The human nervous system may be viewed as a three-stage system, as depicted in the block diagram of Fig. 11.1. Central to the system is the brain, represented by the neural (nerve) net, which continually receives information, perceives it and makes appropriate decisions. Two sets of arrows are shown in the figure. Those pointing from left to right indicate the forward transmission of information-bearing signals through the system.
The arrows pointing from right to left signify the presence of feedback in the system. The receptors convert stimuli from the human body or the external environment into electrical impulses which convey information to the neural net (brain). The effectors convert electrical impulses generated by the neural net into discernible responses as system outputs.
The struggle to understand the brain has been made easier because of the pioneering idea of neurons as structural constituents of the brain. Typically, neurons are five to six orders of magnitude slower than silicon logic gates; events in a silicon chip happen in the nanosecond (10-9s) range, whereas neural events happen in the millisecond (10-3 s) range. However, the brain makes up for the relatively slow rate of operation of a neuron by having a truly staggering number of neurons (nerve cells) with massive interconnections between them.
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It is estimated that there are approximately 10 billion neurons in the human cortex, and 60 million synapses or connections. The net result is that the brain is an enormously efficient structure. Specifically the energetic efficiency of the brain is approximately 10-16 joules (J) per operation per second, whereas the corresponding value for the best computers in use today is about 10-6 joules per operation per second.
Synapses are elementary structural and functional units which mediate the interactions between neurons. The most common kind of synapse is a chemical synapse, which operates as follows. A presynaptic process liberates a transmitter substance which diffuses across the synaptic junction between neurons and then acts on a postsynaptic process.
Thus, a synapse converts a presynaptic electrical signal into a chemical signal and then back into a postsynaptic electrical signal. In electrical terminology, such an element is said to be a non-reciprocal two-port device. In traditional descriptions of neural organisation, it is assumed that a synapse is simple connection which can impose excitation or inhibition, but not both on the receptive neuron.
In an adult brain, plasticity may be accounted for by two mechanisms:
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i. The creation of new synaptic connections between neurons, and
ii. The modification of existing synapses.
Axons, the transmission lines and dendrites, the receptive zones, constitute two types of cell filaments which are distinguished on morphological grounds; an axon has a smoother surface, fewer branches and greater length, whereas a dendrite (so called because of its resemblance to a tree) has an irregular surface and more branches.
Neurons come in a wide variety of shapes and sizes in different parts of the brain. Figure 11.2., illustrates the shape of a pyramidal cell, which is one of the most common types of cortical neurons. Like many other types of neurons, it receives most of its inputs through dendritic spines (see inset in Fig. 11.2). The pyramidal cell can receive 10,000 or more synaptic contacts and it can project onto thousands of target cells.
The majority of neurons encode their outputs as a series of brief voltage pulses. These pulses, commonly known as action potentials or spikes, originate at or close to the cell body of neurons and then propagate across the individual neurons at constant velocity and amplitude. The reasons for the use of action potentials for communication among neurons are based on the physics of axons.
The axon of a neuron is very long and thin and is characterised by high electrical resistance and very large capacitance. Both of these elements are distributed across the axon. The axon may therefore be modeled as an RC transmission line, hence the common use of “cable equation” as the terminology for describing signal propagation along an axon.
Analysis of this propagation mechanism reveals that when a voltage is applied at one end of the axon it decays exponentially with distance, dropping to an insignificant level by the time it reaches the other end. The action potentials provide a way to circumvent this transmission problem.
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In the brain there are both small-scale and large-scale anatomical organisations, and different functions take place at lower and higher levels. Fig. 11.3, shows a hierarchy of interwoven levels of organisation which has emerged from the extensive work done on the analysis of local regions in the brain. The synapses represent the most fundamental level, depending on molecules and ions for their action. At the next levels we have neural microcircuits, dendritic trees, and then neurons.
A neural microcircuit refers to an assembly of synapses organised into patterns of connectivity to produce a functional operation of interest. A neural microcircuit may be likened to a silicon chip made up of an assembly of transistors.
The smallest size of microcircuits is measured in micrometers (pm) and their fastest speed of operation is measured in milliseconds. The neural microcircuits are grouped to form dendritic subunits within the dendritic trees of individual neurons.
The whole neuron, about 100 pm in size, contains several dendritic subunits. At the next level of complexity we have local circuits (about 1 mm in size) made up of neurons with similar or different properties; these neural assemblies perform operations characteristic of a localised region in the brain.
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This is followed by interregional circuits made up of pathways, columns, and topographic maps, which involve multiple regions located in different parts of the brain.
Topographic maps are organised to respond to incoming sensory information. These maps are often arranged in sheets, as in the superior colliculus, where the visual, auditory and somatosensory maps are stacked in adjacent layers in such a way that stimuli from corresponding points in space lie above or below each other.
Sensory inputs (motor, somatosensory, visual, auditory, etc.) are mapped onto corresponding areas of the cerebral cortex in an orderly fashion. At the final level of complexity, the topographic maps and other interregional circuits mediate specific types of behaviour in the central nervous system.
It is important to recognise that the structural levels of organisation described herein are a unique characteristic of the brain. They are nowhere to be found in a digital computer and we are nowhere close to re-creating them with artificial neural networks. Nevertheless we are inching our way toward a hierarchy of computational level similar to that described in Fig. 11.3. The artificial neurons we use to build our neural networks are truly primitive in comparison to those found in the brain.
The neural networks we are presently able to design are just as primitive compared to the local circuits and the interregional circuits in the brain. What is really satisfying, however, is that remarkable progress has been made on so many fronts during the past two decades.
With neurobiological analogy as the source of inspiration and the wealth of theoretical and technological tools which we are bringing together, it is certain that in another decade or so our understanding of artificial neural networks, will be much more sophisticated than it is today.