Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics

Oesterle, Jonathan; Behrens, Christian; Schröder, Cornelius; Hermann, Thoralf; Euler, Thomas; Franke, Katrin; Smith, Robert G.; Zeck, Günther; Berens, Philipp

doi:10.7554/elife.54997

Cited by 22 publications

(28 citation statements)

References 110 publications

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“…To simulate the electrical spreads on bipolar cell axon branches, we have constructed a passive cable model (Figures 2D and 2E) using the anatomical parameters of bipolar cells described before (Oesterle et al, 2020;Oltedal et al, 2009): diameter in the axon, 1.3 mm; membrane capacitance (R m ), 25 kU cm 2 ; axial resistivity (R i ), 132 U cm. The source activity was modeled by an alpha function f:…”

Section: Passive Cable Modelmentioning

confidence: 99%

Direction selectivity in retinal bipolar cell axon terminals

Matsumoto

Agbariah

Nolte

et al. 2021

Neuron

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Summary The ability to encode the direction of image motion is fundamental to our sense of vision. Direction selectivity along the four cardinal directions is thought to originate in direction-selective ganglion cells (DSGCs) because of directionally tuned GABAergic suppression by starburst cells. Here, by utilizing two-photon glutamate imaging to measure synaptic release, we reveal that direction selectivity along all four directions arises earlier than expected at bipolar cell outputs. Individual bipolar cells contained four distinct populations of axon terminal boutons with different preferred directions. We further show that this bouton-specific tuning relies on cholinergic excitation from starburst cells and GABAergic inhibition from wide-field amacrine cells. DSGCs received both tuned directionally aligned inputs and untuned inputs from among heterogeneously tuned glutamatergic bouton populations. Thus, directional tuning in the excitatory visual pathway is incrementally refined at the bipolar cell axon terminals and their recipient DSGC dendrites by two different neurotransmitters co-released from starburst cells.

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Section: Passive Cable Modelmentioning

confidence: 99%

Direction selectivity in retinal bipolar cell axon terminals

Matsumoto

Agbariah

Nolte

et al. 2021

Neuron

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“…We applied the Sequential Neural Posterior Estimation method described in (Lueckmann et al, 2017) (code available at https://github.com/mackelab/delfi) (also called SNPE-B) with some modifications which were also applied in (Oesterle et al, 2020; Yoshimatsu et al, 2020a). In brief, SNPE-B draws over several rounds samples { θi} iϵl from a prior and evaluates the model for these parameters.…”

Section: Methodsmentioning

confidence: 99%

“…Parameter Inference. We applied the Sequential Neural Posterior Estimation method described in (Lueckmann et al, 2017) (code available at https://github.com/mackelab/delfi) (also called SNPE-B) with some modifications which were also applied in (Oesterle et al, 2020;Yoshimatsu et al, 2020a We took the same approach for setting an adaptive bandwidth for the kernel. As an additional post-hoc verification of the posteriors, we took as final posterior distributions the posterior of the round with the smallest median discrepancy of its samples ("early stopping").…”

Section: Data Indices Maximal Activation Sustain and Transiencementioning

confidence: 99%

Distinct Synaptic Transfer Functions in Same-Type Photoreceptors

Schröder

Oesterle

Berens

et al. 2021

Preprint

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Summary. 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″ 2-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 specialized mechanisms which are advantageous for the encoding of different visual features.

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“…These methods have also been applied to study the evolutionary dynamics of protein networks [173] and in yeast strains [174]. In neuroscience, they have, e.g., been used to estimate the properties of ion channels from high-throughput voltage-clamp, and properties of neural circuits from observed rhythmic behaviour in the stomatogastric ganglion [133], to identify network models which can capture the dynamics of neuronal cultures [175], to study how the connectivity between different cell-types shapes dynamics in cortical circuits [176], and to identify biophysically realistic models of neurons in the retina [177,178].…”

Section: Examplesmentioning

confidence: 99%

Simulation Intelligence: Towards a New Generation of Scientific Methods

Lavin¹,

Zenil²,

Krakauer³

et al. 2021

Preprint

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The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. 1 We present the Nine Motifs of Simulation Intelligence, a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We call this merger simulation intelligence (SI), for short. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. Using this metaphor, we explore the nature of each layer of the simulation intelligence "operating system" stack (SI-stack) and the motifs therein:1. Multi-physics and multi-scale modeling 2. Surrogate modeling and emulation 3. Simulation-based inference 4. Causal modeling and inference 5. Agent-based modeling 6. Probabilistic programming 7. Differentiable programming 8. Open-ended optimization Machine programmingWe believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery, from solving inverse problems in synthetic biology and climate science, to directing nuclear energy experiments and predicting emergent behavior in socioeconomic settings. We elaborate on each layer of the SI-stack, detailing the state-of-art methods, presenting examples to highlight challenges and opportunities, and advocating for specific ways to advance the motifs and the synergies from their combinations. Advancing and integrating these technologies can enable a robust and efficient hypothesis-simulation-analysis type of scientific method, which we introduce with several use-cases for human-machine teaming and automated science.

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Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics

Cited by 22 publications

References 110 publications

Direction selectivity in retinal bipolar cell axon terminals

Direction selectivity in retinal bipolar cell axon terminals

Distinct Synaptic Transfer Functions in Same-Type Photoreceptors

Simulation Intelligence: Towards a New Generation of Scientific Methods

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