2 Does our understanding of neural networks adequately expla

2. Does our understanding of neural networks adequately explain the complex functions of the human nervous system? In your answer, you should include such terms as emergent properties, affective behaviors, cognitive behaviors, neurons (and the functional anatomy of each of its sections), action potentials, graded potentials, electrochemical gradient, depolarization, repolarization, hyperpolarization, voltage-gated Na+ channel, voltage-gated K+ channel, Na+ /K+ -ATPase, EPSP, IPSP, temporal summation, spatial summation, trigger zone of the axon hillock, myelination, salutatory conduction, nodes of Ranvier, axon terminal, voltage-gated Ca+ channel, synaptic vesicle, neurotransmitter, and synapse. You should be able to describe a few nervous pathologies, how they arise, and how they affect normal organismal function.

Solution

Neural networks is a series of interconnected neurons which when activated convey the neural signal in a linear pattern. Neurons are interconnected with each other through axon terminals and dendrites at the synapse. The axon terminal of the pre synaptic neuron convey the signal across the synapse to the dendrites of the post synaptic neurons. There are two kinds of signal through which the neurons cross talk among each other- electrical signal- which operates throughout the length of the axon and is due to the activity of the ion channels. The other signal is chemical signal which is conveyed though the neurotransmitters across the synapse to the other neuron. To initiate an electrical signal the neuronal membrane must overcome the resting membrane potential. This is brought about by the threshold potential. A threshold potential can initiate an electrical activity in the neuron. a potential lower than the threshold potential will not generate the electrical stimulation in the neuron. The electrical signal is of two types graded and action potential. The graded potential diminishes across the length of the neuron whereas the action potential remains same from the source to the sink/target. These potentials are generated due to the electrochemical gradients of different ions across the neuronal membrane. Normally sodium ions are more outside the cell whereas the potassium ions are abundant inside the cell. These difference is disturbed by the leakage of potassium ions from the cells to the outside environment. When a stimulation is received and the threshold is reached the Na ion channels open and the Na ions move inside the cell causing membrane depolarization. The depolarization moves long the axon carrying the signal of the same strength in case of an action potential. In mammals the axons are covered with myelin sheath except for the nodes of Ranvier. These nodes of ranvier can conduct the electrical signal but the part covered by the myelin sheath cannot. This type of conduction is called salutatory conduction and is faster. Once the action potential has passed the membrane is repolarized i.e. the Na is pumped out and K is pumped in this is done by the Na K atpase pump. The action potential can beinhibitory or excitatory in effect. When a chemical signal reaches the synapse it opens Na channels and the electrical stimulation of the post synaptic neuron takes place. A post synaptic neuron can receive signals from a single neuron (temporal summation) or from multiple neurons (spatial summation).

There are certain forms of neural networks which are arise due to the interactions which can be positive or negative. Due to these interactions the networks self organize becuase of which some non linear connections occur. This non linearity leads to unpredicted outputs and organizations of neural networks giving rise to emergent properties. These properties are unprecedented and cannot be predicted prior to the network organization. Therefore they are not dependent on the elements or neurons of the network. These can be both harmful as in CNS disorders or beneficial as in learning process. Cognitive and affective psychological phenomenon of brain are also output of these non linear neural networks.