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Long-term plasticity induces sparse and specific synaptic changes in a biophysically detailed cortical model

Eilif Benjamin Muller, Michael Reimann, James Gonzalo King, Marwan Muhammad Ahmed Abdellah, Pramod Shivaji Kumbhar, András Ecker, Sirio Bolaños Puchet, James Bryden Isbister, Daniela Egas Santander, Jorge Blanco Alonso, Giuseppe Chindemi, Ioannis Magkanaris

Synaptic plasticity underlies the brain’s ability to learn and adapt. This process is often studied in small groups of neurons in vitro or indirectly through its effects on behavior in vivo. Due to the limitations of available experimental techniques, investigating synaptic plasticity at the micro-circuit level relies on simulation-based approaches. Although modeling studies provide valuable insights, they are usually limited in scale and generality. To overcome these limitations, we extended a previously published and validated large-scale cortical network model with a recently developed calcium-based model of functional plasticity between excitatory cells. We calibrated the network to mimic an in vivo state characterized by low synaptic release probability and low-rate asynchronous firing, and exposed it to ten different stimuli. We found that synaptic plasticity sparsely and specifically strengthened synapses forming spatial clusters on postsynaptic dendrites and those between populations of co-firing neurons, also known as cell assemblies: among 312 million synapses, only 5% experienced noticeable plasticity and cross-assembly synapses underwent three times more changes than average. Furthermore, as occasional large-amplitude potentiation was counteracted by more frequent synaptic depression, the network remained stable without explicitly modeling homeostatic plasticity. When comparing the network’s responses to the different stimuli before and after plasticity, we found that it became more stimulus-specific after plasticity, manifesting in prolonged activity after selected stimuli and more unique groups of neurons responding exclusively to a single pattern. Taken together, we present the first stable simulation of Hebbian plasticity without homeostatic terms at this level of detail and analyze the rules determining the sparse changes.

2023

Modeling and simulation of neocortical micro- and mesocircuitry. Part II: Physiology and experimentation

Matthias Wolf, Henry Markram, Felix Schürmann, Eilif Benjamin Muller, Srikanth Ramaswamy, Michael Reimann, Daniel Keller, Werner Alfons Hilda Van Geit, James Gonzalo King, Pramod Shivaji Kumbhar, Alexis Arnaudon, Jean-Denis Georges Emile Courcol, Rajnish Ranjan, Armando Romani, András Ecker, Michael Emiel Gevaert, Vishal Sood, Sirio Bolaños Puchet, James Bryden Isbister, Judit Planas Carbonell, Daniela Egas Santander, Maria Reva, Genrich Ivaska, Natali Barros Zulaica, Mustafa Anil Tuncel, Christoph Pokorny, Elvis Boci, Jorge Blanco Alonso, Aleksandra Zuzanna Teska, Darshan Mandge, Polina Litvak, Gianluca Ficarelli, Weina Ji, Giuseppe Chindemi, Christian Andreas Rössert, Omar Awile, Joni Henrikki Herttuainen, Samuel Lieven D. Lapere, Thomas Brice Delemontex, Tanguy Pierre Louis Damart, Alexander Dietz

In recent years, large-scale computational models of the cortex have emerged as a powerful way to study the multi-scale mechanisms of neural processing. However, due to computational costs and difficulty of parameterization, detailed biophysical reconstructions have so far been restricted to small volumes of tissue, where the study of macro- and meso-scale interactions that are central to cortical function is not possible. We describe here, and in a companion paper, an approach to address the scaling challenges and provide a model of multiple interacting cortical regions at a subcellular level of detail. The model consists of 4.2 million morphologically detailed neurons in 8 sub-regions and connected with 13.2 billion synapses through local and long-range connectivity. Its anatomical aspects are described in the companion paper; here, we introduce physiological models of neuronal activity and synaptic transmission that integrate a large number of literature sources and were built using previously published algorithms. Biological neuronal diversity was captured in 208 morpho-electrical neuron types, five types of synaptic short-term dynamics, and pathway-specificity of synaptic parameters. A representation of synaptic input from cortical regions not present in the model was added and efficiently calibrated to reference firing rates. The model exhibits a spectrum of dynamical states differing in the degree to which they are internally versus externally driven. We characterized which parts of the spectrum are compatible with available experimental data on layer-specific delays and amplitudes of responses to simple stimuli, and found an in vivo-like regime at the edge of a transition from asynchronous to synchronous spontaneous activity. We developed a rich set of simulation tools to recreate a diverse set of laboratory experiments in silico, providing further validation and demonstrating the utility of the model in a variety of paradigms. Finally, we found that the large spatial scale of the model, that incorporates multiple cortical regions, led to the emergence of multiple independent computational units interacting through long-range synaptic pathways. The model provides a framework for the continued integration of experimental findings, for challenging hypotheses and making testable predictions, and provides a foundation for further simulation-based studies of cortical processing and learning.

2023

Estimating the Readily-Releasable Vesicle Pool Size at Synaptic Connections in the Neocortex

Henry Markram, Eilif Benjamin Muller, Srikanth Ramaswamy, Rodrigo de Campos Perin, Natali Barros Zulaica, Giuseppe Chindemi

Previous studies based on the 'Quantal Model' for synaptic transmission suggest that neurotransmitter release is mediated by a single release site at individual synaptic contacts in the neocortex. However, recent studies seem to contradict this hypothesis and indicate that multi-vesicular release (MVR) could better explain the synaptic response variability observed in vitro. In this study we present a novel method to estimate the number of release sites per synapse, also known as the size of the readily releasable pool (N-RRP), from paired whole-cell recordings of connections between layer 5 thick tufted pyramidal cell (L5_TTPC) in the juvenile rat somatosensory cortex. Our approach extends the work of Loebel et al. (2009) by leveraging a recently published data-driven biophysical model of neocortical tissue. Using this approach, we estimated N-RRP to be between two to three for synaptic connections between L5_TTPCs. To constrain N-RRP values for other connections in the microcircuit, we developed and validated a generalization approach using published data on the coefficient of variation (CV) of the amplitudes of post-synaptic potentials (PSPs) from literature and comparing them against in silico experiments. Our study predicts that transmitter release at synaptic connections in the neocortex could be mediated by MVR and provides a data-driven approach to constrain the MVR model parameters in the microcircuit.

2019