That favorite song, a fond romantic moment, the solution to an algebra problem learned in college, all are stored in about 3 pounds of squishy, jellylike matter inside your skull.

Amid all of the human brain’s amazing abilities, that ability to store and retrieve memory over a lifetime is one of the most puzzling. And according to a study led by Salk Institute scientists, that ability is far greater than previously thought.

Using rat neurons as a proxy for human brain cells, the study measured the storage capacity of synapses, the neural connections believed to store memories. It found that on average each synapse can hold about 4.7 bits of information.

Scaled up to the size of the human brain, this number implies a storage capacity of about one petabyte, or 1,000,000,000,000,000 bytes, said Salk scientist Tom Bartol, who figured out a way to deduce synaptic information. That’s about 10 times greater capacity than previously thought, he said.


By comparison, one petabyte can hold 333,333 human genomes, which come in around 3 gigabytes each. In terms of music, one petabyte can hold 333,333,333 songs averaging 3 megabytes.

The study, published in the journal eLife, also sheds light on how the brain can manage massive amounts of data with very little energy, and avoids conceptual traps that can stymie machine learning algorithms, said Bartol, the study’s first author. Noted brain researcher Terry Sejnowski was senior author. The study can be found at j.mp/synapti.

Terry Sejnowski, Calley Bromer and Tom Bartol. ( / Salk Institute)

The capacity estimate probably understates the real case, said Paul J. Reber, director of the Brain, Behavior, & Cognition program in the psychology department at Northwestern University. Reber, who described the science behind the study as “robust,” said he thinks the true number is probably between 3 to 5 petabytes.


“But importantly, the sum total of all the information in the brain is not really a sensible number until you think about what/how you are using it for,” Reber said by email.

Bartol said that many synapses participate intermittently in learning, which mirrors an approach used by machine learning software. By turning off some artificial neurons, the machine learning process avoids stumbling into false generalizations.

Energy saving

That pitfall is called “overfitting,” which takes place when statistical models include too much complex data. The search for an association can stumble into an invalid relationship, a sort of data mirage. And it can also waste energy.


“In neuroscience, we’ve known for a long time that when one neuron sends a signal to another, it’s only able to get a message across 10 to 20 percent of the time on average,” Bartol said. “And the percentage it’s able to get a message across is proportional to how strong the synapse is.”

It’s been unclear just what purpose this intermittent strategy served. The study indicates this approach might be a way of conserving energy used by synapses.

“If its only active 10 or 20 percent of the time, then its energy use is 80 percent lower than if it were active all the time,” Bartol said.

Moreover, the success rates of synaptic connections are averaged over time, allowing for a precise readout of values. It is this average value over time that is significant, not the individual successes and failures.


“The information that is stored is not read out on each individual event, but it’s read out by averaging over many events,” Bartol said. “Our hypothesis is that building something that’s accurate on every event is more (energy) expensive and harder to do than having something that is highly accurate and stable over many events.”

This method of calculating values as the average of many events would also reduce the effect of any one synaptic mistake, making the system more robust than in a traditional computer, in which one erroneous flipped bit could cause the whole program to fail.

The heart of the study concerned measuring how many different sizes of synapses exist, because the number of sizes is related to how much information each synapse can hold.

Synapses adjust their sizes according to the signals they receiveid. So the more sizes available, the more information each synaptic state can hold, Bartol said. It had been thought that synapses had just three size states, small, medium and large, which equates to a base 3 system. (The familiar 1s and 0s of computer code use a base 2 system.)


Researchers found a minimum of 26 synapse sizes in the rat neurons, providing a vastly greater potential for memory storage than earlier envisioned. It’s hard to precisely quantify that increased amount, Bartol said, because of the way the brain was constructed. But by any amount, it’s vastly greater than envisioned before.

Size matters

However, that vast storage comes with practical limitations, Reber said.

“Suppose you had a blank iPod (for music) that had infinite storage capacity. That would be handy, but your music listening would still be constrained by how much music you could get on to the iPod,” Reber said.


“For example, if you had to pay for each song, you’d be limited by money (not storage). And even if you had a few million songs, you might also have some trouble finding the one you were looking for unless you had a really good system for organizing them.”

Another limitation is that compared to the iPod, human memory operates very slowly.

“Imagine it took you an hour to store a song on your infinite iPod,” Reber said. “At that speed, you wouldn’t get very many songs on it, even with its infinite storage capacity. In human memory, the storage process is called ‘consolidation’ and the process runs for many hours after we experience something we will remember (including during sleep).

“Consolidation is the bottleneck that leads to us forgetting a lot of what we experience,” Reber said. “The storage capacity appears to be there, but the rate at which we can store memories is limited, so not everything gets saved.”


However, the human system has the great advantage of using very little energy, Bartol said.

The study didn’t examine human neurons because of the difficulty in getting intact, normal, living human brain tissue, Bartol said. Such samples can’t be ethically collected just for the sake of research, so they would only be available as byproducts of brain surgery. While it is possible to grow brain cells from stem cells in lab cultures, they wouldn’t have grown in a normal environment, and so their synaptic properties might not be comparable to those in an intact brain.

The paper is titled, “Nanoconnectomic upper bound on the variability of synaptic plasticity”, and can be found at j.mp/synapsize. Other authors besides Sejnowski and Bartol are Cailey Bromer and Justin Kinney, also of the Salk, and Michael Chirillo, Jennifer Bourne and Kristen Harris, all of the University of Texas at Austin.