Learning without a brain – how bacteria store memories and remember like ANNs Research published in PRX Life by a computational biophysicist's lab shows that a single E. coli bacterium can learn from past experience, store memories and use them to prepare for future conditions, with the team tracking tens of thousands of individual cells in a microfluidic device. Bacteria that had just experienced a feast-and-famine environment adapted much faster to the same nutrient pulse than bacteria from a stable environment, indicating a stored internal record of the past rather than a simple reaction to present conditions. A mathematical model of the molecular network controlling bacterial growth pointed to ribosomes as the likely memory storage site. Learning without a brain—how bacteria store memories and remember the past like artificial neural networks Gaby Clark Scientific Editor Andrew Zinin Chief Editor Learning is often thought to require a brain https://theconversation.com/how-does-your-brain-create-new-memories-neuroscientists-discover-rules-for-how-neurons-encode-new-information-254558 . But learning is a broad concept https://doi.org/10.7554/eLife.61907 that does not necessarily depend on neurons https://doi.org/10.1016/j.cub.2026.03.080 . If an organism uses information from past experiences to shape its future https://doi.org/10.3758/s13423-013-0386-3 decisions, it is also learning. Research from my lab, published in the journal PRX Life, shows that even a single bacterium can learn https://doi.org/10.1103/5zbg-8vll from experience, store memories of the past and use those memories to prepare for the future. Keeping track of nutrients Bacteria live in environments that change constantly and on many different timescales https://doi.org/10.1093/femsre/fuaa068 . In the human gut, for example, nutrient levels go up and down, temperatures shift, and antibiotic threats come and go. To survive, a bacterium has to respond quickly to what is happening right now while still preserving useful information about what it recently experienced. Adapting too quickly leaves the bacterium vulnerable to changing conditions, while forgetting too readily makes it unable to anticipate a recurring threat. How does a bacterium manage this balancing act? This question interested me as a computational biophysicist who studies how living systems process information and adapt to changing environments. To investigate whether single-celled organisms such as bacteria can learn from past experience, my colleagues and I used what's called a https://theconversation.com/tiny-laboratories-that-fit-in-your-hand-can-rapidly-identify-pathogens-using-electricity-241184 microfluidic device https://doi.org/10.7554/eLife.88463.4 to track the behavior of tens of thousands of individual E. coli cells as we switched their nutrient supply on and off at different rates. We found that bacteria not only react to current nutrient levels in their environment, they also keep track of their nutrient history https://doi.org/10.1103/5zbg-8vll to cope with changing conditions. If the bacteria were simply reacting to their present environment, they would respond to a sudden pulse of food in exactly the same way, regardless of whether their previous environment was stable or rapidly fluctuating. Instead, when exposed to the same influx of nutrients, bacteria that had just experienced a feast-and-famine environment adapted much faster than bacteria coming from a stable environment. Because the immediate conditions were identical for both bacteria, we reasoned that the difference in their behavior must originate from a stored internal record of their past rather than a simple reaction to their present. In other words, the bacteria's past experiences were shaping their present behavior—they were learning. Where bacteria store memory But where were these cells storing this information? To better understand how bacteria were responding to changes in their nutrient environment, my team and I constructed a mathematical model https://doi.org/10.1103/5zbg-8vll of the internal molecular network https://phys.org/news/2023-01-chatterboxes-bacteria-communicate.html?utm source=embeddings&utm medium=related&utm campaign=internal that controls bacterial growth. Our model not only reproduced how bacteria behaved under different nutrient environments, but also revealed where their memory likely resides. Our model pointed to a component of cells called ribosomes as a potential memory storage site https://doi.org/10.1103/5zbg-8vll . Ribosomes are the molecular factories of cells, building proteins and setting how fast a cell grows https://doi.org/10.1126/science.1192588 . Because bacterial memory revealed itself to us as changes in growth rate, our model located the source of this memory within the factories that determine growth. No other component we tested in the model reproduced this behavior. Some ribosomes responded quickly to nutrient changes, while others changed more slowly. We reasoned that the fast responders track what's happening in the present, while the slow responders retain traces of the past. Together, they give the cell a memory that spans many timescales, from minutes to hours. Bacteria and AI systems Using our mathematical model, we found that this molecular system follows the same basic computational logic as a gated recurrent neural network https://doi.org/10.48550/arXiv.1412.3555 : a type of artificial intelligence used to process sequences such as speech and sensor data. Key to this process is what computer scientists call a gate https://colah.github.io/posts/2015-08-Understanding-LSTMs/ —that is, a molecular switch that decides how much of an existing memory to keep and how much to overwrite when new information arrives. Cells hold onto memory at a cost because maintaining readiness to adapt comes at the expense of growth. If a bacterium is spending its energy adapting to its current environment, it has fewer resources to spend on growth. Bacteria use molecular gates to actively tune how much to retain and how much to discard. Modern AI systems rely on gates to remember important details while staying flexible https://doi.org/10.1162/089976600300015015 enough to learn something new. In the bacterium, the gate is not built from software but from the chemistry within the cell. This strategy also helps solve a problem familiar to AI researchers: how to learn something new without erasing