Computational Neuroscience
- [Greece - Jimmy Teoh]
- Overview
Computational neuroscience (CNS) (also known as theoretical neuroscience or mathematical neuroscience) is a branch of neuroscience that uses mathematics, computer science, theoretical analysis, and brain abstraction to understand the processes that control the development, structure, physiology, and cognitive abilities of the nervous system.
- Methods: Researchers use mathematical models, computer simulations, and statistical analyses to study the brain, nervous system, and behavior.
- Topics: Researchers ask questions about how the brain stores memories, how it makes decisions, and how it processes information.
- Approaches: Researchers use a variety of approaches, including creating neural hybrid systems that combine biological neurons with computer-simulated components.
- Background: Researchers in this field come from a range of backgrounds, including biology, psychology, physics, mathematics, statistics, computer science, and engineering.
- Education: Most computational neuroscientists have a Ph.D.
CNS employs computational simulations to validate and solve mathematical models, and so can be seen as a sub-field of theoretical neuroscience; however, the two fields are often synonymous. The term mathematical neuroscience is also used sometimes, to stress the quantitative nature of the field.
CNS employs computational simulations to validate and solve mathematical models and can therefore be considered a subfield of theoretical neuroscience; however, the two fields are often synonymous. The term mathematical neuroscience is also sometimes used to emphasize the quantitative nature of the field.
- Collecting data
- Creating computer models based on the brain's electrical patterns and biological functions
- Using mathematical multi-scale models
- Performing theoretical analysis
- Simulations of neural function
Research areas in CNS include: Machine learning, Modeling, Artificial intelligence, Neural networks, Memory, Visual intelligence, Audition, Natural language processing, Learning theory.
- Artificial Intelligence (AI) and Neuroscience
AI and neuroscience have closely interacted and inspired each other since the beginning of AI. With the fast developments in deep learning over the last decade, AI has had a substantial impact on many applications in science and society.
Advances in efficient computing hardware, which are in turn inspired by insights from neuroscience will be instrumental in moving AI beyond narrow applications.
AI and neuroscience have a complex relationship:
- AI helps neuroscience: AI can simulate the brain, which helps neuroscientists test hypotheses and detect neurological disorders early. AI can also help with interpreting large datasets and build better models of the human brain.
- Neuroscience inspires AI: Neurosciences can inspire AI by developing neural networks that mimic the human brain structure. AI systems are modeled after the human brain, using neural networks as "virtual brains".
- Cognitive science: Cognitive science is a field that combines AI, neuroscience, computer science, and other disciplines to study the human mind.
AI has many uses in neurology, including:
- Accurate epilepsy diagnosis
- Brain mapping
- Surgical planning
- Enhancing the diagnosis and treatment of brain diseases
- [Hallstatt, Austria]
- The Brain's Neural Network vs. Artificial Neural Networks
While both the brain's neural network and artificial neural networks (ANNs) share the concept of interconnected nodes processing information, the key difference lies in the complexity, scale, and biological mechanisms of the brain's network compared to the simplified, computationally designed structure of ANNs, which are significantly smaller, operate at a faster speed, and use different learning algorithms to adapt to data; essentially, the brain's network is far more intricate and nuanced than current ANNs, even though they are inspired by its structure.
Key points of comparison:
- Complexity: The human brain contains billions of neurons with trillions of connections, creating a highly complex network with diverse neuron types and intricate pathways, while artificial neural networks typically have a much smaller number of nodes and simpler connections.
- Signal transmission: Biological neurons communicate through electrical impulses called action potentials, which are "all-or-none" signals, whereas artificial neurons typically use continuous values for signal transmission.
- Learning mechanisms: The brain's learning process is not fully understood but likely involves complex interactions between neurons, including synaptic plasticity, while artificial neural networks primarily use supervised learning algorithms like backpropagation to adjust connection weights.
- Energy efficiency: The brain operates on relatively low power consumption despite its complexity, whereas ANNs currently require significantly more energy to perform computations.
- Adaptability: While the brain can continuously learn and adapt to new situations throughout life, most artificial neural networks are designed for specific tasks and may require retraining to learn new information.
Similarities:
- Basic structure: Both biological and ANNs consist of interconnected nodes (neurons) that receive input signals, process information, and send outputs to other nodes.
- Parallel processing: Both systems can process information in parallel across multiple connections, allowing for efficient computation.
- Inspiration: ANNs are designed to mimic the basic computational principles observed in the brain's neural network.
In ANNs, an external algorithm tries to modify synaptic connections in order to reduce error, whereas the researchers propose that the human brain first settles the activity of neurons into an optimal balanced configuration before adjusting synaptic connections.
