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

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

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: Rieke et al, 1999, Spikes: Exploring the Neural Code, Bradford Books. Churchland and Sejnowski, 1994, The Computational Brain, Bradford Books.

What is a Neuron? A neuron is an ‘excitable cell’, like a muscle cell. Neurons are very primitive—found 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.

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.

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.

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.

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.

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

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.

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 areas—possibly chaotic in the hippocampus and olfactory system

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): , December 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).

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.

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.

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.

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.

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.

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

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

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.

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

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

Take-Home Message Neurons are complex and the brain uses that complexity to do wonderful things. Don’t 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. It’s likely you’re right. Write your own MATLAB models—what 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.