“Swiftly the brain becomes an enchanted loom, where millions of flashing shuttles weave a dissolving pattern-always a meaningful pattern-though never an abiding one.” - Sir Charles Sherrington.
In the last chapter we have seen how the structure of the nervous system changes with the complexity of the organism. We have also seen how as the size of the nervous system grows, under the pressure of the ‘save wire’ principle, the nervous system evolves from a diffuse-nerve-net type to a brain-and-cord variety. These more evolved architectures of the nervous system are associated in general with organisms with a greater range of capabilities. The correlation between brain structure and intelligence, in the final analysis, is a weak one. Therefore we are compelled to look for more precise indicators of brain’s capabilities.
Looking at it from a different angle, we can see how intelligence and brain structure need not be strictly correlated since intelligence is a matter of function. Structure and function need not have a one-to-one relationship though there can be an overlap. Invoking the computer analogy (which we will use sparingly, since it can sometimes be misleading), it is like estimating the capabilities of a computer by measurements of its CPU’s chassis. What a computer can do, first of all, is determined by the general specifications of its hardware, but also,
more importantly, by the software loaded in its hard disk. Therefore, to understand how intelligence is represented in the brain, we must first identify brain’s “software.”
Brain’s hardware consists of the structure, the wiring, the connectivity patterns and the rest, while the “software” is more difficult to define because there is no precise correspondence.
It certainly has to do with what brain structures do, their activity or function. Our discussion of the brain in this book began with brain’s “hardware” because it is easier to explain, but a major focus of this book is to define and describe what brain’s “software” consists of.
Anatomy and insight – the two terms seem to be almost contradictory. Textbooks on neuroanatomy describe brain’s structure in excruciating detail, - which of course is a compulsory diet for a student of medicine, - but do little to explain the function of those structures. But perhaps it would be somewhat judgmental to say so, because the job of explaining brain function must be left to neurophysiology. Even if we invoke an important tenet of biology that “structure inspires function”, in case of the brain, structure can often mislead our understanding of function. The history of neuroscience is full of such wrong turns and blind alleys. Therefore, traditional wisdom urges you to steer clear of anatomy and anatomists if you want to get an insight into brain function. But there are exceptions to every rule. An extremely influential book called Vehicles, written by V. Braitenberg, a preeminent neuroanatomist, is one such an exception.
The complete title of this delightful book which reads “Vehicles: Experiments in synthetic psychology” makes the reader wonder what unearthly link might exist between vehicles and psychology. In this book, Braitenberg uses the word “vehicles” as a metaphor to an organism that possesses a nervous system. An organism is described as a vehicle that is capable of sensing the objects in the world around, and navigate that world in ways that increase its survival. But for
an organism to do all that, we expect it to have a complex and sophisticated nervous system. But this is where Braitenberg steps in, with his delightful little creations, to present an important insight which can be simply expressed as follows: complex behavior does not require a complex nervous system; a simple nervous system in its interaction with a complex environment can produce complex behavior. In order to illustrate this idea, Braintenberg presents a series of simple devices, of gradually increasing complexity, through various chapters of the book. These devices, the vehicles, are wired up to respond and move around in their environment in specific ways, displaying behaviors that resemble complex human emotions like love, fear, anger and even shyness.
Vehicles of Love and War:
Braitenberg’s vehicles are like little toy carts with wheels that children play with. Each vehicle has some sensors that measure properties of its ambience like temperature, light, humidity etc. Signals measured by the sensors are fed to the motors that drive the wheels. Thus environmental properties control the wheels of the vehicles and hence its movements.
Let us consider the simplest kind of these vehicles, the Vehicle-I, in which there is a single sensor in the front and a motor that drives a single wheel (fig. 3.1). Assume that the sensor measures temperature and result of the measurement controls motor speed. The greater the temperature, the higher the motor speed. Such a vehicle speeds up in a hot environment and slows down in colder regions. Furthermore, on ideal, friction-free surfaces, it would follow a straight path, but the real-world frictional forces, between the surface and the wheels, make the vehicle deviate from its straight path. The vehicle will be seen to negotiate complex, winding trajectories, slowing down and speeding up, in response to ambient temperature. It would almost
seem alive, following some complex inner law of life, while all along it was obeying a simple scalar, thermal life-policy.
Figure 3.1: Braitenberg’s Vehicle-I
But vehicle-I is too simplistic to be considered as a serious analog of a real life organism with a nervous system, though it shows enough activation to be considered to possess life. Let us consider, therefore, the second class of Braitenberg’s creations, the Vehicle-II. This class of vehicles have two sensors and two motors, one on each side of its rectangular body (fig. 3.2).
Consider the three possible architectures of Vehicle-II: 1) each sensor is connected to the motor on the same side only, 2) each sensor is connected to the motor on the opposite side only, 3) both sensors are connected to both motors. It is evident that the third case is simply a glorified form of Vehicle I. Therefore, we consider only cases 1 and 2. Assume that the sensors respond to light (it could be smell, or sound or heat or several other physical properties). The stronger the sensation the greater is the drive to the corresponding motor. In the case of fig. 3.2a, if the light source is, say, to the right of the vehicle, the right sensor picks up a stronger signal than the left one, and the right wheel rotates faster. Thus the vehicle will be seen to be running away from the light source. The opposite effect will be seen in the vehicle of fig. 3.2b, since the wheel on the opposite side of the light source turns faster. In this case the vehicle rushes towards the light
source, increasing its speed as it approaches it closer and closer. Now let us imagine that these simple machines are housed inside real-looking creatures, soft and slimy. An unwitting observer of these vehicles would conclude that both the vehicles, first of all, dislike light, and express their dislike in contrary ways. The first one looks like a coward, fearful of light and its possible harmful effects. The second one hates light sources and rushes towards them aggressively as if to destroy them.
Figure 3.2: Two variations of Braitenberg’s Vehicle-II. (a) Sensors are connected to wheels/motors on the same side. (b) Sensors are connected to motors on the opposite side.
The configurations of the last two figures have only excitatory connections. That is, increased intensity of external stimuli can only increase the motor speed. Such an arrangement yields a limited range of behaviors. More interesting behaviors emerge if we introduce inhibitory connections, i.e., if we permit the motor to slow down when the corresponding sensor record increased intensity of stimulus. Fig. 3.3 shows two variants: one in which the sensors are connected to the motors on the same side, and the other with connections on the opposite side.
In the vehicle of fig. 3.3a, the motor on the “stimulus side” runs slower due to inhibitory connections. Therefore this vehicle unlike its counterpart of fig. 3.2a actually orients towards the stimulus. Contrarily, the vehicle of fig. 3.3b turns away from the stimulus. But we may quickly
note an important common difference between the vehicles of fig. 3.3 and those of fig. 3.2. Both slow down when they approach close to the stimulus because the overall intensity of signal received from the stimulus increases with proximity, and the motors slow down. Here the vehicle with “same side” connections simply approaches the stimulus and stops at a distance. This vehicle is influenced by two apparently contrary forces: one preventing it from coming too close to the stimulus, and the other preventing it from turning away from the stimulus. But the forces working on the vehicle of fig. 3.3b are slightly different. While it may be able to draw too close to the stimulus, it is free and actually compelled to turn away from the same. Thus vehicle of fig.
3.3b displays a curious behavior. As it approaches a stimulus it slows down and once it is sufficiently close to the same, it gently veers away and goes off elsewhere!
Figure 3.3: Braitenberg’s vehicle with negative connections. Two variants are shown: (a) one in which the sensors are connected to the motors on the same side, and (b) the other with connections on the opposite side.
We may describe the “feelings” of the vehicles of fig. 3.3 as those of “love” since, unlike the vehicle of fig. 3.2b, they do not make aggressive advances towards the stimulus. But this latter class of vehicles show such rich shades of sophisticated “love.” The vehicle of fig. 3.3a displays a quiet “adoration” of the stimulus, drawn towards it, but “shy” to draw too close. On
the contrary, the vehicle of fig. 3.3b shows a more fickle and fanciful love: as it approaches the stimulus it suddenly grows afraid of a “commitment,” changes its mind and wanders away in search of other relationships!
Braitenberg’s vehicles are metaphors of real nervous systems. They show that to produce complex behavior, the organism need not be complex. It is the interaction of a simple organism with a complex environment that produces complex behavior. The vehicles have a common underlying theme: a set of sensors that respond to various environmental properties drive a set of motors through a network. All the subtle variations in the behavior can be seen to arise out of the network, because it is the network that determines the relationship between the sensory input and motor output. Another important feature of the vehicles is that they are not “programmed.” There is a constant flow of information into the vehicle into its sensors to its motor organs over a network. All the “programming” the vehicle needs, or has, is encoded in the connections of the network. Here we encounter a very important idea that the nervous system can be seen as a network of connections, between the sensory and motor organs, that determines the behavior of the organism.
The difference between the behaviors of the two vehicles above arises due to the nature of the connections – are the connections to the same side or opposite, are they positive or negative? The side to which connections are made may be classified as a structural property. But what are positive and negative connections in the real brain? To answer this question we must begin our journey into brain’s function. We may begin by saying that neurons are not passive links between sensory and motor structures, but active “processing units” that receive information from sensory areas, work on that information, and transmit the results to the motor areas. To understand this processing and transmission, we need to take a closer look at the
neuron and its function. We shall look at a neuron as a complex electrical device, with electrical currents flowing, in rhythmic waves, all over its intricate arboreal body. We shall learn how neurons talk to each other by sprinkling minute quantities of chemical at each other. We shall learn about the complex electrical, chemical and structural changes that occur in the microscopic world of brain tissue and how these changes support our brain’s large scale functions, creating our thoughts, emotions and, everything else that constitutes what we call our self.
The Neuron
A neuron, for all its arboreal abundance, is basically a cell. Like any other cell it is a fluid-filled bag consisting of all the standard paraphernalia like the nucleus and nucleolus, golgi bodies, mitochondria, a membrane that separates the rest of the world from itself and so on. But if a neuron is just like any other cell in the body, why aren’t the other organs smart? Why is genius the special prerogative of the brain and not of gall bladder? There are indeed a few differences between neurons, the brain cells, and other cells of the body, which seem to make all the difference.
Figure 3.4: A Purkinje neuron drawn by neurobiologist Ramon y Cajal
A distinctive feature of a neuron which can be noticed in micrographic pictures is the hairy projections that stick out of its cell body. Fig. 3.4 shows a picture of a neuron, a specific type called the Purkinje neuron, drawn by Ramon y Cajal. It is an impressive instance of scientific art considering that it was hand-drawn in an era when microscopic pictures could not be photographed. The tiny spot in the middle of the neuron in the fig. 3.4 is its cell body, formally known as the soma; the rest of its body is the “branches” or the “wire” of which deliberated at length in the previous chapter. The branches come in a variety of patterns –for example, thick bushy shrubs, or long slender stalks terminated by a short tuft – which produce a large variety of neuronal morphologies. For instance, compare the bipolar neuron (fig. 3.5) found in the retina of the eye, with two single strands arising out of the soma and extending in opposite directions, with a Purkinje cell with its rich, arboreal outgrowth. The peculiar shapes of neuronal arboreal patterns often enable them to serve their unique functions.
Figure 3.5: Three different neuronal morphologies. A) a bipolar neuron, b) a multipolar neuron and c) a pseudounipolar neuron.
A closer look at the branches of a neuron shows that they can be further segmented into two broad regions. Fig. 3.6 shows a pyramidal cell, a ubiquitous type of neuron found in the brain, its name referring to its conical soma. Its branches can be seen to be distributed on either side of the soma. On one side we notice many wires emerging out of the soma, each of them branching repeatedly to form a dense arbor. These are called the dendrites, and the arbor formed by them, the dendritic tree. On the other side, we notice a single wire emerging from a slightly swollen part of the soma, known as the axon hillock. This single wire, known as the axon, extends to a distance before it branches into many axon collaterals.
Figure 3.6: A pyramidal neuron.
The dendritic tree is smaller in size with a diameter of few hundred microns (1 micron = 1 millionth of a meter). The axons are typically much longer, in extreme cases extending to as much as a few feet. The axons are the neuron’s long tentacles by which they reach out and make connections to each other. The axon is a neuron’s mouthpiece with which a neuron sends out signals, in the form of bursts of electrical energy, to other neurons with which it is connected. At the point where one neuron meets another, the axon terminal of one neuron makes contact with the dendrite of another neuron. The meeting point between the axon of one neuron, and the dendrite of another, known as the synapse, occupies a very important place in all of brain’s activity. Thanks to the synapse, and the myriad activities that take place within its narrow confines, a neuron is able to converse with another neuron.
The number of connections a single neuron can have to other neurons can vary. A typical neuron in the human brain receives about 1000 to 10,000 connections. Some of the more densely connected neurons, like the Purkinje neurons, receive as many as 1 or 2 lakh (105) connections.
An adult brain has about 100 billion neurons. That makes the number of synapses about 1011 X 104 = 1015, or a quadrillion synapses. In other words, brain is a network of 100 billion units with a quadrillion connections! To get an idea of the complexity of such a network let us compare it with the contemporary mobile network of the world. The number of mobile connections in the world had recently breached the barrier of 5 billion and is set to exceed 6 billion by 2012.
Assume that each mobile has about 500 contacts in its address book, easily an overestimate, and therefore “connected” to so many other mobiles. That gives the mobile network about 2.5 X 1012, or 2.5 trillion connections. The brain is definitely a much larger network, but weighs only about 1.3 kgs, all packed neatly in a volume of 14 cm X 16 cm X 9 cm.
But that only gives us an idea only of the structural complexity of the network in the brain. The brain is not a static network. The connections among neurons make and break, even on the time scale of minutes, as we learn new things, and forget old ones. Furthermore, there are all the electrical and chemical signals that flash along the brain’s wiring system at speeds of hundreds of kilometers per hour, in our waking, as much as in our sleep. While structural complexity of the brain is impressive, it is the complexity of the signaling that drives brain’s function. The sources of human intelligence are mostly likely to be found in these signaling mechanisms. It is this functional aspect that has been ignored by the anatomical studies of Einstein’s brain. In order to understand brain function, we must first understand the electrochemical basis of a neuron’s function.
Electrochemistry of a neuron:
Imagine a beaker containing a salt solution like potassium chloride (KCl) (fig. 3.7). The beaker has a central partition with the solution present on either side. Assume now that the compartment on the left has a higher concentration of KCl than the compartment on the right.
Also assume that the partition consists of a semi-permeable membrane that allows only water to pass between the compartments. (A good example of such a membrane is the thin film on the inside of an egg, which allows only passage of water. When the egg is placed in pure, distilled water, water from outside enters the egg and the egg swells). By the familiar process of osmosis, water from the compartment with lower concentration of KCl now moves to the side with higher concentration, until the concentration in the two compartments equalizes, and that is not very interesting!
Figure 3.7: A beaker containing two compartments (left and right) of KCl, separated by a
Figure 3.7: A beaker containing two compartments (left and right) of KCl, separated by a