Tag Archives: human brain

How the brain learns Gorgy Buzsaki

The following is from Gorgy Buzsaki  Scientific American, June 2022 pp 38-42a neuro scientists about aspects of the brain. One part struck me, that its often thought that the brain grows and expands as it learns. Buzsaki seems to be saying that instead, the brain is already pre-programmed. Through our own actions we can mess it up, but instead of the brain being tabula rasa, it looks like God pre-programs how our brain functions, and still leaving it so we can mess it up.

“…Most students were happy with my textbook explanations of the brain’s input-output mechanisms. Yet a minority – the clever ones – always asked a series of awkward questions. ‘Where in the brain does perception?’ ‘What initiates a finger movement before cells in the motor cortex fire?’ I would always dispatch their queries with a simple answer: ‘hat all happens in the neocortex.’ Then I would skillfully change the subject or use a few obscure Latin terms that my students did not really understand but that seemed scientific enough o that my authoritative sounding accounts temporarily satisfied them.

Like other researchers, I began my investigation of the brain without worrying much whether this perception-action theoretical 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.’ Yet my inability to give satisfactory answers to the legitimate questions of my smartest students has haunted me ever since. I had to wrestle with he difficulty of trying to explain something that I didn’t really understand.

Over the years I realized that this frustration was not uniquely my own. Many of my colleagues, whether they admitted it or not, felt the same way. There was a bright side, though, because these frustrations energized my career. They nudged me over the  years to develop a perspective tha provides an alternative description of how the brain interacts with the outside world…

…The contrast between outside-in and inside-out approaches becomes most striking when used to explain the mechanisms of learning. A tacit assumption of the blank slate model is that the complexity of the brain grows with the amount of experience. As we learn, the interactions of brain circuits should become increasingly more elaborate. In the inside-out framework, however, experience is not the main source of the brain’s complexity.

Instead the brain organizes itself into a vast repertoire of preformed patterns of firing known as neutonal trajectories. This self-organized brain model [??? -mine, self-organized, how does that happen?] can be likened to a dictionary filled initially with nonsensical words. New experience does not change the way these networks function – their overall activity level, for instance. Learning takes place, rather, through a process of matching the preexisting neuronal trajectories to events in the world.

To understand the matching process, we need to examine the advantages and constraints brain dynamics impose on experience. In its basic version, models of blank slate neuronal networks assume a collection of largely similar randomly connected neurons. The presumption is that brain circuits are highly plastic and that any arbitrary input can alter the activity of neuronal circuits. We can see the fallacy of this approach by considering an example from the field of artificial intelligence. Classical AI research – particularly the branch known as connectionism, the basis for artificial neural networks – adheres to the outside-in, tabula rosa model. This prevailing view was perhaps most explicitly promoted in the 20th century by Alan Turing, the great pioneer of mind modeling: ‘Presumably the child brain is something like a notebook as one buys it from the stationer’s, ‘ he wrote.

Artificial neural networks built to ‘write’ inputs onto a neural circuit often fail because each new input inevitably modifies the circuits connections and dynamics. The circuit is said to exhibit plasticity. But there is a pitfall. While constantly adjusting the connections in its networks when learning, the AI system, at an unpredictable point, can erase all stored memories – a bug known as catastrophic interference, an even a real brain never experiences.

The inside-out model in contrast, suggests that self-organized brain networks should resist such perturbations. Yet they should also exhibit plasticity selectively when needed. The way the brain strikes this balance relates to vast differences in the connection strength of different groups of neurons. Connections among neurons exit on a continuum. Most neurons are only weakly connected to others whereas a smaller subset retains robust links. The strongly connected minority is always on the alert. It fires rapidly, shares information readily within its own group, and stubbornly resists any modifications to the neurons’ circuitry. Because of the multitude of connections and their high communication speeds, these elite subnetworks, sometimes described as a ‘rich club,’ remain well informed about neuronal events throughout the brain.

The hard-working rich club makes up roughly 20 percent of the overall population of neurons, but it is in charge of nearly half of the brain’s activity. In contrast to the rich club, most of the brain’s neurons – the neural ‘poor club’ – tend to fire slowly and are weakly connected to other neurons. But they are also highly plastic and able to physically alter the connections points between neurons, known as synapses.

Both rich and poor clubs are important for maintaining brain dynamics. Members of the ver ready rich club fire similarly in response to diverse experiences. They offer fast, good-enough solutions under most conditions. We can make good guesses about he unknown not because we remember it but because our brains always make a surmise about a new, unfamiliar event. Nothing is completely novel to the brain because it always relates the new to the old. It generalizes. Even an inexperienced brain has a vast reservoir of neuronal trajectories at the ready. Offering opportunities to match events in the world to preexisting brain patterns without requiring substantial reconfiguring of connections. A brain that remakes itself constantly would be unable to adapt quickly to fast changing events in the outside world.

But there also is a critical role for the plastic, slow-firing-rate neurons. These neurons come into play when something of importance to the organism is detected and needs to be recorded for future reference. They then go on to mobilize their vast reserve to capture subtle differences between one thing and another by changing the strength of some connections to other neurons. Children learn the meaning of the word ‘dog’ after seeing various kinds of canines. When a youngster sees a sheep for the first time, they may say ‘dog’. Only when the distinction matters – understanding the difference between a pet and livestock – will they learn to differentiate….

…neurons devote most of their activity to sustaining the brain’s perpetually varying internal states rather than being controlled by stimuli impinging on our senses…”