How does the brain work, and how can we understand it? To approach this big question from a broad perspective, I want to report on some ideas about the brain that marked me most over the past twelve months and that, on the other hand, do not overlap too much with my own research focus. Enjoy the read! And check out previous year-end write-ups: 2018, 2019, 2020, 2021, 2022, 2023, 2024.

During a talk in Basel in 1980, Sydney Brenner stated that “so much progress depends on the interplay of techniques, discoveries and new ideas, probably in that order of decreasing importance” (Brenner, 2002). This statement got converted into the famous quote: “Progress in science depends on new techniques, new discoveries, and new ideas, probably in that order”. What is missing from the famous quote is the word interplay. This omission highlights what scientific research truly needs, and something that proponents of technology development often forget to mention: technology needs to interact with scientific ideas and discoveries in order to become powerful.

Sometimes I get the impression that neurotechnology takes on a life of its own and is developed for its own sake. The fact that the ultimate goal of a specific technology is unattainable seem to discourage interaction with ideas, because those ideas are not within reach, and investments into technology alone seems therefore more reasonable. That is why I want to try today to think through what it would actually mean if we could indeed achieve three different seemingly unachievable experimental goals.

Here comes part I, with parts II and III following in a couple of days.

I. Reconstructing memories and mind from the connectome of a human brain

The idea:
Synaptic connections between neurons can be observed anatomically as long as the spatial precision is high enough, which can be achieved with electron microscopy that can achieve a resolution of a few nanometers. Other methods, such as expansion microscopy (Tavakoli et al., 2025) or X-ray tomography (Bosch et al., 2025), may provide similar resolution in the future, but the basic assumption does not change: the brain must already be dead (preferably not for too long). If it were possible to image an entire human brain with electron microscopy and detect all synapses and their strengths, we would obtain the (synaptic) connectome: all connections between all neurons, including their weights. Like an artificial neural network, this biology-derived network could then be run in a simulation, reconstructing the knowledge and experiential world of the dead brain. There is even a not-so-small community that hopes that such a method could grant them some form of life after death and, therefore, immortality.

Why it won’t work:
In principle, the method itself works very well. In 2025, connectomics was even named “Method of the Year” by Nature Methods (Marx, 2025). Over the past years, connectomics has provided fascinating insights into brain organization, particularly in simpler animals, most notably the fruit fly Drosophila. For the fruit fly, the connectome was approached five years ago (the “hemibrain” dataset) (Scheffer et al., 2020) and nearly completely reconstructed more recently (Dorkenwald et al., 2024; Lin et al., 2024; Matsliah et al., 2024; Schlegel et al., 2024). Since individual neurons are arranged almost identically from fly to fly, this catalog has become extremely useful for the entire fruit fly community.

So why shouldn’t this also work for a mammalian or human brain? For several reasons. First, the human brain is simply too large. Even imaging small brains or brain fragments is currently an enormous challenge. Producing a single high-quality, high-resolution 2D image of brain tissue is not particularly difficult. Maintaining that same quality across thousands, millions, or billions of images is. If we say that one image is 4096×4096 pixels, and we wanted to image at 10 nm resolution in x and y (the fruit fly connectomes use even higher resolutions) and in 20 nm slices in z, we would need to record roughly 10²⁰ imaging voxels for a 1-2 liter volume of human brain tissue, or 6 10¹² (or 6 trillion) images. Which is a lot of images that need to be acquired without mistakes! If even one small brain region hidden in a corner is not well captured, the entire idea of a complete connectome is called into question. Microscopes can fail due to power outages; cutting blades may drag a dust particle across the sample and scratch entire image series; heavy metals used for contrast may not penetrate certain brain regions well enough to reveal synapses. Any such high-precision methods are inherently fragile and error-prone, making them less scalable. When imaging a fruit fly brain, this is manageable – you can simply try again with another brain of another fly until the sample quality is sufficient and your software does not produce a critical crash. But if you want to reconstruct the connectome of a specific human, you get only one attempt.

Second, there is the issue of data analysis. Manually reconstructing a single human neuron takes several days, or, according to more optimistic views (which I do not share), a few hours. The human brain contains roughly 100 billion neurons. Who is supposed to do this work? AI, of course. There are already impressive methods for automated segmentation of neurons and detection of synapses. However, in all cases, manual validation is still required to correct false mergers and reassemble neurons that have been split during segmentation. Researchers have tried to automate this process (Celii et al., 2025; Schmidt et al., 2024), but in practice these steps are still mostly performed by humans. Could AI fully automate this time-critical step? Probably yes. Will it happen in the near future? At present, I see little concrete evidence, only preliminary prototypes and promises.

Third, an open question is how much we would actually learn from the connectome. How much information can be inferred from the anatomical appearance of a synapse? Can we tell which neurotransmitter it uses, or whether the synapse adapts or facilitates to incoming input? How can we reconstruct dynamic activity patterns from static synapses? Can we understand how a synapse on a spine interacts in terms of electrophysiologcally with the dendrite and soma based solely on an electron micrograph? How can we take into account molecular details such as voltage-gated ion channels and their distribution? All of these aspects are under active investigation, but they are far from being resolved. And it remains unclear (with unclear, I mean as unclear as in the halting problem) when they will be understood well enough so we can judge whether a purely anatomical connectome is sufficient to reconstruct memories and the inner life of a brain, be it human or not.

Based on these considerations, I still believe that connectomics is one of several essential methods in basic neuroscience, and certainly one of the most promising. But even if a connectomic anatomical reconstruction worked perfectly, it would not suddenly mean that we can wake up the mind from a dead brain with its dynamics.

What we would learn:
Suppose we could reconstruct all neurons in a human brain and detect all synapses, assigning each a weight that reflects its true functional strength (even though it is still unclear whether this is fully possible). We could systematically revisit and test many existing findings in neuroscience. For example, global connectivity in the human brain is often measured using diffusion tensor imaging (DTI), which relies on anisotropic diffusion of water molecules along fiber bundles. It would be both exciting and important to validate and refine this method using a connectome, improving accuracy by several orders of magnitude.

Even more importantly, a connectome would revolutionize research fields that focus on specific brain regions. For example, there are relatively large research communities for each of the major cortical and non-cortical brain areas such as the basolateral amygdala (processing of emotion and fear), the locus coeruleus (stress and arousal), or the hypothalamus (homeostasis and endocrine control). A recurring question is: where does this region actually receive its inputs from? For that, current neuroscience heavily relies on retrograde (virus) tracing, which labels neurons projecting to a given region. These methods are powerful but not fully reliable, as injections are difficult to restrict spatially, and viruses often have unexplained biases for or against certain neurons’ axons. A connectome would replace all such experiments in one stroke.

But it would go much further. We could examine not only the inputs to a region, but the inputs to those inputs – mapping the full receptive field of a region or even specific cell types. Conversely, we could finally understand the impact of a region or neuron: where do its axons project, and where do the axons of its downstream targets project? Analogous to a receptive area, we could map an impact field in a clear and systematic way.

With a connectome, many theories about the organization of brain regions could be decisively confirmed or discarded. With today’s relatively small datasets from animal experiments, this is already partially possible, though with major limitations, as shown, for example, in work from Moritz Helmstaedter’s lab, testing ideas such as predictive processing or the standard model of the cortical column (Sievers et al., 2024). For open questions like the importance of recurrent connectivity in the hippocampus or olfactory cortex, functional characterization of recurrence currently requires enormous effort with weak statistical power (Guzman et al., 2016; Layous et al., 2025). Right now, we have no good intuition about the prevalance of recurrent connections throughout brain areas and what kind of anatomical recurrences (single-synaptic loops, loops between thalamus and cortex, loops between specific cortical layers). Even a single connectome could clarify these issues immediately.

As with the fruit fly brain, such a dataset would be publicly accessible and would therefore constitute a reference for everyone, enabling any researcher or even interested laypeople to contribute not only ideas but also corrections, and thus making a big contribution to open science.

Overall, it is difficult to overstate the potential impact of a complete connectome. Yet, some scientists manage to do exactly that by claiming it would allow reconstruction of a person’s full memories. That remains far away and just as unrealistic as the promises made by the Blue Brain Project in the early 2000s. It is harder – but necessary – to explain to non-experts why the connectome would not deliver this result, and still be an absolute game changer.

References

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