Summary: Researchers have developed the most detailed 3D computational models of key brain regions, including the hippocampus and sensory cortices, to better understand their roles in memory formation and connectivity. These models integrate anatomical and physiological data, capturing synaptic plasticity and long-range interactions. By simulating brain activity, the models enable predictions about cortical processing and provide tools for future experimental validation.
They are openly accessible to the scientific community for further research and refinement. Insights from the models reveal how connectivity shapes complex brain networks and how learning occurs through synaptic plasticity in realistic conditions. This work paves the way for studying phenomena ranging from neural coding to the impacts of specific neurotransmitters.
Key Facts:
- Researchers created 3D models integrating data on anatomy, connectivity, and physiology of the hippocampus and sensory cortices.
- The models reveal how connectivity patterns form structured brain networks and enable learning through synaptic plasticity.
- Accessible on a public platform, the models support global research and experimental validation.
Source: EPFL
The hippocampus is one of the most fascinating brain regions. Associated with the formation of memories, it also helps us to navigate through the world without getting lost.
Sensory cortices on the other hand play an important role in how we perceive our environment and make appropriate movements, and how our brains determine what to focus on and what to ignore.
While both regions have been extensively studied and many of their secrets revealed, there is still a lot we do not understand about them due to the high complexity of interacting parts, from individual synapses and the zoo of different neuron types, to the detailed connectivity rules between them.
To better our understanding, EPFL researchers have built detailed computational models of these regions.
Putting together the neurons comprising these regions and describing the rules of their interactions through computer code, they are able to simulate the brain activity in these regions and study the roles of each part in the concert of brain activity.
Unlike previous models, these models were built with the exact three-dimensional geometry of their corresponding brain region. This opens the door for future refinement and testing of the models with any new experimental data.
By focusing on building such general three-dimensional models, the models can be also used to explore a wide range of phenomena.
This is not an easy process. Describing the rules governing the regions and turning them into computer simulations required the input of the many experts that have found and know these rules. The researchers have therefore collaborated with over 80 colleagues from all over the world to develop the largest and most detailed models of these brain regions.
“The integration of data from multiple sources and collaboration among scientists are the strengths of these models, though they also presented challenges”, remarks Dr. Armando Romani, group leader of the Circuits groups at Blue Brain.
“By addressing these obstacles, the models have become more robust, adaptable, and accessible to a broader scientific community.”
They have now openly released the models to the scientific community along with the tools to study and use them. The models are described in four extensive papers that each focus on different aspects.
In Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part I, published in the journal eLife, the focus lies on the anatomy of the somatosensory regions and its connectivity.
Its main insight is that the shape of brain regions affects the structure of brain networks formed within and a description of how connectivity at different scales comes together to form highly complex patterns.
“We are sometimes used to thinking about local and long-range connectivity as separate systems”, notes Dr. Michael Reimann, group leader of the Connectomics groups at Blue Brain.
“It really surprised us to see how the systems interact to form these very structured types of network.”
Part II, published in eLife alongside the first paper, describes the physiology of the brain region and how it was modeled and validated at the synaptic, neuronal and network-level.
“This allowed us to make predictions about how particular components of the brain, such as specific connectivity patterns, contribute to observations about cortical processing made by our experimental colleagues,” explains lead researcher Dr. James Isbister.
“The model’s 3D geometry allows us to study communication between brain areas, and most interestingly, to recreate experiments combining complex laboratory methods such as optogenetics with approaches only possible in simulations, such as lesions between very specific populations”.
A third paper in eLife explains how the model was then improved further to include the process of synaptic plasticity, the fundamental mechanism that allows us to learn new information.
Its insights pertain to the complex rules that govern the processes that emerge when millions of synapses undergo plasticity under in vivo conditions – like in the living brain.
“For the longest time, simulations have focused on plasticity rules based on lab experiments, under artificial conditions”, lead researcher Dr. Andras Ecker points out. “We wanted to explore plasticity in detailed networks and in vivo.”
Finally, a fourth paper in PLOS Biology presents a comprehensive in silico model of the rat CA1 region, integrating diverse experimental data from synapse to network levels, including the Schaffer collaterals – key conduits for information transfer and synaptic plasticity in the hippocampal circuit – as well as the effects of the neurotransmitter acetylcholine .
“Each component was rigorously tested and validated, and we made all the input data, assumptions, and methodologies fully transparent” adds Dr. Romani.
“Now accessible on hippocampushub.eu, this model serves as a flexible tool for scientists, providing extensive analyses and an interface to support further hippocampal research.”
Three additional journal articles and three preprint manuscripts demonstrate the value of the models to the scientific community. In them, the models have been used to study inter-areal processing, the neural code, and the relation between neuron connectivity and activity.
Results of plasticity simulations were compared to electron microscopy data and a predicted motif effect on synapse strength was confirmed.
“We have long known that brain networks are complex and follow specific rules” explains lead researcher Dr. Egas Santander.
“The model allows us to begin to explore the reasons for those rules.”
About this brain mapping research news
Author: Emmanuel Barraud
Source: EPFL
Contact: Emmanuel Barraud – EPFL
Image: The image is credited to Neuroscience News
Original Research: Open access.
“Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part I: Anatomy” by Michael Reimann et al. eLife
Open access.
“Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part II: Physiology and Experimentation” by James Isbister et al. eLife
Open access.
“Assemblies, synapse clustering and network topology interact with plasticity to explain structure-function relationships of the cortical connectome” by Andras Ecker et al. eLife
Open access.
“Community-based reconstruction and simulation of a full-scale model of the rat hippocampus CA1 region” by Armando Romani et al. PLOS Biology
Abstract
Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part I: Anatomy
The function of the neocortex is fundamentally determined by its repeating microcircuit motif, but also by its rich, interregional connectivity.
We present a data-driven computational model of the anatomy of non-barrel primary somatosensory cortex of juvenile rat, integrating whole-brain scale data while providing cellular and subcellular specificity.
The model consists of 4.2 million morphologically detailed neurons, placed in a digital brain atlas. They are connected by 14.2 billion synapses, comprising local, mid-range and extrinsic connectivity.
We delineated the limits of determining connectivity from anatomy, finding that it reproduces targeting by Sst+ neurons, but requires additional specificity to reproduce targeting by PV+ and VIP+ interneurons.
Globally, connectivity was characterized by local clusters tied together through hub neurons in layer 5, demonstrating how local and interegional connectivity are complicit, inseparable networks.
The model is suitable for simulation-based studies, and a 211,712 neuron subvolume is made openly available to the community.
Abstract
Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part II: Physiology and Experimentation
Cortical dynamics underlie many cognitive processes and emerge from complex multi-scale interactions, which are challenging to study in vivo. Large-scale, biophysically detailed models offer a tool which can complement laboratory approaches.
We present a model comprising eight somatosensory cortex subregions, 4.2 million morphological and electrically-detailed neurons, and 13.2 billion local and mid-range synapses.
In silico tools enabled reproduction and extension of complex laboratory experiments under a single parameterization, providing strong validation. The model reproduced millisecond-precise stimulus-responses, stimulus-encoding under targeted optogenetic activation, and selective propagation of stimulus-evoked activity to downstream areas.
The model’s direct correspondence with biology generated predictions about how multiscale organization shapes activity; for example, how cortical activity is shaped by high-dimensional connectivity motifs in local and mid-range connectivity, and spatial targeting rules by inhibitory subpopulations.
The latter was facilitated using a rewired connectome which included specific targeting rules observed for different inhibitory neuron types in electron microscopy.
The model also predicted the role of inhibitory interneuron types and different layers in stimulus encoding. Simulation tools and a large subvolume of the model are made available to enable further community-driven improvement, validation and investigation.
Abstract
Assemblies, synapse clustering and network topology interact with plasticity to explain structure-function relationships of the cortical connectome
Synaptic plasticity underlies the brain’s ability to learn and adapt. While experiments in brain slices have revealed mechanisms and protocols for the induction of plasticity between pairs of neurons, how these synaptic changes are coordinated in biological neuronal networks to ensure the emergence of learning remains poorly understood.
Simulation and modeling have emerged as important tools to study learning in plastic networks, but have yet to achieve a scale that incorporates realistic network structure, active dendrites, and multi-synapse interactions, key determinants of synaptic plasticity.
To rise to this challenge, we endowed an existing large-scale cortical network model, incorporating data-constrained dendritic processing and multi-synaptic connections, with a calcium-based model of functional plasticity that captures the diversity of excitatory connections extrapolated to in vivo-like conditions.
This allowed us to study how dendrites and network structure interact with plasticity to shape stimulus representations at the microcircuit level.
In our simulations, plasticity acted sparsely and specifically, firing rates and weight distributions remained stable without additional homeostatic mechanisms.
At the circuit level, we found plasticity was driven by co-firing stimulus-evoked functional assemblies, spatial clustering of synapses on dendrites, and the topology of the network connectivity.
As a result of the plastic changes, the network became more reliable with more stimulus-specific responses. We confirmed our testable predictions in the MICrONS datasets, an openly available electron microscopic reconstruction of a large volume of cortical tissue.
Our results quantify at a large scale how the dendritic architecture and higher-order structure of cortical microcircuits play a central role in functional plasticity and provide a foundation for elucidating their role in learning.
Abstract
Community-based reconstruction and simulation of a full-scale model of the rat hippocampus CA1 region
The CA1 region of the hippocampus is one of the most studied regions of the rodent brain, thought to play an important role in cognitive functions such as memory and spatial navigation.
Despite a wealth of experimental data on its structure and function, it has been challenging to integrate information obtained from diverse experimental approaches.
To address this challenge, we present a community-based, full-scale in silico model of the rat CA1 that integrates a broad range of experimental data, from synapse to network, including the reconstruction of its principal afferents, the Schaffer collaterals, and a model of the effects that acetylcholine has on the system.
We tested and validated each model component and the final network model, and made input data, assumptions, and strategies explicit and transparent. The unique flexibility of the model allows scientists to potentially address a range of scientific questions.
In this article, we describe the methods used to set up simulations to reproduce in vitro and in vivo experiments. Among several applications in the article, we focus on theta rhythm, a prominent hippocampal oscillation associated with various behavioral correlates and use our computer model to reproduce experimental findings.
Finally, we make data, code, and model available through the hippocampushub.eu portal, which also provides an extensive set of analyses of the model and a user-friendly interface to facilitate adoption and usage.
This community-based model represents a valuable tool for integrating diverse experimental data and provides a foundation for further research into the complex workings of the hippocampal CA1 region.
