More on this book
Community
Kindle Notes & Highlights
We realized that the brain’s model of the world is built using maplike reference frames. Not one reference frame, but hundreds of thousands of them. Indeed, we now understand that most of the cells in your neocortex are dedicated to creating and manipulating reference frames, which the brain uses to plan and think.
I find it amazing that the only thing in the universe that knows the universe exists is the three-pound mass of cells floating in our heads.
The neocortex is in a decidedly unfair position, as it doesn’t control behavior directly. Unlike other parts of the brain, none of the cells in the neocortex connect directly to muscles, so it can’t, on its own, make any muscles move. When the neocortex wants to do something, it sends a signal to the old brain, in a sense asking the old brain to do its bidding.
The precise and extremely complex neural circuits seen everywhere in the neocortex tell us that every region is doing something far more complex than feature detection.
The neocortex got big by making many copies of the same thing: a basic circuit.
All the things we think of as intelligence—from seeing, to touching, to language, to high-level thought—are fundamentally the same.
What makes them different is not their intrinsic function but what they are connected to. If you connect a cortical region to eyes, you get vision; if you connect the same cortical region to ears, you get hearing; and if you connect regions to other regions, you get higher thought, such as language.
the function of neocortical regions is not set in stone. For example, in people with congenital blindness, the visual areas of the neocortex do not get useful information from the eyes. These areas may then assume new roles related to hearing or touch.
Humans can do many things for which there was no evolutionary pressure. For example, our brains did not evolve to program computers or make ice cream—both are recent inventions. The fact that we can do these things tells us that the brain relies on a general-purpose method of learning. To me, this last argument is the most compelling. Being able to learn practically anything requires the brain to work on a universal principle.
My brain, specifically my neocortex, was making multiple simultaneous predictions of what it was about to see, hear, and feel.
neocortex learns a model of the world, and it makes predictions based on its model.
The brain creates a predictive model. This just means that the brain continuously predicts what its inputs will be. Prediction isn’t something that the brain does every now and then; it is an intrinsic property that never stops, and it serves an essential role in learning. When the brain’s predictions are verified, that means the brain’s model of the world is accurate. A mis-prediction causes you to attend to the error and update the model.
The brain learns its model of the world by observing how its inputs change over time. There isn’t another way to learn. Unlike with a computer, we cannot upload a file into our brain.
How does the neocortex, which is composed of thousands of nearly identical cortical columns, learn a predictive model of the world through movement?
Thoughts, Ideas, and Perceptions Are the Activity of Neurons
Everything We Know Is Stored in the Connections Between Neurons
If two neurons spike at the same time, they will strengthen the connection between them. When we learn something, the connections are strengthened, and when we forget something, the connections are weakened.
much of learning occurs by forming new connections between neurons that were previously not connected. Forgetting happens when old or unused connections are removed entirely.
Discovery Number One: The Neocortex Learns a Predictive Model of the World
We are not consciously aware of most of the predictions made by the brain unless an error occurs.
our theory says, only those neurons spike and the other neurons are inhibited. Thus, when an input arrives that is unexpected, multiple neurons fire at once. If the input is predicted, then only the predictive-state neurons become active. This is a common observation about the neocortex: unexpected inputs cause a lot more activity than expected ones.
A prediction occurs when a neuron recognizes a pattern, creates a dendrite spike, and is primed to spike earlier than other neurons.
We started with the idea that the neocortex learns a rich and detailed model of the world, which it uses to constantly predict what its next sensory inputs will be. We then asked how neurons can make these predictions. This led us to a new theory that most predictions are represented by dendrite spikes that temporarily change the voltage inside a neuron and make a neuron fire a little bit sooner than it would otherwise. Predictions are not sent along the cell’s axon to other neurons, which explains why we are unaware of most of them. We then showed how circuits in the neocortex that use the
...more
This simple observation, that we perceive objects as being somewhere—not in our eyes and ears, but at some location out in the world—tells us that the brain must have neurons whose activity represents the location of every object that we perceive.
That is what grid cells and place cells do. They create unique maps for every environment.
Neurons take the same amount of time to search through a thousand maps as to search through one.
the brain arranges all knowledge using reference frames, and that thinking is a form of moving. Thinking occurs when we activate successive locations in reference frames.
Cortical grid cells in what columns attach reference frames to objects. Cortical grid cells in where columns attach reference frames to your body.
In some ways, your body is just another object in the world. The neocortex uses the same basic method to model your body as it does to model objects such as coffee cups. However, unlike external objects, your body is always present. A significant portion of the neocortex—the where regions—is dedicated to modeling your body and the space around your body.
The trick is that reference frames don’t have to be anchored to something physical. A reference frame for a concept such as democracy needs to be self-consistent, but it can exist relatively independent of everyday physical things.
The second trick is that reference frames for concepts do not have to have the same number or type of dimensions as reference frames for physical objects such as coffee cups.
It is likely that columns in the neocortex don’t have a preconceived notion of what kind of reference frame they should use. When a column learns a model of something, part of the learning is discovering what is a good reference frame, including the number of dimensions.
Discovering a useful reference frame is the most difficult part of learning, even though most of the time we are not consciously aware of it.
By now I have introduced four uses for reference frames, one in the old brain and three in the neocortex. Reference frames in the old brain learn maps of environments. Reference frames in the what columns of the neocortex learn maps of physical objects. Reference frames in the where columns of the neocortex learn maps of the space around our body. And, finally, reference frames in the non-sensory columns of the neocortex learn maps of concepts.
One person might arrange the facts on a timeline, and another might arrange them on a map. The same facts can lead to different models and different worldviews.
Being an expert is mostly about finding a good reference frame to arrange facts and observations.
Knowledge in the brain is distributed. Nothing we know is stored in one place, such as one cell or one column. Nor is anything stored everywhere, like in a hologram. Knowledge of something is distributed in thousands of columns, but these are a small subset of all the columns.
This is why we call it the Thousand Brains Theory: knowledge of any particular item is distributed among thousands of complementary models.
This is one of the reasons why the binding problem is considered a mystery, but we have proposed an answer: columns vote. Your perception is the consensus the columns reach by voting.
The voting layer wants to reach a consensus—it does not permit two objects to be active simultaneously—so it picks one possibility over the other.
This is how the entire world is learned: as a complex hierarchy of objects located relative to other objects.
The hope is that if we can get computers to outperform humans on a few difficult tasks, then eventually we will discover how to make computers better than humans at every task.
Deep learning networks work well, but not because they solved the knowledge representation problem. They work well because they avoided it completely, relying on statistics and lots of data instead.
Once AI researchers understand the essential role of movement and reference frames for creating AGI, the separation between artificial intelligence and robotics will disappear completely.
It is worth emphasizing again that intelligence cannot be measured by how well a machine performs a single task, or even several tasks. Instead, intelligence is determined by how a machine learns and stores knowledge about the world. We are intelligent not because we can do one thing particularly well, but because we can learn to do practically anything.
In fact, designing a machine to have human-like emotions is far more difficult than designing one to be intelligent, because the old brain comprises numerous organs, such as the amygdala and hypothalamus, each of which has its own design and function. To make a machine with human-like emotions, we would have to recreate the varied parts of the old brain. The neocortex, although much larger than the old brain, comprises many copies of a relatively small element, the cortical column. Once we know how to build one cortical column, it should be relatively easy to put lots of them into a machine to
...more
The neocortex must be attached to something that already has sensors and already has behaviors. It does not create completely new behaviors; it learns how to string together existing ones in new and useful ways.
The neocortex is actively involved in how motivations and goals influence behavior, but the neocortex does not lead.
In summary, an intelligent machine will need some form of goals and motivations; however, goals and motivations are not a consequence of intelligence, and will not appear on their own.
The world we think we live in is not the real world; it is a simulation of the real world. This leads to a problem. What we believe is often not true. Your brain is in a box, the skull. There are no sensors in the brain itself, so the neurons that make up your brain are sitting in the dark, isolated from the world outside. The only way your brain knows anything about reality is through the sensory nerve fibers that enter the skull.
