Protosequences in human cortical organoids model intrinsic states in the developing cortex

van der Molen, Tjitse; Spaeth, Alex; Chini, Mattia; Bartram, Julian; Dendukuri, Aditya; Zhang, Zongren; Bhaskaran-Nair, Kiran; Blauvelt, Lon J.; Petzold, Linda R.; Hansma, Paul K.; Teodorescu, Mircea; Hierlemann, Andreas; Hengen, Keith B.; Hanganu-Opatz, Ileana L.; Kosik, Kenneth S.; Sharf, Tal

doi:10.1101/2023.12.29.573646

Cited by 4 publications

References 102 publications

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Model-agnostic neural mean field with a data-driven transfer function

Spaeth,

Haussler,

Teodorescu

2024

Neuromorph. Comput. Eng.

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As one of the most complex systems known to science, modeling brain behavior and function is both fascinating and extremely difficult. Empirical data is increasingly available from ex vivo human brain organoids and surgical samples, as well as in vivo animal models, so the problem of modeling the behavior of large-scale neuronal systems is more relevant than ever. The statistical physics concept of a mean-field model offers a tractable way to bridge the gap between single-neuron and population-level descriptions of neuronal activity, by modeling the behavior of a single representative neuron and extending this to the population. However, existing neural mean-field methods typically either take the limit of small interaction sizes, or are applicable only to the specific neuron models for which they were derived. This paper derives a mean-field model by fitting a transfer function called Refractory SoftPlus, which is simple yet applicable to a broad variety of neuron types. The transfer function is fitted numerically to simulated spike time data, and is entirely agnostic to the underlying neuronal dynamics. The resulting mean-field model predicts the response of a network of randomly connected neurons to a time-varying external stimulus with a high degree of accuracy. Furthermore, it enables an accurate approximate bifurcation analysis as a function of the level of recurrent input. This model does not assume large presynaptic rates or small postsynaptic potential size, allowing mean-field models to be developed even for populations with large interaction terms.

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Model-agnostic neural mean field with a data-driven transfer function

Spaeth,

Haussler,

Teodorescu

2024

Neuromorph. Comput. Eng.

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Model-Agnostic Neural Mean Field With The Refractory SoftPlus Transfer Function

Spaeth,

Haussler,

Teodorescu

2024

Preprint

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Due to the complexity of neuronal networks and the nonlinear dynamics of individual neurons, it is challenging to develop a systems-level model which is accurate enough to be useful yet tractable enough to apply. Mean-field models which extrapolate from single-neuron descriptions to large-scale models can be derived from the neuron’s transfer function, which gives its firing rate as a function of its synaptic input. However, analytically derived transfer functions are applicable only to the neurons and noise models from which they were originally derived. In recent work, approximate transfer functions have been empirically derived by fitting a sigmoidal curve, which imposes a maximum firing rate and applies only in the diffusion limit, restricting applications. In this paper, we propose an approximate transfer function called Refractory SoftPlus, which is simple yet applicable to a broad variety of neuron types. Refractory SoftPlus activation functions allow the derivation of simple empirically approximated mean-field models using simulation results, which enables prediction of the response of a network of randomly connected neurons to a time-varying external stimulus with a high degree of accuracy. These models also support an accurate approximate bifurcation analysis as a function of the level of recurrent input. Finally, the model works without assuming large presynaptic rates or small postsynaptic potential size, allowing mean-field models to be developed even for populations with large interaction terms.Author SummaryAs one of the most complex systems known to science, modeling brain behavior and function is both fascinating and extremely difficult. Empirical data is increasingly available fromex vivohuman brain organoids and surgical samples, as well asin vivoanimal models, so the problem of modeling the behavior of large-scale neuronal systems is more relevant than ever. The statistical physics concept of a mean-field model offers a tractable approach by modeling the behavior of a single representative neuron and extending this to the population. However, most mean-field models work only in the limit of weak interactions between neurons, where synaptic input behaves more like a diffusion process than the sum of discrete synaptic events. This paper introduces a data-driven mean-field model, estimated by curve-fitting a simple transfer function, which works with larger interaction strengths. The resulting model can predict population firing rates and bifurcations of equilibria, as well as providing a simple dynamical model that can be the basis for further analysis.

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Human Neural Organoid Microphysiological Systems Show the Building Blocks Necessary for Basic Learning and Memory

Alam El Din,

Moenkemoeller,

Loeffler

et al. 2024

Preprint

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Brain Microphysiological Systems including neural organoids derived from human induced pluripotent stem cells offer a unique lens to study the intricate workings of the human brain. This paper investigates the foundational elements of learning and memory in neural organoids, also known as Organoid Intelligence by quantifying immediate early gene expression, synaptic plasticity, neuronal network dynamics, and criticality to demonstrate the utility of these organoids in basic science research. Neural organoids showed synapse formation, glutamatergic and GABAergic receptor expression, immediate early gene expression basally and evoked, functional connectivity, criticality, and synaptic plasticity in response to theta-burst stimulation. In addition, pharmacological interventions on GABAergic and glutamatergic receptors, and input specific theta-burst stimulation further shed light on the capacity of neural organoids to mirror synaptic modulation and short-term potentiation, demonstrating their potential as tools for studying neurophysiological and neurological processes and informing therapeutic strategies for diseases.

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Protosequences in human cortical organoids model intrinsic states in the developing cortex

Cited by 4 publications

References 102 publications

Model-agnostic neural mean field with a data-driven transfer function

Model-agnostic neural mean field with a data-driven transfer function

Model-Agnostic Neural Mean Field With The Refractory SoftPlus Transfer Function

Human Neural Organoid Microphysiological Systems Show the Building Blocks Necessary for Basic Learning and Memory

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