides a unified framework for relating structure, activity, function, and learning in neural circuits and supports the conjecture that similarity-matching shapes the transformation of neural representations.