Real Neurons for Engineers Lecture 2

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Real Neurons for Engineers(Lecture 2)

• Harry R. Erwin, PhD
• COMM2E
• University of Sunderland

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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

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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.

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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.

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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.

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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.

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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.

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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.

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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

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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.

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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

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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).

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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.

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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.

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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.

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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.

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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.

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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

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Some Neurotransmitters(Bob covers)

• Glutaminergic neurons
• Excitatory
• GABAergic neurons
• Inhibitory, shunting, or facilitive
• Cholinergic neurons
• Excitatory
• Dopaminergic neurons
• Neuromodulatory

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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.

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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

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Interneurons

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

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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.