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Eckmann S, Young EJ, Gjorgjieva J. Synapse-type-specific competitive Hebbian learning forms functional recurrent networks. Proc Natl Acad Sci U S A 2024; 121:e2305326121. [PMID: 38870059 PMCID: PMC11194505 DOI: 10.1073/pnas.2305326121] [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: 04/04/2023] [Accepted: 04/25/2024] [Indexed: 06/15/2024] Open
Abstract
Cortical networks exhibit complex stimulus-response patterns that are based on specific recurrent interactions between neurons. For example, the balance between excitatory and inhibitory currents has been identified as a central component of cortical computations. However, it remains unclear how the required synaptic connectivity can emerge in developing circuits where synapses between excitatory and inhibitory neurons are simultaneously plastic. Using theory and modeling, we propose that a wide range of cortical response properties can arise from a single plasticity paradigm that acts simultaneously at all excitatory and inhibitory connections-Hebbian learning that is stabilized by the synapse-type-specific competition for a limited supply of synaptic resources. In plastic recurrent circuits, this competition enables the formation and decorrelation of inhibition-balanced receptive fields. Networks develop an assembly structure with stronger synaptic connections between similarly tuned excitatory and inhibitory neurons and exhibit response normalization and orientation-specific center-surround suppression, reflecting the stimulus statistics during training. These results demonstrate how neurons can self-organize into functional networks and suggest an essential role for synapse-type-specific competitive learning in the development of cortical circuits.
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Affiliation(s)
- Samuel Eckmann
- Computation in Neural Circuits Group, Max Planck Institute for Brain Research, Frankfurt am Main60438, Germany
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, CambridgeCB2 1PZ, United Kingdom
| | - Edward James Young
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, CambridgeCB2 1PZ, United Kingdom
| | - Julijana Gjorgjieva
- Computation in Neural Circuits Group, Max Planck Institute for Brain Research, Frankfurt am Main60438, Germany
- School of Life Sciences, Technical University Munich, Freising85354, Germany
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Jung T, Zeng N, Fabbri JD, Eichler G, Li Z, Willeke K, Wingel KE, Dubey A, Huq R, Sharma M, Hu Y, Ramakrishnan G, Tien K, Mantovani P, Parihar A, Yin H, Oswalt D, Misdorp A, Uguz I, Shinn T, Rodriguez GJ, Nealley C, Gonzales I, Roukes M, Knecht J, Yoshor D, Canoll P, Spinazzi E, Carloni LP, Pesaran B, Patel S, Youngerman B, Cotton RJ, Tolias A, Shepard KL. Stable, chronic in-vivo recordings from a fully wireless subdural-contained 65,536-electrode brain-computer interface device. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.17.594333. [PMID: 38798494 PMCID: PMC11118429 DOI: 10.1101/2024.05.17.594333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Minimally invasive, high-bandwidth brain-computer-interface (BCI) devices can revolutionize human applications. With orders-of-magnitude improvements in volumetric efficiency over other BCI technologies, we developed a 50-μm-thick, mechanically flexible micro-electrocorticography (μECoG) BCI, integrating 256×256 electrodes, signal processing, data telemetry, and wireless powering on a single complementary metal-oxide-semiconductor (CMOS) substrate containing 65,536 recording and 16,384 stimulation channels, from which we can simultaneously record up to 1024 channels at a given time. Fully implanted below the dura, our chip is wirelessly powered, communicating bi-directionally with an external relay station outside the body. We demonstrated chronic, reliable recordings for up to two weeks in pigs and up to two months in behaving non-human primates from somatosensory, motor, and visual cortices, decoding brain signals at high spatiotemporal resolution.
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Wang EY, Fahey PG, Ponder K, Ding Z, Chang A, Muhammad T, Patel S, Ding Z, Tran D, Fu J, Papadopoulos S, Franke K, Ecker AS, Reimer J, Pitkow X, Sinz FH, Tolias AS. Towards a Foundation Model of the Mouse Visual Cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.21.533548. [PMID: 36993435 PMCID: PMC10055288 DOI: 10.1101/2023.03.21.533548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Understanding the brain's perception algorithm is a highly intricate problem, as the inherent complexity of sensory inputs and the brain's nonlinear processing make characterizing sensory representations difficult. Recent studies have shown that functional models-capable of predicting large-scale neuronal activity in response to arbitrary sensory input-can be powerful tools for characterizing neuronal representations by enabling high-throughput in silico experiments. However, accurately modeling responses to dynamic and ecologically relevant inputs like videos remains challenging, particularly when generalizing to new stimulus domains outside the training distribution. Inspired by recent breakthroughs in artificial intelligence, where foundation models-trained on vast quantities of data-have demonstrated remarkable capabilities and generalization, we developed a "foundation model" of the mouse visual cortex: a deep neural network trained on large amounts of neuronal responses to ecological videos from multiple visual cortical areas and mice. The model accurately predicted neuronal responses not only to natural videos but also to various new stimulus domains, such as coherent moving dots and noise patterns, underscoring its generalization abilities. The foundation model could also be adapted to new mice with minimal natural movie training data. We applied the foundation model to the MICrONS dataset: a study of the brain that integrates structure with function at unprecedented scale, containing nanometer-scale morphology, connectivity with >500,000,000 synapses, and function of >70,000 neurons within a ~1mm3 volume spanning multiple areas of the mouse visual cortex. This accurate functional model of the MICrONS data opens the possibility for a systematic characterization of the relationship between circuit structure and function. By precisely capturing the response properties of the visual cortex and generalizing to new stimulus domains and mice, foundation models can pave the way for a deeper understanding of visual computation.
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Affiliation(s)
- Eric Y Wang
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Paul G Fahey
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Kayla Ponder
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Zhuokun Ding
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Andersen Chang
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Taliah Muhammad
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Saumil Patel
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Zhiwei Ding
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Dat Tran
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Jiakun Fu
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Stelios Papadopoulos
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Katrin Franke
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Alexander S Ecker
- Institute for Computer Science, University Göttingen, Göttingen, Germany
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - Jacob Reimer
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Xaq Pitkow
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Fabian H Sinz
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
- Institute for Computer Science, University Göttingen, Göttingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Germany
| | - Andreas S Tolias
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
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