Intro to Neurons in Computational Neuroscience

Jan 8, 2025

Lecture 1: Introduction to Neurons - Computational Neuroscience

Course Information

  • Course: Computational Neuroscience, Systems 552, Biology 487
  • University: University of Waterloo, Winter 2021

Lecture Focus

  • Introduction to Neurons
  • Focus on scale: from neurons to molecules
  • Next lecture: Central Nervous System

Readings

  • Main Reading: Chapter 2 of Kendall et al.
  • Optional: Additional readings for more detail

Understanding Neurons

Historical Perspective

  • Aristotle's View: Brain as a radiator for cooling blood (incorrect)
  • Modern Neuroscience: Began in early 1900s
    • Research by Ramoni Cahal - Pioneering neuron diagrams through brain slices and selective staining

Neuron Structure

  • Diversity in Neurons: Varied shapes and sizes, but common features can be classified
    • Example: Henry Markham et al.'s extensive work on neuron classification
  • 3D Neuron Imaging: Advancements in reconstructing neuron 3D structures

Neuron vs. Glial Cells

  • Focus on neurons in this course
  • Glial cells (e.g., Schwann cells) provide insulation, nutrition, and neurotransmitter recycling
  • Possible computation role but less controversial focus on neurons

Common Neuron Structure

Physical Structure

  • Dendrites: Branching structures receiving inputs
  • Cell Body (Soma): Contains cell machinery
  • Axon: Long extension transmitting output to other neurons
  • Synapse: Connection point for transmitting information

Functionality

  • Inputs: Typically from other neurons or sensory signals
  • Outputs: Typically to other neurons, sometimes to muscles

Neuron Functionality

Action Potential (Spike)

  • Resting Potential: ~-70 millivolts
  • Threshold for Action Potential: ~-55 millivolts
  • Spike Characteristics:
    • Spike shape is consistent; information conveyed by spike timing
    • Positive feedback loop causes voltage to shoot up, then recover

Variability in Neuron Responses

  • Neurons can exhibit different spiking patterns for the same input
  • Examples:
    • Regular spiking, burst firing, single spike

Synaptic Transmission

Neurotransmitter Release

  • Spike causes neurotransmitter release at synapse
  • Neurotransmitter influences next neuron

Ion Channels and Electrical Signaling

  • Ion Channels: Proteins in neuron membrane that allow ion flow
  • Ligand-Gated Channels: Opened by neurotransmitters
  • Voltage-Gated Channels: Opened by changes in voltage

Computational Modeling

Circuit Model

  • Neuron modeled as an electrical system: capacitors, resistors, current sources
  • Used for computational models of neurons

Projects and Applications

  • Modify existing computational models
  • Explore effects of synaptic release, axon structure, recovery dynamics on neuron behavior

Conclusion

  • Neurons are diverse but have common functional components
  • Spikes are key information carriers, not spike shape
  • Follow-up reading: Chapters 5-8 for more on membranes, action potentials, and synapses

Next Lecture

  • Focus on connecting neurons to form whole brains
  • Explore complexity of brain systems

Note: For detailed study, refer to Chapter 2 of Candle It All. Further reading involves chapters 5-8 for deeper insights into neuronal structures and functions.