Biological Neural Network Model
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A Biological Neural Network Model is a neural network model for a biological neural network that captures the structure, connectivity, or dynamics of neural systems in living organisms.
- Context:
- It can (often) be integrated into broader Neuroscience Frameworks for understanding Brain Function and Cognition.
- It can (often) be used to study the relationship between neural structure and function in the context of various cognitive or behavioral phenomena.
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- It can range from being a Single-Neuron Model that focuses on the properties of individual neurons to being a Network Model that examines large-scale connectivity patterns.
- It can range from representing a Local Circuit Model for small neural circuits (e.g., within a single cortical column) to representing a Global Network Model that maps the entire brain's connectivity.
- It can range from being a Data-Driven Model that relies on experimental observations to being a Theoretical Model that is built using assumptions and mathematical constructs.
- It can range from being a Deterministic Model that assumes a fixed set of properties and outcomes to being a Stochastic Model that incorporates variability and randomness in neural responses.
- It can range from being a Computationally Lightweight Model that focuses on simulating high-level dynamics to being a Computationally Intensive Model that simulates detailed neuron-to-neuron interactions.
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- It can represent the Neural Connectivity of an organism's nervous system by mapping the connections between neurons or brain regions.
- It can include data-driven models that reconstruct the Synaptic Architecture of the brain based on experimental data.
- It can leverage Computational Neuroscience methods to simulate and analyze the behavior of biological neural networks under various conditions.
- It can involve Dynamic Models that describe changes in neural connectivity over time, such as during learning, disease, or neurodevelopment.
- It can be a key component in developing Brain Simulation Platforms that aim to replicate human or animal brain networks.
- It can include Whole-Brain Models that simulate the complete neural activity of an organism, or Partial Models that focus on specific subsystems (e.g., the visual cortex).
- It can use Graph Theory and Network Analysis to evaluate properties like centrality, clustering, and modularity within neural networks.
- It can support research into Neuroplasticity by modeling how neural connections change with learning and experience.
- It can be used to study the impacts of Neurodegenerative Disorders by simulating abnormal connectivity patterns and their functional consequences.
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- Example(s):
- A Connectome for mapping the detailed neural connections within an organism’s nervous system.
- A Cortical Column Model that simulates the interactions between neurons within a small region of the cerebral cortex.
- A Hodgkin-Huxley Model that mathematically represents the electrical properties of individual neurons.
- A Neural Population Model for studying the collective behavior of groups of neurons within a brain region.
- A Spiking Neural Network Model that captures the timing of spikes in neural communication for more biologically accurate simulations.
- A Blue Brain Project Model, which aims to reconstruct and simulate the microcircuitry of the mammalian brain.
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- Counter-Example(s):
- A Deep Neural Network model that is purely artificial and not directly inspired by the structure or dynamics of biological neural systems.
- A Genetic Regulatory Network model that describes gene interactions rather than neural connectivity.
- A Behavioral Model that simulates organism behavior without representing neural structures.
- See: Connectome, Neural Network, Computational Neuroscience, Neuroscience Framework, Brain Function, Cognition.