The Storage Dilemma

The human cortex contains an estimated ten billion neurons. Using a cortical connectivity matrix, a form of square matrix used to map the probability of neural connections between nodes and parcels in the cortex, it would take, at a minimum, 10^15 bits to specify the data represented by these neurons. Interestingly, however, the human genome only encodes for 6^10 bits of data. As the genome encodes for the entire structure, function, and operation of the nervous system, including the cortex, how is it possible that the genome can contain so little information and construct such a complex, high-functioning, and large-capacity neural network? (Shuvaev et al. 2024) 

This effect is known as the genomic bottleneck. It is suspected that the genome does not encode neurons one by one, but rather through a set of connectivity rules. For example, rather than explicitly stating which neural cell should connect during stimulus, a class of rules is outlined instead based on chemical surface markers that axons may use to find synaptic destinations. This bottleneck effect is proposed to arise from an evolutionary drive to enable organisms with relatively low-capacity genomes to specify the complex neural circuits required for innate behaviours that improve fitness at birth. (Shuvaev et al. 2024)

Artificial neural networks (ANNs) face a similar dilemma. For example, GPT-2, released in 2019, was estimated to have been trained on 4 billion tokens, compared to GPT-4, released in 2023, which was trained on almost 13 trillion tokens (Samborska 2025). 4 in 5 university students now use generative artificial intelligence (AI), with some models exceeding human baselines on PhD-level questioning, multimodal reasoning, and complex mathematics (Gil and Perrault 2026). Its growing magnitude, capabilities, and presence demand more computational power, complex networking, and, most notably, data compression abilities. 

Artificial Intelligence, by definition, mimics human neural networks. Naturally, it should also be possible to recapitulate natural evolutionary genomic bottleneck compression of neural data in artificial neural networks by emulating complex systems through simple connectivity rules. A study conducted by Cold Spring Harbour Laboratory outlined the possibility of compressing artificial neural networks through genomic bottlenecking to capture essential data, portray complex systems, and enhance performance. (Shuvaev et al. 2024)

Their study found that by dividing machine learning problems into nested loops restricted by a low-information bottleneck with one outer “evolutionary loop” that generates the connectivity rules and an inner generic “learning loop,” the system can find general circuits of rules that can be reused and adapted to different situations. The algorithm neatly parallels Darwinian evolution, where systems that can “learn” a behaviour rapidly that gives them a selective advantage result in the loop being naturally selected for its capacity to learn. The evolutionary loop selects for sparse and functionally important connections, complementary to preexisting compression methods like network pruning. (Shuvaev et al. 2024)

The genomic bottleneck algorithm condensing method, as presented by Shuvaev and peers, shows promise in condensing the information required to output complex systems in ANNs, aiding in improving the efficiency and computational capacities of artificial systems. It serves as a reminder of the synergistic nature of learning, through application of the known in combination with the vastly expanding unknown. 

References

Gil, Yolanda, and Raymond Perrault. 2026. “Artificial Intelligence Index Report.” Stanford University Human-Centered Artificial Intelligence. https://hai.stanford.edu/ai-index/2026-ai-index-report.

Samborska, Veronika. 2025. “Scaling up: how increasing inputs has made artificial intelligence more capable.” Our World in Data. https://ourworldindata.org/scaling-up-ai.

Shuvaev, Sergey, Divyansha Lanchi, Alexei Koulakov, and Anthony Zador. 2024. “Encoding innate ability through a genomic bottleneck.” Proceedings of the National Academy of Sciences. https://mcmaster.primo.exlibrisgroup.com/discovery/fulldisplay?docid=cdi_proquest_journals_3112914517&context=PC&vid=01OCUL_MU:OMNI&lang=en&search_scope=OCULDiscoveryNetwork&adaptor=Primo%20Central&tab=OCULDiscoveryNetwork&query=any,contains,Encoding%20inn.