Oct. 13, 2017

A First Principles Approach to Memory Recall

HBP theoretical neuroscientist Dr Misha Tsodyks is attempting to provide a first principles approach to understanding the free recall of memory.

With his collaborators Mikhail Katkov and Sandro Romani, Tsodyks recently published a perspective in Neuron that sought to demonstrate a first principles approach to understanding memory retrieval.

The theory is based on two principles:

  1. An encoding principle, which states that an item is encoded in the brain by a specific group of neurons in a dedicated memory network. When it is recalled, this specific group of neurons is activated.
  2. The associativity principle, which states that in the absence of sensory cues, a retrieved item plays the roles of an internal cue that triggers the retrieval of the next item.

When one set of neurons in a memory network is activated, it triggers the retrieval of the next item. The stronger the connections between two given memory networks, the more likely one will trigger the other.

The Neuron paper describes this process in what Tsodyks says is fairly simple mathematics. The value of this description is that it can produce predictable, testable results, something which is fairly rare in this area of neuroscience, he says.

“The surprising thing is that by applying it in an analytical framework, you get really clear predictions. If you are trying to remember something that has no structure, e.g. a list of words, or the movies you have watched, and if you apply this idea of sequential triggering, then you can predict how many things you are going to remember out of a list of a certain size.”

When tested against classic free recall experiments, Tsodyks’ formula produced results close to those observed. Further experiments are planned, and Tsodyks is hopeful that the theory will hold.

One of the surprising predictions of the model was that each word participants were exposed to in the free recall experiment had an intrinsic recall probability. Furthermore, the retrieval of easy words leads to a reduction of the overall number of retrieved words, due to the circuits following strong connections around in a circle.

Because memory is so important to how the brain functions, Tsodyks believes theories of its processes will help in other areas. Indeed, he sees no fundamental difference between memory, thought or talking.

“I think thinking and talking are memory recall. The way I think about it is that thinking and talking are actually the same process. Because the way I think it works is that every thought you have is the signal for the next, every idea is linked to another one.”

“In Neuroscience, there is nothing you can really predict. We do not know how things really work, and the brain is so complex. Both these things mean you cannot make quantitative predictions.”

The physical world is the same, he says; for example, you cannot predict temperature of the sun. But you can simplify it, and with basic rules you can predict things like how long it takes a ball to fall. His paper on first principles seeks to provide a similar framework.

As the Neuron paper says: “Despite thousands of years of astronomical observations, a clear picture unifying the motion of celestial objects appeared only after the formulation of a very few basic principles, known as Newton’s laws.”

Tsodyks says the theory should be able accommodate more biologically realistic mechanisms and representations with the possibility of still deeper explanatory powers.

Dr Misha Tsodyks works at the Weizmann Institute of Science in Israel. He is part of the HBP’s Subproject 4, whose theories help inform the work of other areas of the HBP such as brain simulation, neuromorphic computing and neurorobotics.

This article is an edited version of a piece published on the HBP's Brain Bytes blog.


Article written by Greg Meylan. Email: gregory.meylan@epfl.ch

Three Steps of Retrieval Process. Each memory item in a list of four is represented by a randomly chosen population of neurons, here illustrated by blue circles. The size of the circle reflects the number of neurons representing a given item; the size of the overlap between the circles (darker area) reflects the number of neurons representing two given items. An arrow shows one retrieval step from one recalled item to the next chosen according to the size of the overlaps. Image and caption courtesy Misha Tsodyks/Neuron.


Read Misha Tsodyk's more recent paper 'Memory States and Transitions between Them in Attractor Neural Networks" published in Neural Computation October 2017