Short-term memory project
Grading rubric
To earn credit, a project must meet the following criteria.
Criterion | Met | Not met |
---|---|---|
Accurately determine the mathematical structure underlying short-term memory storage by the model network | ||
Give a clear interpretation on how this structure is used to store a short-term memory |
Project receives credit | YES | NO |
Submitting project
Submit the following by the due date.
- This cover sheet
- Answers to the project questions (typed or handwritten)
Background
Short-term memory is the capacity to actively remember small amounts of information for a short amount of time. Unlike long-term memory, which is stored by changing the connections among neurons, a short-term memory is maintained through the activity of neurons in a fixed network.
One way to explore the neuronal basis of short-term memory is through measuring the activity of neurons while animals perform tasks that require short-term memory. In one version of such a task, monkeys attempt to remember the location of an object presented on a computer monitor. At the beginning of a trial, no objects appear on the monitor (we'll call it a blank screen). Then, an object is briefly projected on the monitor. During the subsequent delay period, the blank screen is shown again. Although no objects appear on the monitor so that the visual stimulus is identical to that from the beginning of the trial, the monkey must actively remember the object's location in order to report its location at the end of the delay period and receive a reward.
Neurons in an area of the brain called the prefrontal cortex have been observed to be involved in this short-term memory task. These neurons show low activity during the initial blank screen present before an object appears. However, depending on the location of the object that was projected, a neuron may fire at a higher rate during the delay period, even though the visual stimulus is identical to that from the beginning of the trial. This delay period activity of certain neurons appears to coincide with times when the monkey is remembering that the object was in a certain location.
Let's imagine that this delay period activity originates in the prefrontal cortex. (It's possible that the delay period activity originates in another brain region that then sends inputs to the prefrontal cortex, in which case our model would be a better fit to that other brain region.) If we imagine that the input to the prefrontal cortex is the same during the initial blank screen and the delay period, then the prefontal cortex must be able to sustain different patterns of activity in the presence of the same input. The history of input to the prefrontal cortex would determine which activity pattern was activated. In other words, the prefrontal cortex would be exhibiting short-term memory of its input history.
In this project, we'll seek to build and analyze a simplified mathematical model that exhibits some of the properties of a short-term memory network. The overarching questions for this project are:
- What mathematical structure must be present in a model for potential short-term memory storage?
- How can the network use that structure to store a short-term memory?
A neuronal network consists of a myriad of neurons interconnected through a web of connections. Rather than developing a model with thousands of differential equations that track each neuron, our goal will be to capture the influence of the network with a simple two-dimensional model that tracks the activity of just two neurons. (The model could also represent two groups of neurons, but we'll imagine they are individual neurons to keep the vocabulary simple.)
We will represent the activity of each neuron by its firing rate. The output of each neuron is actually a sequence of spikes (called action potentials), and we'll model the rate at which each neuron is emitting these spikes. (We'll measure the rates in spikes per second.) The model for the dynamics of these rates should capture how the input to a neuron increases the neuron's firing rate. With such a simple model, we can then analyze how the network could store a short-term memory.