I was a young course instructor in seminars for medical students and I taught neuroscience by the book. Sensory stimuli from the eyes, ears, and such are converted to electrical signals and then transmitted to the sensory cortex that process these inputs and induce perception. The motor cortex sends impulses to the spinal cord to start a movement.
The textbook explanations of the brain were well received by most students. The clever ones always asked a series of awkward questions.
Like other young researchers, I began my investigation of the brain without worrying about whether the framework was right or wrong. I was happy for many years with my own progress and the spectacular discoveries that gradually evolved into what became known in the 1960s as the field of neuroscience. I had to wrestle with the difficulty of explaining something that I didn't understand.
I realized over the years that this frustration wasn't my own. Many of my colleagues felt the same way. There was a bright side, though, because of the frustration. Over the years, they pushed me to develop a perspective that gives an alternative description of how the brain interacts with the outside world.
The question of what, exactly, is the mind is a challenge for me and other neuroscientists. Since the time of Aristotle, philosophers have assumed that the soul or the mind is a blank slate, a tabula rasa on which experiences can be painted. The view has influenced thinking in many areas. It has also influenced psychology and cognitive science in the past century. The mind is a tool for learning about the true nature of the world. According to the alternative view, the primary preoccupation of brain networks is to maintain their own internal dynamics and generate nonsensical patterns of neural activity. When a seemingly random action gives a benefit to the organisms survival, the neuronal pattern leading to that action gains meaning. The sound of the baby's cry acquires meaning when the parent happily offers the baby Teddy. Recent progress in neuroscience has lent support to this framework.
The blank slate framework was given to neuroscience by early philosophers. We are still looking for neural mechanisms that might relate to their dreams. The outstanding discoveries of David Hubel and Torsten Wiesel, who were awarded the Nobel Prize in Medicine in 1981 for their work on the visual system, show the dominance of the outside-in framework. In their signature experiments, they showed animals images of various shapes and recorded their neural activity. The physical qualities of moving lines, edges, light or dark areas elicited firing in different sets of neurons. The assumption was that simple patterns are synthesised into more complex ones. The features are bound together in the brain to represent an object. There is no need for active participation. The brain does this exercise.
The outside-in framework presumes that the brain's fundamental function is to perceive signals from the world and correctly interpret them. An additional operation is needed to respond to the signals if this assumption is true. A hypothetical central processor that takes in sensory representations from the environment and makes decisions about what to do with them to perform the correct action is wedged between perceptual inputs and outputs.
What is the central processor in this paradigm? The entity is called free will, homunculus, decision maker, executive function, intervening variables or just a black box. All of these concepts refer to the same thing.
The implication of the outside-in framework is that the next frontier for progress in neuroscience should be to find where the central processor resides in the brain. One of the most popular focuses in neuroscience is the physiology of decision-making. The place where all things come together is believed to be the prefrontal cortex.
This approach can't explain how a single photon can transform into a recollection of a summer outing. The outside-in framework requires a human experimenter to observe the event. The experimenter-in-the-middle is needed because even if the firing patterns of the neurons are changed by light or sound, these changes are not permanent. The visual cortex has no clue about the image of a rose. The appearance of a flower is not what they do. They simply generate electrical oscillations in response to inputs from other parts of the brain.
The events that happen in the world cannot be seen by the sensory cortical areas and the central processor. The changes in firing patterns are not understood by the brain. The neurons that take this all in are unaware of the events that caused the changes in their firing patterns. Fluctuations in activity in the brain are meaningless for a scientist who is in a position to observe both events in the brain and outside world.
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Because of their lack of access to the outside world, neurons need a way to compare their firing patterns to something else. The ability of the brain to assign meaning to changes in firing patterns is referred to asgrounding. This activity is connected to something else to accomplish this task. The only source of a second opinion in the brain is the letter.
The sticks that look bent in water are not broken by moving them. The distance between two trees and two mountain peaks may appear the same, but by moving around and shifting our perspective we learn the difference.
The outside-in framework follows a chain of events. In this model, the sensory areas of the brain are driven by environmental signals and cannot relate their activity to something else. The brain doesn't go one by one through each step. A message known as a corollary discharge is sent to the rest of the cerebral cortex when a person takes an action.
Two tasks are assigned to the neural circuits that initiate an action. The first thing to do is send a command to the muscles that control the eyes and other bodily sensors. The circuits orient bodily sensors in the optimal direction for in-depth investigation of the source of an input and enhance the brain's ability to identify initially ambiguous signals from the senses.
The second task of these same action circuits involves sending notifications. Think of them as mail receipts. If a flower is moving in the wind or is being handled by a person, the visual areas of the cortex will detect it and disambiguate.
The second opinion sensory circuits need a confirmation that my actions are the agent of change. Without exploration, the signals from the flower alone would never become signals that would give a meaningful description of the flower. We can define perception as what we do, not what we passive take in.
A simple version of the discharge mechanism can be demonstrated. If you want to read this text while covering one of your eyes with one hand, move the other eye gently from the side with the tip of your finger at about three times per second. The page is moving quickly. Nothing seems to move when you are looking around the room. The visual system sends a signal to the brain to indicate whether the world is moving or not, and that's why this happens.
When used to explain the mechanisms of learning, the contrast between outside-in and inside-out approaches becomes striking. The blank slate model assumes that the complexity of the brain grows with experience. Brain circuits should become more complex as we learn. Experience is not the main source of the brain's complexity in the inside-out framework.
Instead the brain organizes itself into a series of firing patterns. A dictionary filled with nonsensical words can be likened to this self-organized brain model. New experience doesn't change the way these networks function. Learning takes place through a process of matching the events in the world with the neuronal trajectory.
The advantages and constraints brain dynamics impose on experience are what we need to understand the matching process. In its basic version, models of blank slate networks assume a bunch of the same cells. The presumption is that brain circuits are plastic and that any input can change their activity. An example from the field of artificial intelligence shows the fallacy of this approach. The branch known as connectionism is the basis for artificial neural networks. Alan Turing, the great pioneer of mind modeling, promoted this view in the 20th century.
Artificial neural networks built to write their own inputs onto a neural circuit often fail because each new input modifies the circuit's connections and dynamics. The circuit is said to change shape. There is a pitfall. The artificial intelligence system can wipe out all stored memories at an unpredictable point, a bug known as catastrophic interference, which is an event a real brain never experiences.
The inside-out model suggests that self-organized brain networks should not be disrupted. They should exhibit plasticity when needed. The way the brain strikes this balance is related to the strength of the connections between different groups of neurons. There are connections among neurons. A small subset of neurons retain robust links, whereas most are weakly connected to others. The strongly connected minority is always alert. It fires rapidly, shares information readily within its own group, and resists any changes to its circuitry. Because of their high communication speeds, the elite subnetworks are well informed about the events in the brain.
The rich club makes up 20% of the total population of neurons, but it is in charge of half of the activity. The neural club of the brain is slower to fire and weakly connected than the rich club. They are plastic and can change the connection points between the neurons.
Rich and poor clubs are important for maintaining brain dynamics. The rich club fire members respond to diverse experiences. Under most conditions, they offer fast, good-enough solutions. We can make good guesses about the unknown because our brains always make a guess about a new event. The brain always relates the new to the old. It generalizes. Even an inexperienced brain has a lot of brain activity at the ready, which can be used to match events in the world to existing brain patterns. A brain that remakes itself constantly is unable to adapt quickly to fast-changing events outside.
The plastic, slow-firing-rate neurons have a critical role to play. When something important to the organisms is detected and needs to be recorded for future reference, these neurons come into play. They use their vast reserve to change the strength of some connections to other neurons in order to capture subtle differences between one thing and another. Children learn the meaning of the word after seeing different types of canines. When a kid sees a sheep for the first time, they should understand the difference between a pet and a livestock.
I didn't set out to build a theory in opposition to the outside-in framework as an experimenter. I realized that the brain is more occupied with itself than with what is happening around it after I started studying it. This realization led to a new research agenda for my lab. Our experiments, along with findings from other groups, showed that the brain's constantly changing internal states are not controlled by stimuli.
During the course of natural selection, organisms adapt to the ecological niches in which they live and learn to predict the likely outcomes of their actions in those niches. Neural computations are inserted between motor outputs and sensory inputs as brain complexity increases. Predicting planned actions in changing environments and long time scales is possible thanks to this investment. When sensory inputs disappear temporarily, more sophisticated brains organize themselves to allow computations to continue. When you close your eyes, you still know where you are because of the activity in your brain. This disengaged mode of activity provides a gateway to a variety of cognitive processes.
An example of a disengaged mode of brain operation can be found in the temporal lobe, an area that includes the hippocampus, the nearby entorhinal cortex and related structures involved with multiple aspects of navigation.
Our research builds on leading theories of the functions of the Hippocampal system, such as the discovery of John O'Keefe of University College London. The firing of hippocampal neurons coincides with the location of an animal. The place cells are known for that.
When a rat walks through a maze, its place cells become active in a sequential chain. One can conclude from that observation that the constant changing of sensory inputs from the environment is in line with the outside-in model.
The same networks are used for our internal worlds that keep track of personal memories, engage in planning and imagine future actions. It is clear from an inside-out perspective that navigation through a physical space or a landscape that is only in the imagination is processed by the same neural mechanisms.
Fifteen years ago, my lab was going to explore the mechanisms of spatial navigation and memory in the hippocampus to contrast the outside-in and inside-out frameworks. Rats were trained to alternate between the left and right arms of a maze to find water. The rat had to run in a wheel for 15 seconds at the beginning of the maze to make sure that it could remember the maze routes and not the environment. We reasoned that if the Hippocampal neurons are present in the maze corridors and the wheel, a few should fire at each spot. The duration of neuronal firing should be the same at all locations, including inside the wheel, if the firing is generated by internal brain mechanisms that support navigation and memory.
The results of the experiments defy outside-in explanations. There were not a single neuron fired during the wheel running. Many neurons were fired in a continuous sequence.
The animal's body was not displaced while at the single location of the running wheel, so these neurons could not be called place cells. The firing patterns of individual neurons in this neuronal trajectory could not be distinguished from the firing patterns of the active neurons in the maze.
When we sorted individual trials according to the rat's future choice of left or right arms, the neuronal trajectory were different. There was no chance that the neuronal sequence came from counting steps, estimating muscular effort or some other undetected feedback from the body. We were able to predict the maze arm choice from the moment the animal entered the wheel and throughout wheel running, a period in which the rat had to keep in mind the previously visited arm. The animals had to choose the alternate maze arm to get their rewards.
The idea that the brain's ability to walk to the supermarket governs mental travel is a result of these experiments. The series of events that make up personal recollections are known as episodic memories.
Explanations of past events are not the only recollections of episodic memories. They let us plan for the future. They are a kind of search engine that allows us to look at both the past and the future. A broadening in nomenclature is presaged by this realization. These experiments show that progressions of place cell activity can be generated from pre configured sequence. They can be called place cells, memory cells or planning cells depending on the circumstance.
When an animal is milling around doing nothing, consuming a reward or just sleeping, the brain activity in the cortex of the animal's brain supports the importance of disengaged circuit operations. As a rat rests in the home cage after a maze exploration, its hippocampus creates short, self-organized neuronal trajectories. The same neurons that were firing during several seconds of maze running, recapitulating the neuronal sequence that occurred during maze traversals, are brought back to life by these sharp wave ripples. Our long-term memories are formed by sharp wave-ripple sequences. Alteration of wave-ripple events by experimental manipulations or disease can result in serious memory impairment.
Experiments done in humans and animals over the past decade show that the time-compressed ripple events constitute an internal trial-and-error process that subconsciously creates real or fictive alternatives for making decisions about an optimal strategy, constructing novel inferences and planning ahead for future actions without having to. Our thoughts and plans are deferred actions, and our brain activity is an essential brain operation. The outside-in theory doesn't try to assign a role to the disengaged brain when it is at rest or in the midst of sleep.
The inside-out approach has a number of practical applications. It could help in the search for better diagnostic tools. Current terminology fails to describe the underlying biological mechanisms of mental and neurological illnesses. Lack of understanding of pathological mechanisms and their relation to symptoms and drug responses has hindered the understanding of the problem by psychiatrists.
The inside-out theory is an alternative to some of the most prevalent connectionist models. A substitute for them might build models that maintain their own self-organized activity and that learn by matching their circuitry. Machines constructed this way could disengage their operations from the inputs of electronic sensors and create novel forms of computation that resemble internal cognitive processes.
Neural processes that disengage from the senses go hand in hand with mechanisms that promote interactions with the surrounding world. All brains use the same basic principles. The essence of cognitive activity is disengaged neural activity. When my medical students asked legitimate questions, I brushed them off too quickly.
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