Part 6 (2/2)

The software programs for an operating system, language compilers, and a.s.semblers are reasonably complex, but modeling a particular program-for example, a speech-recognition program based on Markov modeling-may be described in only a few pages of equations. Nowhere in such a description would be found the details of semiconductor physics. A similar observation also holds true for the brain. A particular neural arrangement that detects a particular invariant visual feature (such as a face) or that performs a band-pa.s.s filtering (restricting input to a specific frequency range) operation on auditory information or that evaluates the temporal proximity of two events can be described with far greater simplicity than the actual physics and chemical relations controlling the neurotransmitters and other synaptic and dendritic variables involved in the respective processes. Although all of this neural complexity will have to be carefully considered before advancing to the next higher level (modeling the brain), much of it can be simplified once the operating principles of the brain are understood.

Trying to Understand Our Own Thinking The Accelerating Pace of Research

We are now approaching the knee of the curve (the period of rapid exponential growth) in the accelerating pace of understanding the human brain, but our attempts in this area have a long history. Our ability to reflect on and build models of our thinking is a unique attribute of our species. Early mental models were of necessity based on simply observing our external behavior (for example, Aristotle's a.n.a.lysis of the human ability to a.s.sociate ideas, written 2,350 years ago).19 At the beginning of the twentieth century we developed the tools to examine the physical processes inside inside the brain. An early breakthrough was the measurement of the electrical output of nerve cells, developed in 1928 by neuroscience pioneer E. D. Adrian, which demonstrated that there were electrical processes taking place inside the brain. the brain. An early breakthrough was the measurement of the electrical output of nerve cells, developed in 1928 by neuroscience pioneer E. D. Adrian, which demonstrated that there were electrical processes taking place inside the brain.20 As Adrian write, ”I had arranged some electrodes on the optic nerve of a toad in connection with some experiment on the retina. The room was nearly dark and I was puzzled to hear repeated noises in the loudspeaker attached to the amplifier, noises indicating that a great deal of impulse activity was going on. It was not until I compared the noises with my own movements around the room that I realized I was in the field of vision of the toad's eye and that it was signaling what I was doing.” As Adrian write, ”I had arranged some electrodes on the optic nerve of a toad in connection with some experiment on the retina. The room was nearly dark and I was puzzled to hear repeated noises in the loudspeaker attached to the amplifier, noises indicating that a great deal of impulse activity was going on. It was not until I compared the noises with my own movements around the room that I realized I was in the field of vision of the toad's eye and that it was signaling what I was doing.”

Adrian's key insight from this experiment remains a cornerstone of neuroscience today: the frequency of the impulses from the sensory nerve is proportional to the intensity of the sensory phenomena being measured. Fr example, the higher the intensity of the light, the higher the frequency (pulses per second) of the neural impulses from the retina to the brain. It was a student of Adrian, Horace Barlow, who contributed another lasting insight, ”trigger features” in neurons, with the discovery that the retinas of frogs and rabbits has single neurons that would trigger on ”seeing” specific shapes, directions, or velocities. In other words, perception involves a series of stages, with each layer of neurons recognizing more sophisticated features of the image.

In 1939 we began to develop an idea of how neurons perform: by acc.u.mulating (adding) their inputs and then producing a spike of membrane conductance (a sudden increase in the ability of the neuron's membrane to conduct a signal) an voltage along the neuron's axon (which connects to other neuron's via a synapse). A. L. Hodgkin and A. F. Huxley described their theory of the axon's ”action potential” (voltage).21 They also made an actual measurement of an action potential on an animal neural axon in 1952. They chose squid neurons because of their size and accessible anatomy. They also made an actual measurement of an action potential on an animal neural axon in 1952. They chose squid neurons because of their size and accessible anatomy.

Building on Hodgkin and Huxley's insight W. S. McCulloch and W. Pitts developed in 1943 a simplified model of neural nets that motivated a half century of work on artificial (simulated) neural nets (using a computer program to simulate the way neurons work in the brain as a network). This model was further refined by Hodgkin and Huxley in 1952. Although we now realize that actual neurons are far more complex that these early models, the original concept has held up well. This basic neural-net model has a neural ”weight” (representing the ”strength” of the connection) for each synapse and a nonlinearity (firing threshold) in the neuron soma (cell body).

As the sum of the weighted inputs to the neuron soma increases, there is relatively little response from the neuron until a critical threshold is reached, as which point the neuron rapidly increased the output of its axon and fires. Different neurons have different thresholds. Although recent research shows that the actual response is more complex than this, the McCulloch-Pitts and Hodgkin-Huxley models remain essentially valid.

These insights led to an enormous amount of early work in creating artificial neural nets, in a field that became known as connectionism. This was perhaps the first self-organizing paradigm introduced to the field of computation.

A key requirement for a self-organizing system is a nonlinearity: some means of creating outputs that are not simple weights sums of the inputs. The early neural-net models provided this nonlinearity in their replica of the neuron nucleus.23 (The basic neural-net method is straightforward.) (The basic neural-net method is straightforward.)24 Work initiated by Alan Turing on theoretical models of computation around the same time also showed that computation requires a nonlinearity. A system that simple creates weighted sums of its inputs cannot perform the essential requirements of computation. Work initiated by Alan Turing on theoretical models of computation around the same time also showed that computation requires a nonlinearity. A system that simple creates weighted sums of its inputs cannot perform the essential requirements of computation.

We now know that actual biological neurons have many other nonlinearities resulting from the electrochemical action of the synapses and the morphology (shape) of the dendrites. Different arrangements of biological neurons can perform computations, including subtracting, multiplying, averaging, filtering, normalizing, and thresholding signals, among other types of transformations.

The ability of neurons to perform multiplication is important because it allowed the behavior of one network of neurons in the brain to be modulated (influenced) by the results of computations of another network. Experiments using electrophysiological measurements on monkeys provide evidence that the rate of signaling by neurons in the visual cortex when processing an image is increased or decreased by whether or not the monkey is paying attention to a particular area of that image.25 Human fMRI studies have also shown that paying attention to a particular area of an image increases the responsiveness of the neurons processing that image in a cortical region called V5, which is responsible for motion detection. Human fMRI studies have also shown that paying attention to a particular area of an image increases the responsiveness of the neurons processing that image in a cortical region called V5, which is responsible for motion detection.26 The connectionism movement experienced a setback in 1969 with the publication of the book Perceptrons Perceptrons by MIT's Marvin Minsky and Seymour Papert. by MIT's Marvin Minsky and Seymour Papert.27 It included a key theorem demonstrating that the most common (and simplest) type of neural net used at the time (called a Perceptron, pioneered by Cornell's Frank Rosenblatt), was unable to solve the simple problem of determining whether or not a line drawing was fully connected. It included a key theorem demonstrating that the most common (and simplest) type of neural net used at the time (called a Perceptron, pioneered by Cornell's Frank Rosenblatt), was unable to solve the simple problem of determining whether or not a line drawing was fully connected.28 The neural-net movement had a resurgence in the 1980s using a method called ”backpropagation,” in which the strength of each simulated synapse was determined using a learning algorithm that adjusted the weight (the strength of the output of each of artificial neuron after each training trial so the network could ”learn” to more correctly match the right answer. The neural-net movement had a resurgence in the 1980s using a method called ”backpropagation,” in which the strength of each simulated synapse was determined using a learning algorithm that adjusted the weight (the strength of the output of each of artificial neuron after each training trial so the network could ”learn” to more correctly match the right answer.

However, backpropagation is not a feasible model of training synaptic weight in an actual biological neural network, because backward connections to actually adjust the strength of the synaptic connections do not appear to exist in mammalian brains. In computers, however, this type of self-organizing system can solve a wide range of pattern-recognition problems, and the power of this simple model of self-organizing interconnected neurons has been demonstrated.

Less well know is Hebb's second form of learning: a hypothesized loop in which he excitation of the neuron would feed back on itself (possibly through other layers), causing a reverberation (a continued reexcitation could be the source of short-term learning). He also suggested that this short-term reverberation could lead to long-term memories: ”Let us a.s.sume then that the persistence or repet.i.tion of a reverberatory activity (or 'trace') tends to induce lasting cellular changes that add to its stability. The a.s.sumption can be precisely stated as follows: When an axon of cell A is near enough to excite a cell B and repeatedly or persistently take part in firing it, some growth process or metabolic change takes place in one or both cells such that A's efficiency, as one of the cell's firing B, is increased.”

Although Hebbian reverberatory memory is not as well established as Hebb's synaptic learning, instances have been recently discovered. For example, sets of excitatory neurons (ones that stimulate a synapse) and inhibitory neurons (ones that block a stimulus) begin an oscillation when certain visual patterns are presented.29 And researchers at MIT and Lucent Technologies' Bell Labs have created an electronic integrated circuit, composed of transistors, that simulates the action of sixteen excitatory neurons and one inhibitory neuron to mimic the biological circuitry of the cerebral cortex. And researchers at MIT and Lucent Technologies' Bell Labs have created an electronic integrated circuit, composed of transistors, that simulates the action of sixteen excitatory neurons and one inhibitory neuron to mimic the biological circuitry of the cerebral cortex.30 These early models of neurons and neural information processing, although overly simplified and inaccurate in some respects, were remarkable, given the lack of data and tools when these theories were developed.

Peering into the Brain

We've been able to reduce drift and noise in our instruments to such an extent that we can see the tiniest motions of these molecules, through distances that are less than their own diameters....[T]hese kinds of experiments were just pipedreams 15 years ago.-STEVEN BLOCK, PROFESSOR OF BIOLOGICAL SCIENCES AND OF APPLIED PHYSICS, STANFORD UNIVERSITY

Imagine that we were trying to reverse engineer a computer without knowing anything about it (the ”black box” approach). We might start by placing arrays of magnetic sensors around the device. We would notice that during operations that updated a database, significant activity was taking place in a particular circuit board. We would be likely to take note that there was also action in the hard disk during these operations. (Indeed, listening to the hard disk has always been one crude window into what a computer is doing.) We might then theorize that the disk had something to do with the long-term memory that stores the databases and that the circuit board that is active during these operations was involved in transforming the data to be stored. This tells us approximately where and when the operations are taking place but relatively little about how these tasks are accomplished.

If the computer's registers (temporary memory locations) were connected to front-panel lights (as was the case with early computers), we would see certain patterns of light flickering that indicated rapid changes in the states of these registers during periods when the computer was a.n.a.lyzing data but relatively slow changes when the computer was transmitting data. We might then theorize that these lights reflected changes in logic state during some kind of a.n.a.lytic behavior. Such insights would be accurate but crude and would fail to provide us with a theory of operation or any insights as to how information is actually coded or transformed.

The hypothetical situation described above mirrors the sort of efforts that have been undertaken to scan and model the human brain with the crude tools that have historically been available. Most models based on contemporary brain-scanning research (utilizing such methods as fMRI, MEG, and others discussed below) are only suggestive of the underlying mechanisms. Although these studies are valuable, their crude spatial and temporal resolution is not adequate for reverse engineering the salient features of the brain.

New Tools for Scanning the Brain. Now imagine, in our computer example above, that we are able to actually place precise sensors at specific points in the circuitry and that these sensors are capable of tracking specific signals at very high speeds. We would now have the tools needed to follow the actual information being transformed in real time, and we would be able to create a detailed description of how the circuits actually work. This is, in fact, exactly how electrical engineers go about understanding and debugging circuits such as computer boards (to reverse engineer a compet.i.tor's product, for example), using logic a.n.a.lyzers that visualize computer signals. Now imagine, in our computer example above, that we are able to actually place precise sensors at specific points in the circuitry and that these sensors are capable of tracking specific signals at very high speeds. We would now have the tools needed to follow the actual information being transformed in real time, and we would be able to create a detailed description of how the circuits actually work. This is, in fact, exactly how electrical engineers go about understanding and debugging circuits such as computer boards (to reverse engineer a compet.i.tor's product, for example), using logic a.n.a.lyzers that visualize computer signals.

Neuroscience has not yet had access to sensor technology that would achieve this type of a.n.a.lysis, but that situation is about to change. Our tools for peering into our brains are improving at an exponential pace. The resolution of noninvasive brain-scanning devices is doubling about every twelve months (per unit volume).31 We see comparable improvements in the speed of brain scanning image reconstruction: The most commonly used brain-scanning tool is fMRI, which provides relatively high spatial resolution of one to three millimeters (not high enough to image individual neurons) but low temporal (time) resolution of a few seconds. Recent generations of fMRI technology provide time resolution of about one second, or a tenth of a second for a thin brain slice.

Another commonly used technique is MEG, which measures weak magnetic fields outside the skull, produced princ.i.p.ally by the pyramidal neurons of the cortex. MEG is capable of rapid (one millisecond) temporal resolution but only very crude spatial resolution, about one centimeter.

Fritz Sommer, a princ.i.p.al investigator at Redwood Neuroscience Inst.i.tute, is developing methods of combining fMRI and MEG to improve the spatiotemporal precision of the measurements. Other recent advances have demonstrated fMRI techniques capable of mapping regions called columnar and laminar structures, which are only a fraction of a millimeter wide, and of detecting tasks that take place in tens of milliseconds.32 fMRI and a related scanning technique using positrons called positron-emission tomography (PET) both gauge neuronal activity through indirect means. PET measures regional cerebral blood flow (rCBF), while tMRI measures blood-oxygen levels.33 Although the relations.h.i.+p of these blood-flow amounts to neural activity is the subject of some controversy, the consensus is that they reflect local synaptic activity, not the spiking of neurons. The relations.h.i.+p of neural activity to blood flow was first articulated in the late nineteenth century. Although the relations.h.i.+p of these blood-flow amounts to neural activity is the subject of some controversy, the consensus is that they reflect local synaptic activity, not the spiking of neurons. The relations.h.i.+p of neural activity to blood flow was first articulated in the late nineteenth century.34 A limitation of tMRI, however, is that the relations.h.i.+p of blood flow to synaptic activity is not direct: a variety of metabolic mechanisms affect the relations.h.i.+p between the two phenomena. A limitation of tMRI, however, is that the relations.h.i.+p of blood flow to synaptic activity is not direct: a variety of metabolic mechanisms affect the relations.h.i.+p between the two phenomena.

However, both PET and tMRI are believed to be most reliable for measuring relative changes in brain state. The primary method they use is the ”subtraction paradigm,” which can show regions that are most active during particular tasks.35 This procedure involves subtracting data produced by a scan when the subject is not performing an activity from data produced while the subject is performing a specified mental activity. The difference represents the change in brain state. This procedure involves subtracting data produced by a scan when the subject is not performing an activity from data produced while the subject is performing a specified mental activity. The difference represents the change in brain state.

An invasive technique that provides high spatial and temporal resolution is ”optical imaging,” which involves removing part of the skull, staining the living brain tissue with a dye that fluoresces upon neural activity, and then imaging the emitted light with a digital camera. Since optical imaging requires surgery, it has been used mainly in animal, particularly mouse, experiments.

Another approach to identifying brain functionality in different regions is transcranial magnetic stimulation (TMS), which involves applying a strong-pulsed magnetic field from outside the skull, using a magnetic coil precisely positioned over the head. By either stimulating or inducing a ”virtual lesion” of (by temporarily disabling) small regions of the brain, skills can be diminished or enhanced.36 TMS can also be used to study the relations.h.i.+p of different areas of the brain on specific tasks and can even induce sensations of mystical experiences. TMS can also be used to study the relations.h.i.+p of different areas of the brain on specific tasks and can even induce sensations of mystical experiences.37 Brain scientist Allan Snyder has reported that about 40 percent of his test subjects hooked up to TMS display significant new skills, many of which are remarkable, such as drawing abilities. Brain scientist Allan Snyder has reported that about 40 percent of his test subjects hooked up to TMS display significant new skills, many of which are remarkable, such as drawing abilities.38 If we have the option of destroying the brain that we are scanning, dramatically higher spatial resolution becomes possible. Scanning a frozen brain is feasible today, though not yet at sufficient speed or bandwidth to fully map all interconnections. But again, in accordance with the law of accelerating returns, this potential is growing exponentially, as are all other facets of brain scanning.

Carnegie Mellon University's Andreas Nowatzyk is scanning the nervous system of the brain and body of a mouse with a resolution of less than two hundred nanometers, which is approaching the resolution needed for full reverse engineering. Another destructive scanner called the ”Brain Tissue Scanner” developed at the Brain Networks Laboratory at Texas A&M University is able to scan an entire mouse brain at a resolution of 250 nanometers in one month, using slices.39

Improving Resolution. Many new brain-scanning technologies now in development are dramatically improving both temporal and spatial resolution. This new generation of sensing and scanning systems is providing the tools needed to develop models with unprecedented fine levels of detail. Following is a small sample of these emerging imaging and sensing systems. Many new brain-scanning technologies now in development are dramatically improving both temporal and spatial resolution. This new generation of sensing and scanning systems is providing the tools needed to develop models with unprecedented fine levels of detail. Following is a small sample of these emerging imaging a

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