As discussed in the previous chapter, the Pavlovian type of learning involves only the presynaptic neuron. In the Hebbian type of learning, the synaptic modification is induced by the participation of both presynaptic and postsynaptic neurons.

Cellular Level Description

Recalling that learning is a process that associates two events. In the hippocampus, events are represented by a population of neurons, each may be either excited or in the resting state. A particular event is represented by a particular set of neurons in the excited state. For instance, if the number of neurons involved in the representation is n, then mathematically an event can be denoted by a vector with dimension n,

X = [x₁, x₂, x₃, .....x_n]

where x_i (i = 1 - n) is either "0" (resting) or "1" (excited).

The hippocampus has the most complex neural network in the brain. Each neuron is connected to thousands of other neurons. For simplicity, we assume that only five neurons are involved in the representation of events. The connection of these neurons are shown in Figure 7-1. The lines are drawn from the axon terminals (presynaptic) in the upper row to the dendrites (postsynaptic) in the lower row. For example, in Figure 7-1, the terminals of the first neuron are connected to the dendrites of the second and fourth neurons. The terminals of the second neuron are connected to the dendrites of the first and third neurons. These lines may also be considered as the synapses between neurons. The "dark cloud" is represented by the excitation of the second and fourth neurons whereas the "rain" is represented by the excitation of the first, fourth and fifth neurons.

In the Hebbian type of learning, the synaptic modification may be induced only when both presynaptic and postsynaptic neurons are excited. In Figure 7-1, these synapses are represented by red lines. The left red line connects the second and first neurons; the right red line connects the fourth and fifth neurons. When the dark cloud and rain occur almost at the same time, these neurons are excited and their synapses are modified so that nerve impulses can be more easily transmitted from the presynaptic neuron to the postsynaptic neuron. Suppose before learning the nerve impulse is unlikely to transmit from the second neuron to the first neuron. After the pairing between dark cloud and rain, this synaptic transmission is greatly enhanced. Next time, the dark cloud alone is likely to cause the excitation of the first neuron, thereby increasing the probability to recall rain.

The Role of NMDA Receptors

How can the nervous system implement Hebbian type of learning? The answer lies in the NMDA receptor (NMDAR), which is a subtype of glutamate receptor ion channels (Traynelis et al., 2010). For most ligand-activated ion channels, activation (opening) requires only the binding of the agonist (e.g., neurotransmitters). However, activation of NMDA receptors requires two events: binding of glutamate (a neurotransmitter) and relief of Mg²⁺ block. NMDARs are located at the postsynaptic membrane. At the resting potential, NMDARs are blocked by Mg²⁺ ions. If the membrane potential is depolarized due to excitation of the postsynaptic neuron, the outward depolarizing field may repel Mg²⁺ out of the channel pore. On the other hand, binding of glutamate can open the gate of NMDARs (via unknown mechanism). In the normal physiological process, glutamate is released from the presynaptic terminal when the presynaptic neuron is excited. Relief of Mg²⁺ block is due to excitation of the postsynaptic neuron. Therefore, excitations of both presynaptic and postsynaptic neurons are necessary and sufficient to open the NMDA receptors.

Another important feature of the NMDA receptor is that it conducts Ca²⁺ ions which control the activities of diverse enzymes. The entry of Ca²⁺ ions into the postsynaptic neuron can then modify the strength of synaptic connections, resulting in long-term potentiation or depression. Subsequent events may further produce long lasting memory traces.