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Martin-Burgos B, McPherson TS, Hammonds R, Gao R, Muotri AR, Voytek B. Development of neuronal timescales in human cortical organoids and rat hippocampus dissociated cultures. J Neurophysiol 2024; 132:757-764. [PMID: 39015071 DOI: 10.1152/jn.00135.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 06/04/2024] [Accepted: 06/24/2024] [Indexed: 07/18/2024] Open
Abstract
To support complex cognition, neuronal circuits must integrate information across multiple temporal scales, ranging from milliseconds to decades. Neuronal timescales describe the duration over which activity within a network persists, posing a putative explanatory mechanism for how information might be integrated over multiple temporal scales. Little is known about how timescales develop in human neural circuits or other model systems, limiting insight into how the functional dynamics necessary for cognition emerge. In our work, we show that neuronal timescales develop in a nonlinear fashion in human cortical organoids, which is partially replicated in dissociated rat hippocampus cultures. We use spectral parameterization of spiking activity to extract an estimate of neuronal timescale that is unbiased by coevolving oscillations. Cortical organoid timescales begin to increase around month 6 postdifferentiation. In rodent hippocampal dissociated cultures, we see that timescales decrease from in vitro days 13-23 before stabilizing. We speculate that cortical organoid development over the duration studied here reflects an earlier stage of a generalized developmental timeline in contrast to the rodent hippocampal cultures, potentially accounting for differences in timescale developmental trajectories. The fluctuation of timescales might be an important developmental feature that reflects the changing complexity and information capacity in developing neuronal circuits.NEW & NOTEWORTHY Neuronal timescales describe the persistence of activity within a network of neurons. Timescales were found to fluctuate with development in two model systems. In cortical organoids timescales increased, peaked, and then decreased throughout development; in rat hippocampal dissociated cultures timescales decreased over development. These distinct developmental models overlap to highlight a critical window in which timescales lengthen and contract, potentially indexing changes in the information capacity of neuronal systems.
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Affiliation(s)
- Blanca Martin-Burgos
- Neurosciences Graduate Program, University of California, San Diego, La Jolla, California, United States
| | - Trevor Supan McPherson
- Neurosciences Graduate Program, University of California, San Diego, La Jolla, California, United States
| | - Ryan Hammonds
- Department of Cognitive Science, University of California, San Diego, La Jolla, California, United States
- Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, California, United States
| | - Richard Gao
- Department of Cognitive Science, University of California, San Diego, La Jolla, California, United States
| | - Alysson R Muotri
- Department of Pediatrics/Rady Children's Hospital San Diego, School of Medicine, University of California, San Diego, La Jolla, California, United States
- Department of Cellular & Molecular Medicine, School of Medicine, University of California, San Diego, La Jolla, California, United States
| | - Bradley Voytek
- Department of Cognitive Science, University of California, San Diego, La Jolla, California, United States
- Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, California, United States
- Neurosciences Graduate Program, University of California, San Diego, La Jolla, California, United States
- Kavli Institute for Brain and Mind, University of California, San Diego, La Jolla, California, United States
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Kosik KS. Why brain organoids are not conscious yet. PATTERNS (NEW YORK, N.Y.) 2024; 5:101011. [PMID: 39233695 PMCID: PMC11368692 DOI: 10.1016/j.patter.2024.101011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/06/2024]
Abstract
Rapid advances in human brain organoid technologies have prompted the question of their consciousness. Although brain organoids resemble many facets of the brain, their shortcomings strongly suggest that they do not fit any of the operational definitions of consciousness. As organoids gain internal processing systems through statistical learning and closed loop algorithms, interact with the external world, and become embodied through fusion with other organ systems, questions of biosynthetic consciousness will arise.
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Affiliation(s)
- Kenneth S. Kosik
- Neuroscience Research Institute and Department of Molecular Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA, USA
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Spaeth A, Haussler D, Teodorescu M. Model-Agnostic Neural Mean Field With The Refractory SoftPlus Transfer Function. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.05.579047. [PMID: 38370695 PMCID: PMC10871173 DOI: 10.1101/2024.02.05.579047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
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.
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Affiliation(s)
- Alex Spaeth
- Electrical and Computer Engineering Department, University of California, Santa Cruz, Santa Cruz, CA, United States
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, United States
| | - David Haussler
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, United States
- Biomolecular Engineering Department, University of California, Santa Cruz, Santa Cruz, CA, United States
| | - Mircea Teodorescu
- Electrical and Computer Engineering Department, University of California, Santa Cruz, Santa Cruz, CA, United States
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, United States
- Biomolecular Engineering Department, University of California, Santa Cruz, Santa Cruz, CA, United States
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