12.3 Neural networks and brain dynamics

Neural networks and brain dynamics are fascinating areas of study in biology. They explore how our brains process information and create complex behaviors. This topic dives into the intricate workings of neurons, synapses, and neural networks.

We'll look at how neurons communicate, form memories, and synchronize their activity. We'll also explore mathematical models that help us understand these processes. This knowledge is crucial for unraveling the mysteries of consciousness and cognition.

Neuronal Dynamics

Hodgkin-Huxley Model and Action Potential Generation

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• Hodgkin-Huxley model mathematical model describing the generation and propagation of action potentials in neurons based on the dynamics of ion channels (sodium and potassium) in the cell membrane
• Action potential brief, rapid change in the membrane potential of a neuron, typically lasting a few milliseconds, generated when the membrane potential reaches a threshold value
• Sodium (Na+) and potassium (K+) ion channels play a crucial role in the generation of action potentials
  • Voltage-gated sodium channels open rapidly when the membrane potential reaches the threshold, allowing an influx of Na+ ions and causing depolarization
  • Voltage-gated potassium channels open more slowly, allowing an efflux of K+ ions and causing repolarization
• After an action potential, the neuron enters a refractory period during which it cannot generate another action potential, allowing time for the ion concentrations to be restored by active transport mechanisms (sodium-potassium pump)

Synaptic Transmission and Neuronal Oscillations

• Synaptic transmission process by which neurons communicate with each other at specialized junctions called synapses
  • Presynaptic neuron releases neurotransmitters (chemical messengers) into the synaptic cleft
  • Neurotransmitters bind to receptors on the postsynaptic neuron, causing changes in its membrane potential (excitatory or inhibitory postsynaptic potentials)
• Neuronal oscillations rhythmic, repetitive patterns of neural activity that can occur at various frequencies (alpha, beta, gamma, theta, delta) and are associated with different brain states and functions
  • Oscillations can arise from the interactions between excitatory and inhibitory neurons in a network
  • Synchronization of neuronal oscillations across brain regions is thought to play a role in information processing, memory, and consciousness
• Firing rate models simplified mathematical models that describe the activity of a neuron or a population of neurons in terms of their average firing rate (number of action potentials per unit time) rather than the detailed dynamics of individual action potentials

Neural Network Phenomena

Synchronization and Hebbian Learning

• Synchronization phenomenon in which multiple neurons or neural populations exhibit coordinated, rhythmic firing patterns
  • Can occur locally within a brain region or across distant brain areas
  • Believed to play a role in information processing, memory, and attention
  • Examples: gamma oscillations associated with conscious perception, theta oscillations in the hippocampus during memory tasks
• Hebbian learning theory of synaptic plasticity based on the idea that "neurons that fire together, wire together"
  • When a presynaptic neuron repeatedly and persistently stimulates a postsynaptic neuron, the synaptic connection between them is strengthened (long-term potentiation, LTP)
  • Conversely, when the firing of the presynaptic and postsynaptic neurons is uncorrelated or weakly correlated, the synaptic connection may be weakened (long-term depression, LTD)
  • Hebbian learning is thought to underlie the formation of memory traces and the organization of neural networks during development and learning

Attractor Networks and Chaos in Neural Systems

• Attractor networks neural networks that exhibit stable patterns of activity (attractors) to which the network tends to converge over time
  • Point attractors: stable fixed points in the network's state space, representing static patterns of activity
  • Limit cycle attractors: closed loops in the state space, representing periodic or oscillatory patterns of activity
  • Attractor networks have been used to model memory storage, decision making, and pattern recognition
• Chaos in neural systems refers to the presence of complex, unpredictable, and sensitive dependence on initial conditions in the dynamics of neural networks
  • Chaotic dynamics can arise from the nonlinear interactions between neurons and the presence of feedback loops in the network
  • Chaotic neural networks exhibit rich, flexible, and adaptable behavior, which may be important for learning, creativity, and problem-solving
  • Example: the olfactory bulb exhibits chaotic dynamics, which may help in the discrimination and recognition of complex odors

Brain Structure

Connectome

• Connectome comprehensive map of the structural and functional connections between neurons and brain regions
• Structural connectome refers to the physical wiring of the brain, including the pattern of synaptic connections between neurons and the white matter tracts connecting different brain areas
  • Techniques used to map the structural connectome include diffusion tensor imaging (DTI) and electron microscopy
• Functional connectome refers to the pattern of statistical dependencies or correlations between the activity of different brain regions, often measured using functional magnetic resonance imaging (fMRI) or electroencephalography (EEG)
• Understanding the connectome is crucial for unraveling how the brain's structure gives rise to its complex functions and behavior, as well as for identifying the neural basis of brain disorders (connectopathies)
• Large-scale connectome projects, such as the Human Connectome Project and the BRAIN Initiative, aim to map the connectivity of the human brain at an unprecedented level of detail, providing insights into individual variability, development, and evolution