This optional section provides more biological details about the neuron. It is recommended for a fuller understanding of the biological basis of the computational models used in the book, but is not strictly necessary for understanding the functional operation of individual neurons.
Figure 2.1.1 shows a tracing of a typical excitatory neuron in the cortex called a pyramidal neuron, which is the primary type that we simulate in our models. The major elements of dendrites, cell body, and axon as discussed in the main chapter are shown. Note that the dendrites have small spines on them -- these are where the axons from sending neurons synapse, forming connections between neurons.
Figure 2.1.2 shows a high-resolution image of a synapse, while Figure 2.1.3 shows a schematic with all of the major elements of the synaptic signaling cascade represented. The primary behavior of a synapse is for an action potential to trigger release of neurotransmitter (NT) from the presynaptic terminal button, and this NT then binds to postsynaptic receptors that open to allow ions to flow, and thus communicating a signal to the postsynaptic neuron. In the predominant case of excitatory AMPA-receptor activation by the NT glutamate, the AMPA channels open to allow Sodium (Na+) ions to enter the postsynaptic neuron, which then have the effect of increasing the membrane potential and thus exciting the neuron. This excitatory input is called an excitatory postsynaptic potential or EPSP.
The other major types of postsynaptic receptors are:
- NMDA, which is involved in learning and allows Calcium ions to flow (Ca++) -- we will discuss these receptors in more detail in the Learning chapter.
- mGluR, which is also involved in learning and also possibly active maintenance of information in working memory -- these receptors do not pass ions, and instead affect complex chemical processes in the postsynaptic cell.
Inhibitory synapses arising from inhibitory interneurons release GABA NT, and the corresponding GABA receptors on the receiving neurons open to allow Chloride (CL-) ions to flow, producing a net negative or inhibitory effect on the postsynaptic cell (called an inhibitory postsynaptic potential or IPSP).
Importantly, the biology shows that synapses in the cortex can either be excitatory or inhibitory, but not both. This has implications for our computational models as we explore in the Networks chapter.