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Neurons and Synapses (BB094) Summary of all lectures

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Course
Neurons & Synapses (BB094)

Institution
Radboud Universiteit Nijmegen (RU)

Summary of all lectures of the course Neurons and Synapses (BB094) that is given in the 2nd year of the bachelor. Great if you struggle with big chunks of text. Full of pictures and bullet points.

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Uploaded on September 23, 2022
Number of pages 51
Written in 2019/2020
Type Summary

neurons
synapses
neuro
biology

Institution
Radboud Universiteit Nijmegen (RU)
Education
Bachelor Biology
Course
Neurons & Synapses (BB094)

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Lecture 1 Types and classification of neurons in the nervous
system
There is not a single way to classify neurons. Therefore we do not actually know how many
different kinds of neurons are there in the brain.

We will mainly be looking at neurons and synapses (duh) but also the network structures
that they form.

Anatomical organization of neurons
Soma size, neuron density and axo-dendritic pattern of neurons vary throughout the nervous
system

Basic functional classification of neurons
● Excitatory neurons secrete neurotransmitters that cause membrane depolarization in
postsynaptic neurons
● Inhibitory neurons secrete neurotransmitters that cause membrane hyperpolarization
in postsynaptic neurons

Dale’s law (1934)
Version 1 - A neuron is either excitatory or inhibitory in its influence on other neurons
Version 2 - A neuron secretes a single (traditional) neurotransmitter at its synapses
Inhibition and excitation dynamics determine rules of computation and communication

Information used to classify neurons
● Electrical dynamics
● Structural information
● Functional information

Modern techniques allow detailed anatomical characterization of neurons
● Intracellularly filled neurons with biocytin
● Genetically targeted pyramidal neurons expressing green fluorescent protein
● Stochastic targeting of neurons throughout the brain
● Stochastic expression of many fluorescent proteins

Molecular labeling in whole brain provide biochemical insight to
neuronal diversity
The entire brain is made transparent by removing lipids. Then
fluorescent proteins are attached to DNA present in specific types
of neurons, colouring the whole brain. In this picture
green/yellow/red are different neurons and blue are glial cells.

1

,Neurons do not only vary in terms of anatomy

How can one classify neurons?
Basic features commonly used for neuronal classification:
● Morphological features
● Biochemical markers expression
● Active and passive electrical characteristics

Morphological features
Most neurons contain three major “compartments”
● Soma - Contacted only by few (inhibitory) synapses; metabolic center of the cell.
● Dendrites - Most synapses are made onto dendrites. The principal compartment for
spatial and temporal integration of incoming information.
● Axon - Efferent part of the cell. Axonal hillock in the initial segment is where action
potential is generated. Initial segment targeted by specific inhibitory synapses.

Study of single neuron morphology

2

,Axonal projection patterns
● Bouton distribution reveals whether region is
transversed - no granulation or innervated - high
density
● Neurons can establish 1000s of boutons in a layer
specific distribution

Blue = dendrites, Red = axons

● Total axonal length [µm]
● Number of branching points [n]
● Number of endings [n]
● Branching pattern (Sholl analysis)
● Total No. of boutons [n]
● Bouton density [n / 100 µm]
● Layer specific bouton distribution

Based on biochemical markers
Typically used for classification of inhibitory neurons only,
because inhibitory neurons have a high variability in gene
expression. Excitatory neurons typically express layer (i.e.
laminae) specific proteins

The problem: Poor correlation between morphological and
biochemical markers. Classification remains controversial.

How many different classes of neurons are there in the cerebral cortex?

Excitatory neurons
By morphology Inhibitory neurons
At least 3 main classes: Spiny stellate, By morphology
star pyramidal and pyramidal cells At least 8
Number of subclasses depends on the By biochemical markers
cortical area At least 7
By biochemical markers
Each layer possesses at least one unique
subclass (up to low tens) - depends on
cortical area
E.g in the primary somatosensory cortex,
i.e. 11

Based on electrical properties
● Resting membrane potential
○ Electrical potential across the membrane at rest, i.e. in the absence of
synaptic input
● Membrane resistance

3

, ○Calculated as V/I (remember the Ohm’s law). It indicates how sensitive a
neuron might be upon excitation.
● Membrane time constant
○ i.e. time needed to reach 63% of voltage change (indicates how fast a neuron
might react to excitation)

Active electrical characteristics of neurons
● Action potential rate, timing, interval
● Rate of adaptation upon sustained current injection
● Membrane potential at which action potential is generated
● Amplitude of subthreshold responses (e.g. after hyperpolarization)

Excitatory neurons display two main firing patterns
● Regular spiking neurons (RS) Interspike interval
relatively remains constant upon sustained somatic
depolarizing current injection
● Intrinsically burst spiking neurons (IB) Rapid sequence of
action potential upon transient current injection.

Functional advantages of burst spiking
● Bursts are more reliable than single spikes in evoking
postsynaptic neuronal responses.
● Bursts overcome synaptic transmission failure.
● Bursts facilitate transmitters in the short-term release
whereas single spikes do not (i.e. short-term facilitation).
● Bursts evoke long-term potentiation and hence affect synaptic plasticity much
greater, or differently than single spikes.

Electrical characterization of inhibitory neurons
LTS: Low threshold spiking
FS: Fast spiking
LS: Late spiking

Fast spiking neurons respond rapidly once reaching
threshold
● Contributes to rapid truncation of excitatory
network activity (important for epilepsy)
Late spiking need strong and lasting excitatory drive
● Modulates the ongoing network activity

Currently there is not an agreed upon classification
approach that takes all the variability across variables into
account

4

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