This thin, highly folded layer forms the outer shell of our brains and carries out a diverse set of tasks that includes processing sensory inputs, motor control, memory, and learning.
The Sponge Pinacocyte
This great range of abilities is accomplished with a rather uniform structure: six horizontal layers and a million micrometer-wide vertical columns all built from neurons, which integrate and distribute electrically coded information along tendrils that extend from them—the dendrites and axons. Like all the cells in the human body, a neuron normally has an electric potential of about —70 millivolts between its interior and exterior.
This membrane voltage changes when a neuron receives signals from other neurons connected to it. And if the membrane voltage rises to a critical threshold, it forms a voltage pulse, or spike, with a duration of a few milliseconds and a value of about 40 mV. If the spike meets certain criteria, the synapse transforms it into another voltage pulse that travels down the branching dendrite structure of the receiving neuron and contributes either positively or negatively to its cell membrane voltage.
Connectivity is a crucial feature of the brain. The pyramidal cell, for example—a particularly important kind of cell in the human neocortex—contains about 30, synapses and so 30, inputs from other neurons. And the brain is constantly adapting. Neuron and synapse properties—and even the network structure itself—are always changing, driven mostly by sensory input and feedback from the environment. General-purpose computers these days are digital rather than analog, but the brain is not as easy to categorize.
Neurons accumulate electric charge just as capacitors in electronic circuits do.
That is clearly an analog process. But the brain also uses spikes as units of information, and these are fundamentally binary: At any one place and time, there is either a spike or there is not. Electronically speaking, the brain is a mixed-signal system, with local analog computing and binary-spike communication. This mix of analog and digital helps the brain overcome transmission losses. Because the spike essentially has a value of either 0 or 1, it can travel a long distance without losing that basic information; it is also regenerated when it reaches the next neuron in the network.
Another crucial difference between brains and computers is that the brain accomplishes all its information processing without a central clock to synchronize it.
Storage and Evolution of Memes in the Brain
Although we observe synchronization events—brain waves—they are self-organized, emergent products of neural networks. Interestingly, modern computing has started to adopt brainlike asynchronicity, to help speed up computation by performing operations in parallel. But the degree and the purpose of parallelism in the two systems are vastly different. The idea of using the brain as a model of computation has deep roots. The first efforts focused on a simple threshold neuron , which gives one value if the sum of weighted inputs is above a threshold and another if it is below.
The biological realism of this scheme, which Warren McCulloch and Walter Pitts conceived in the s, is very limited. Nonetheless, it was the first step toward adopting the concept of a firing neuron as an element for computation. In , Frank Rosenblatt proposed a variation of the threshold neuron called the perceptron.
A network of integrating nodes artificial neurons is arranged in layers. Rosenblatt also introduced an essential feature found in the brain: inhibition. Instead of simply adding inputs together, the neurons in a perceptron network could also make negative contributions. This feature allows a neural network using only a single hidden layer to solve the XOR problem in logic, in which the output is true only if exactly one of the two binary inputs is true.
This simple example shows that adding biological realism can add new computational capabilities.edutoursport.com/libraries/2019-12-21/506.php
TOTAL RECALL – The Evolution of Memory | TOTAL RECALL – The Evolution of Memory
But which features of the brain are essential to what it can do, and which are just useless vestiges of evolution? Nobody knows. We do know that some impressive computational feats can be accomplished without resorting to much biological realism. Deep-learning researchers have, for example, made great strides in using computers to analyze large volumes of data and pick out features in complicated images. Although the neural networks they build have more inputs and hidden layers than ever before, they are still based on the very simple neuron models. Their great capabilities reflect not their biological realism, but the scale of the networks they contain and the very powerful computers that are used to train them.
But deep-learning networks are still a long way from the computational performance, energy efficiency, and learning capabilities of biological brains. There have been several such efforts over the years, but they have all been severely limited by two factors: energy and simulation time. As an example, consider a simulation that Markus Diesmann and his colleagues conducted several years ago using nearly 83, processors on the K supercomputer in Japan.
Simulating 1. And these simulations generally ran at less than a thousandth of the speed of biological real time. Why so slow? The reason is that simulating the brain on a conventional computer requires billions of differential equations coupled together to describe the dynamics of cells and networks: analog processes like the movement of charges across a cell membrane. These computer simulations can be a tool to help us understand the brain, by transferring the knowledge gained in the laboratory into simulations that we can experiment with and compare with real-world observations.
But if we hope to go in the other direction and use the lessons of neuroscience to build powerful new computing systems, we have to rethink the way we design and build computers. Copying brain operation in electronics may actually be more feasible than it seems at first glance. It turns out the energy cost of creating an electric potential in a synapse is about 10 femtojoules 10 joules. The gate of a metal-oxide-semiconductor MOS transistor that is considerably larger and more energy hungry than those used in state-of-the-art CPUs requires just 0.
A synaptic transmission is therefore equivalent to the charging of at least 20 transistors. The notion of building computers by making transistors operate more like neurons began in the s with Caltech professor Carver Mead. If the brain is able to assess probabilities in a continuous way, this should lead to a range of human behavior that varies smoothly as the probabilities change.
However, if the human brain works on a discrete basis, it must treat some probabilities in the same way. For example, a person might judge probabilities as being low, medium, or high. In other words, the probabilities must be rounded into specific categories—probabilities of 0. In that case the range of human behavior would follow a step-like structure that reflects the jump from low to medium to high risk. So Tee and Taylor studied human decision-making as probabilities change.
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They did this by testing the way over 80 people judged and added probabilities associated with roulette wheels in more than 2, experimental trials. The experiments employed a similar approach. For example, participants were shown a roulette wheel with a certain sector mapped out and asked to judge the probability of the ball landing in that sector.
Then they were shown two wheels with different sectors mapped out.
They had to judge the probability of the ball landing in both sectors. Finally, they were asked to judge whether the probability was higher in the case of the single roulette wheel or the double roulette wheel example. The researchers then varied the size of sectors in the experiments to span a wide range of probabilities, in total carrying out 2, trials. Participants performed the tests in random order on a computer touch screen and were paid a token amount for their participation although they also had the chance to win a bonus based on their performance. The results make for interesting reading.
Tee and Taylor say that far from matching the smooth distribution of behavior expected if the brain stores information in analog form, the results are more easily interpreted using a discrete model of information storage. An important factor is the extent to which the brain quantizes the probabilities. For example, does it divide them into three or four or more categories? And how does this quantization change with the task at hand?
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In that respect, Tee and Taylor say that a 4-bit quantization best fits the data. Indeed, engineers have found this in designing products for the real world. Images are usually encoded with a bit quantization, whereas music is generally quantized using a bit system. This reflects the maximum resolution of our visual and auditory senses.
Superior pattern processing is the essence of the evolved human brain
The work has implications for other areas too. There is increasing interest in devices that link directly with the brain. Such machine-brain interfaces will obviously benefit from a better understanding of how the brain processes and stores information, a long-term goal for neuroscientists. So research like this will help pave the way toward that goal. Ref: arxiv. Emerging Technology from the arXiv.