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Real Neurons for Engineers (Lecture 2)

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Title: Real Neurons for Engineers (Lecture 2)


1
Real Neurons for Engineers (Lecture 2)
  • Harry R. Erwin, PhD
  • COMM2E
  • University of Sunderland

2
Take-Home Message
  • What real neurons compute
  • Phasic activity
  • Tonic activity
  • Interneural interaction
  • Plasticity
  • How they compute it
  • The role of channels
  • The role of neurotransmitters
  • The role of specialized synapses
  • The role of neuronal topology
  • The role of axonal delays

3
Resources
  • Shepherd, G., ed., 2004, The Synaptic
    Organization of the Brain, 5th edition, Oxford
    University Press.
  • Nicholls et al.
  • Kandel et al.
  • Koch, 2004, Biophysics of Computation, OUP.
  • Bower and Beeman, 1998, The Book of Genesis,
    second edition, TELOS, ISBN 0-387-94938-0
  • Rieke et al, 1999, Spikes Exploring the Neural
    Code, Bradford Books.
  • Churchland and Sejnowski, 1994, The Computational
    Brain, Bradford Books.

4
What is a Neuron?
  • A neuron is an excitable cell, like a muscle
    cell.
  • Neurons are very primitivefound in most animals.
  • Neurons operate by allowing ions to pass through
    their membranes. This changes ion concentrations
    and the potential across their membrane. The ions
    then function in various ways to cause changes in
    the neuron.
  • Bob will teach this. I will show you how to model
    it.

5
Ions and Cells
  • Sodium (Na)outside
  • Potassium (K)inside
  • Magnesium (Mg)blocks NMDA receptors
  • Chlorine (Cl-)plays various roles
  • Calcium (Ca)important in intercellular
    communication.
  • Most negative charges within neurons are bound to
    proteins and respond to membrane potential
    changes by moving a small distance.

6
Phasic activity
  • A neuron is called phasic if it responds to
    synaptic input by generating one or more action
    potentials in a short time.
  • In the extreme, a phasic neuron can serve as a
    coincidence detector. Such neurons tend to have
    very negative resting potentials and short time
    constants so that multiple synchronized inputs
    are needed to trigger them.

7
Tonic activity
  • A neuron is called tonic if it responds to
    activation by generating an extended sequence of
    action potentials or enters a state where action
    potentials are generated continuously.
  • Tonic neurons tend to have resting potentials
    near threshold and long time constants.
  • Neuromodulation (dopamine, 5HT, etc.) controls
    the state of these neurons.

8
Interneural Interaction
  • Neurons can interact via chemical and electrical
    synapses (gap junctions).
  • Interaction via electrical synapses allows a
    small network of neurons to respond to the
    activation of one neuron.
  • This can also prepare nearby neurons for
    follow-on activation.

9
Electrical synapses
  • Rare in the cortex
  • Common in the retina and in the basal ganglia
  • Unknown presence in the auditory system
  • Generally involve GABAergic cells in the cortex

10
Plasticity
  • Plasticity (a form of learning) involves changes
    in synaptic weights, either short-term or
    long-term.
  • Short-term plasticity tends to involve tonic
    neurons and neuromodulation. It can also involve
    recurrent signaling within a small network.
  • Long-term plasticity is believed to involve
    changes in receptor densities on the
    post-synaptic side and vesicle densities on the
    pre-synaptic side.

11
Memory
  • Short-term memory mechanisms
  • Changes in vesicle count
  • Slow time constant channel dynamics
  • Changes in receptor counts
  • Long-term memory mechanisms
  • Changes in channel count
  • Formation of new synapses/activation of silent
    synapses
  • Associative memory
  • Local recurrent networks in cortex
  • Coincidence detectors in the basal ganglia
  • Interacting areaspossibly chaotic in the
    hippocampus and olfactory system

12
Caveat
  • However, there is new evidence that synapses have
    discrete synaptic states.
  • See Montgomery and Madison, 2004, Discrete
    synaptic states define a major mechanism of
    synapse plasticity, Trends in Neurosciences,
    27(12)744-750, December 2004.
  • Glutaminergic synapses can be active (normal),
    potentiated (increased AMPA receptor count),
    depressed (reduced AMPA receptor count), silent
    (no AMPA receptors expressed), and recently
    silent (potentiated).

13
Transitions
  • NMDA receptors for Glutamate facilitate the
    transition from active to potentiated.
  • mGlu receptors facilitate the transition to
    active from potentiated.
  • Depressed synapses may form a continuum.
  • Silent synapses cannot be potentiated directly to
    an active or potentiated state. They pass through
    the recently silent state first.
  • Recently silent synapses cannot be depressed.

14
The role of channels
  • Potassium channels return the cell to a resting
    state. They often control the overall time
    constant.
  • Chloride channels may be inhibitory, shunting
    (desensitizing) and even facilitatory. They tend
    to have longer time constants.
  • Sodium channels are typically depolarizing. Short
    time constants.
  • Calcium channels are typically depolarizing. Long
    time constants. Used in signaling.

15
The role of neurotransmitters
  • Glutamate is excitatory (AMPA, NMDA, Kainate) and
    neuromodulatory.
  • Aspartate is similar.
  • Acetylcholine is excitatory and neuromodulatory.
  • GABA and Glycine are often treated as inhibitory,
    but they have other roles as well.
  • Epinephrine, 5HT, and dopamine are
    neuromodulatory. Many more, too.

16
The role of neuronal topology
  • Pyramidal cells have multiple compartments
  • Soma
  • Axon
  • Apical dendrites
  • Basalar dendrites
  • The apical dendrite apparently communicates with
    the soma using calcium spikes (I.e., active
    conductances).
  • Multiplicative interactions among synapses are
    important.

17
The role of delay tuning
  • For cells that a coincidence detectors, tuning of
    axonal delays may play a role in their
    computation.
  • For example, azimuth is estimated in man from
    relative arrival times of action potentials.
  • Echo delays can also be measured this way.

18
Real Neurons and What They Do
  • Principal neurons (fast)
  • Long-range transmission of signals, using action
    potentials along a long axon
  • Usually have local collaterals
  • Interneurons (slower)
  • Local processing
  • Signal sharpening
  • Stabilization of network activity
  • Some have axons, some not. Some are binary,
    forwarding signals to a dendrite down their axon.
  • Neuromodulatory neurons (slowest)
  • General control of activity over a large area

19
Some Neurotransmitters (Bob covers)
  • Glutaminergic neurons
  • Excitatory
  • GABAergic neurons
  • Inhibitory, shunting, or facilitive
  • Cholinergic neurons
  • Excitatory
  • Dopaminergic neurons
  • Neuromodulatory

20
Persistent Activity
  • Not well-understood
  • Hot research area
  • Underlies short-term memory.
  • Related to learning.
  • Important in the cortex
  • Neurons with persistent activity are rarely used
    in artificial neural networks, but are important
    in producing behaviour.

21
Mechanisms of Persistent Activity
  • Network recurrence
  • Neurons excite each other
  • Inhibitory recurrence needed for stability
  • Neuron-level persistent activity
  • Long time-constant channels
  • Causes the soma to depolarize repetitively
  • Can produce bursting or periodic signals

22
Interneurons
  • Functions
  • Signal normalization/sharpening
  • Network stabilization
  • Motion sensitivity
  • Amacrine type cells in the retina
  • Longer-range interactions

23
Take-Home Message
  • Neurons are complex and the brain uses that
    complexity to do wonderful things.
  • Dont be afraid to make assumptions about how
    neurons might do complex things if it allows your
    model to do what you need it to do. Its likely
    youre right.
  • Write your own MATLAB modelswhat the Neural
    Network Toolbox gives you is very limited.
  • GENESIS is intended to allow you to study neural
    models before you simplify them for MATLAB.
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