Introduction to the Brain and Nervous System

Welcome Remarks
- Good Morning, everyone.
- Final lecture of the module: Introduction to the Brain and Nervous System.
- Today will continue from where part one ended on Monday, discussing neural activity and networks.

Neural Activity and Networks

Neurons' Function
- Neurons communicate with each other to perform specific functions.
- Important to distinguish:
  - What neurons do: communicate.
  - How they accomplish tasks: through networks.
Neural Circuits and Reflexes
- Example of the knee-jerk (patellar) reflex:
  - Simple neural circuit demonstrating how reflex actions occur.
- Computational Neural Networks
  - Design of networks that perform logical functions (e.g., XOR).
  - Input neurons fire based on conditions provided by other neurons.
  - Excitatory and inhibitory signals influence network behavior.

How Neural Networks Scale Up

Formation of Larger Networks
- Simple arrangements allow formation of more complex networks leading to functions like thinking and feeling.
- Introduced concept of Connectionism in the 1980s:
  - Focused on types of connections neurons form.
  - Evolved into neural network models/computational neuroscience.
- With time, network models have become increasingly complex.
Connectionist Model for Reading
- Example model from the 1980s that simulates how we read:
  - Input layer features detect specific characteristics (e.g., lines).
  - Neurons at hidden layers represent components like letters.
  - Connections illustrated:
    - Excitatory signals lead to activation of relevant letters.
    - Inhibitory signals reduce activation of irrelevant words.
- This simplified model indicates:
  - Handling of words disregards position (e.g., distinguishes "god" from "dog").

Neural Network Models and Real Brain Function

Model Limitations
- Validating models involves placing them in new scenarios to compare behavior with human brain responses.
- Computational neuroscientists may focus more on function rather than replicating the brain.
Complexity of Connectionist Models
- Models can become intricately connected, challenging to interpret function.
- Example model deemed extremely complex and potentially limited.

The Human Brain Project

Overview
- Launched in 2014, awarded €500 million by the EU.
- Led by Professor Henry Markram; aimed to simulate an entire human brain.
Challenges
- Many experts believe full simulation is not currently feasible.
- Questions raised about practical value of simulating an entire brain.
- Emphasis on abstraction in modeling for insights on functions.

Neural Network Exercise

Exercise Challenge
- Create a neural network model with three input neurons (a, b, c) that outputs a signal if at least two neurons fire but not if all three fire.
- Reference to existing solutions posted as learning tools.

Myths about the Brain

Myth: The Brain is Like a Computer
- The idea of the brain functioning as a digital computer is complicated.
- Similarities and Differences:
  - Neurons perform computations; distinctions between neural computation vs machine computation:
    - Computers are digital (0s and 1s).
    - Neural computation: analog and digital properties.
      - Example: action potentials are binary (all or none).
      - Neurotransmitter release may vary (analog).
Concept of CPU
- Computers operate through a central processing unit (CPU); serial processing of data.
- Brains operate via billions of neurons/network connections working in parallel.
- Implications for task performance between the two systems.

Comparisons Between Computers and Brains

Computational Advantages
- Computers excel in fast, accurate, algorithmic tasks.
- Human brains are slower but better at complex, ambiguous tasks such as language interpretation.
- Example:
  - Simple arithmetic is much faster for computers than humans.
  - Tasks like species identification rely more on human judgement.
- Shift toward neural networks in AI development improving performance.

Additional Myths

Myth: The Right Brain is Artistic and the Left Brain is Logical
- Simplistic categorization of brain hemispheres.
- Existences of differences in functions, particularly regarding language.
  - Majority of language processing and logic lie in the left hemisphere.
- Importance of executive communication between both hemispheres via the corpus callosum.
Myth: We Only Use 10% of Our Brain
- Absolutely false; we use all of our brain.
- Periodic activation of neurons leads to metabolic balance.
- Misunderstanding of functionality leads to this common belief.

Overview of the Nervous System

Components
- Central Nervous System (CNS) versus Peripheral Nervous System (PNS).
- PNS further divides into Somatic and Autonomic Nervous Systems.
Somatic Nervous System
- Controls voluntary movements; connects nerves to muscles leading to observable behaviors.
Autonomic Nervous System
- Regulates internal functions and states of arousal.
- Comprised of Sympathetic and Parasympathetic branches.
  - Sympathetic: increase arousal (fight or flight).
  - Parasympathetic: reduce arousal (rest and digest).
Therapeutic Uses of Polygraphs
- Used as physiological arousal measurement, not lie detection.
- Measures heart rate, blood pressure, breathing rate, and skin conductance.

Understanding the Spinal Cord

Spinal Cord Overview
- Key structures and function discussed.
- Functions of sensory (dorsal) and motor (ventral) roots.
Clinical Cases for Understanding
- Patient examples to analyze spinal cord damage:
  - Different symptoms based on damage locations (e.g., sensation vs movement loss).
- Understanding of sensory/motor pathways critical for diagnosis.

Brain Functions and Structures

Representation of Body Functions
- Different parts of the brain are specialized for specific functions.
  - Motor cortex for movement; sensory cortex for touch.
- Importance of structural representation indicating bodily awareness.
Functional Specialization of Brain Regions
- Frontal lobe: higher order functions (planning, impulse control).
- Parietal lobe: spatial representations and numerical understanding.
- Occipital lobe: vision processing.
- Temporal lobe: language and memory systems.

Summary

End of Lecture
- Discussion of myths and misinterpretations regarding brain functionality.
- Summary and review of content covered in the module.
- Best wishes for assessment and practical future application of knowledge.