Cognitive Neuroscience: Memory and Learning

Introduction¶

This content is based on #189 of the Mindscape podcast, hosted by Sean Carroll, an American theoretical physicist and professor specializing in quantum mechanics, cosmology, and the philosophy of science. Carroll is currently affiliated with Johns Hopkins University. In this episode, his guest is Dr. Jeff Lichtman, an American neuroscientist and Professor of Molecular and Cellular Biology and Arts and Sciences at Harvard University.

Dr. Lichtman is renowned for developing a groundbreaking neuroimaging technique called Brainbow. When combined with serial electron microscopy, Brainbow enables the creation of detailed 3D reconstructions of a brain’s neural wiring. This comprehensive map of all neural connections, known as a connectome, represents a major advancement in neuroscience, with Dr. Lichtman as one of its pioneering contributors.

The episode delves into four key themes: our current understanding of how the brain works, the science behind Brainbow, the potential applications of connectomics, and the nature of consciousness.

What is Our Current Understanding of How the Brain Works?¶

What is our current of how the brain works? In Prof. Lichtman’s opinion, this question is perhaps best answered through analogy. Imagine you are a mountain climber attempting to summit Mount Everest. After climbing just three feet, how much progress have you made? Relative to the starting point, it might seem infinite, but in reality, it’s a very small fraction of the journey. Similarly, when it comes to understanding the brain in complete detail, we don’t even know how tall the metaphorical mountain is. Despite this immense challenge, scientists have still made significant strides in uncovering much of how our neurobiology works.

The basic anatomy of the brain consists of: X Glial Cells > N Neurons.
$\quad$Glial cells support neural function,
$\quad$but Neurons perform the processing and are the key.

The basic function of the brain is to:
$\quad$a. receive input from sensory organs,
$\quad$b. process input information,
$\quad$c. and execute a response to input.

That is essentially what every single neuron does as well, but
$\quad$Neurons receive input at synaptic sites (1–10,000 sites) on the dendrites.
$\quad$$\quad$ONLY IF the total input signal @ synapse $\rightarrow$ <em>local</em> membrane depolarization > some threshold potential $\rightarrow$ signal propagates to the cell body.

$\quad$Incoming signals from dendrites are integrated across the cell body $\rightarrow$ cell body membrane depolarization.
$\quad\quad$ONLY IF depolarization > some threshold potential $\rightarrow$ generation of an output signal at the Axon Hillock and down the axon.
$\quad\quad\quad$ Axon lengths of neurons vary from microns to meters.

When it comes to brain function in general (i.e., response to stimuli), a neuron’s connectivity and the strength of its connections within a group play a critical role. The more connected and influential a neuron becomes, the more essential it is to the function of the neural circuit.

Thus, both a neuron’s role within circuits and its mechanism of operation are crucial to overall circuit function:

$\quad$Wiring (structural connectome) determines the flow of information across neural networks, AND
$\quad$Programming (functional connectome) determines a neuron’s response to stimuli by modulating its excitability or inhibition.

$\because$ A neuron’s sensitivity (i.e., synaptic strength) and response (output) can change—becoming more excitable or sensitized—even without changes to the wiring itself. This is influenced by experience (activity), meaning each neuron’s programming is unique due to variability in experience.

We do not fully understand how neurons communicate. Some neurons respond to excitation with a single action potential, while others respond with bursts of multiple action potentials. The strength of excitatory input signals is encoded in the frequency of action potentials, akin to switching between AM (amplitude modulation) and FM (frequency modulation) in radio. Input is encoded in output frequency.

These differences in input-output modulation depend on the specific types of channels present in the neuron’s membrane, which regulate ion flow and ultimately determine signal properties. This membrane system is highly complex and nonlinear.

In nature, if something can be useful, evolution has likely taken advantage of it. However, it’s not designed to be understood—it just needs to work.

Modeling membrane dynamics is extremely complicated. Therefore, it’s often easier to model a neuron’s response through direct stimulation, such as step-function depolarizations.

The human brain contains an estimated 86 billion neurons. Each neuron forms connections ranging from a few to tens of thousands with other neurons, resulting in trillions of synapses. Interestingly, our brains are neither particularly large nor do they contain an unusually high number of neurons compared to other animals. We are encephalized however, meaning we have a relatively large brain in proportion to our body size.

In addition, compared to other animals, humans have a significant amount of association cortex, which is primarily involved in cognitive processing. The brain’s functional capacity, appears to depend on both the number and size of neurons. Importantly, more or bigger is not always better.

According to Lichtman, there is no magic involved in understanding what a human being is—apart from the enormous complexity of the brain. However, the ability to "understand" depends greatly on how one defines the term.

I think most people’s understanding of the word "understand" implies a shorthand—a compressed version of complexity—where, once you grasp the gist of something, you no longer need the details because now you "get it."

I propose an alternative: certain things in the world, are in their most concise form, and not simplifiable.

If there were a simpler state, the brain would already exist in it.

How to Reconstruct the Structural Connectome¶

Figure 1 – 50 of largest neurons in the fly’s brain connectome. source

The Human Connectome Project mapped the structural (wiring) and functional (information flow between areas) aspects of the human brain in a macro connectome—also known as the Projectome—in 2012. However, to understand how neural information translates into behavior, it is necessary to examine the structural and functional microwiring, which constitutes the official Connectome.

In general, this process requires two key techniques: Brainbow and serial section electron microscopy. The procedure begins by preserving the brain in a paraformaldehyde resin at the time of death * to minimize neural network decay. The brain is then sliced into thin sections, 10–30 nm thick, using a diamond blade. These slices are stained with Brainbow, a multiplex staining protocol that enables the resolution and visualization of individual neurons at the synapse level across each slice (visit to understand why this is hard). The stained slices are imaged in series using electron microscopy, allowing the projections of each neuron to be traced across slices. When the images are stacked, they produce a nanometer-resolution 3D reconstruction of all neural wiring.

Despite a large and growing body of knowledge on neural signaling, we currently lack the integration necessary for comprehensive functional connectome modeling.

* This presents ethical challenges in obtaining a complete human brain for scanning.

Connectomics¶

The first complete structural connectome was mapped for a small nematode worm, C. elegans, which has only 300 nerve cells. This effort took 10 years. More recently, in 2024, the structural connectome of the fruit fly (Drosophila melanogaster) was published (figure 1). The next planned milestone is mapping the mouse brain, a project expected to take 5–7 years. Mapping a whole mouse brain will require terabytes of data per section, posing a significant challenge as these terabytes quickly scale to petabytes or even exabytes. Currently, Prof. Lichtman and his colleagues are developing the tools necessary to handle, store, and integrate this immense volume of data.

The human brain, being orders of magnitude larger and more complex than a mouse brain, will generate data on the scale of zettabytes—equivalent to the total digital content of the World Wide Web in a single year.

At present, we simply lack the capacity to process and manage this amount of information.

The ultimate goal of the connectome is to functionally model the human brain, allowing sensory input sent along sensory fibers to result in motor behavior output. However, a major challenge lies in the complexity of a brains neural network, where nuerons can form thousands to tens of thousands of connections and participate in numerous circuits. In addition to processing information, the brain must also store it, adding another layer of complexity.

This challenge is further compounded by latent influences not captured in the structural diagram, such as synaptic strength, the nonlinear responses of cells, the timing of inputs that activate cells (resulting in excitatory or inhibitory effects), and the influence of modulatory neurotransmitter inputs.

While there is still much to be learned from the structural connectome alone, true brain simulations will require integrating with the dynamic processes of information processing that govern input-to-response pathways. There is still much work to be done before this can be achieved.

While the connectomes of worms and fruit flies have some relevance to humans, they are primarily valuable to neuroscience researchers working with these animal models. For instance, despite the fruit fly connectome being published only in mid-2024, several studies have already utilized it to explore a range of topics, including the discovery of new circuits (Seung, 2024), elucidation of circuit functions (Sapkal et al., 2024), brain modeling (Shiu et al., 2024), and brain dynamics (Pospisil et al., 2024).

The mouse connectome is anticipated to have even greater utility. Furthermore, the connectome could prove beneficial for advancing artificial neural network architectures. Its first direct human application may lie in studying psychiatric, developmental, and cognitive disorders. There is a possibility that these conditions stem from some form of miswiring in the brain—but what kind of miswiring? Nobody knows. And how could we know without the connectome?

The Easy and Hard Problem of Conciousness¶

The Easy Problem of Consciousness = How do sensory inputs give rise to motor reactions and behavior.