Neural decoding and mind reading project
To earn credit, a project must meet the following criteria.
|Create an accurate probabilistic model of the influence of the rat's location on the neuron's spikes.|
|Accurately decode the rat's location from the spikes of each neuron and interpret the results.|
|Accurately determine the implications of measuring from the two neurons simultaneously.|
|Project receives credit||YES||NO|
Submit the following by the due date.
- This cover sheet
- Answers to the project questions (typed or handwritten)
Neural decoding is the process of attempting to infer features of a stimulus or other external variable from measurements of neurons' activity. The idea is that the neuronal response is encoding information about the inputs to the brain. If one understood how the information is encoded, then one might be able to go backward and make conclusions about these inputs by analyzing patterns of neuronal activity. This backward inference is at the heart of decoding. Since decoding is an attempt to interpret what the brain's activity means, we can whimsically refer to it as mind reading.
Many sensory neurons encode features of a stimulus by their firing pattern. For example, a visual neuron might be highly likely to fire a spike only when an object is in a certain location of the visual field, or it may spike only when an object has a certain shape, or spike only when an object is moving in a particular direction. Similarly, an auditory neuron might be likely to spike only for sounds at a certain frequency or it might spike only after certain combinations of sounds. When a neuron is likely to spike only when certain stimulus features are present, we can say that the neuron's response is encoding those features. The process of decoding such a sensory neuron attempts to ask the reverse question: what stimulus features were likely present based on an observation of when the neuron spiked?
In this project, rather than attempting to decode stimulus features, we will attempt to decode the location of a rat. The principles are the same, only we rely on different types of neurons. In an area of the brain called the hippocampus are neurons that we refer to as place cells because they are more likely to fire a spike when an animal, we'll say a rat, is in a particular location. We call the region that a particular neuron “prefers” its place field. We can say that the spikes of a place cell encode the location of the rat in the sense that the spikes give evidence that the rat is in that neuron's place field.
When attempting to decode a rat's position based on the spikes of place cells, we have to take different evidence in account. First of all, rats tend to have preferred location where they are more likely to hang out. (For example, a rat would much prefer to stay near a wall of a room rather than mosey around the middle of a room.) Second, spikes of a place cell gives evidence that the rat may be in its place field. Since, ideally, we'd like to measure from multiple place cells simultaneously, we'd need to integrate evidence from multiple neurons. (A third source of evidence should be our estimate of where we thought the rat was a moment ago, as it would be highly unlikely for a rat to be able to teleport across a room in an instant. But, for simplicity, we won't deal with this evidence from multiple time points.)
To combine these source of information, we will use Bayesian inference. We will use the information about where the rat prefers to be as the prior distribution of the rat's location. Then, using Bayes' Theorem, we can update the probability distribution of the rat's location when we receive evidence from the spiking of one or two place cells.
For this project, we will analyze a highly idealized scenario so that we can frame the decoding problem in terms of simple probability. We will starting by imagining that we are measuring whether or not a single neuron (a place cell) in a rat's brain spikes once in one short time window. From this single measurement, we'll attempt to infer the rat's location. We will then look to see if we can improve our decoding by looking at place cells simultaneously. (In a more realistic setting, we should simultaneously measure the spiking activity from many tens of neurons over a period of many seconds, obtaining thousands of spikes from which to decode the rat's location.)
The overarching questions of this project are:
- What types of conclusions about the rat's location can we make from just observing a neuron's spikes?
- What properties of the neuronal spiking enhance or detract from the decoding?
A rat is wandering along a linear track. In its wanderings, the rat prefers to be at the ends of the track. In fact, it spends 50% of the time at the two ends of the track (evenly divided between the two ends) and spends the remaining 50% of the time being equally likely to be anywhere along the rest of the track.
During these wanderings, you record the spiking activity of different neurons. You find two neurons whose firing activity appears to be strongly modulated by the location of the rat along the track (i.e., two place cells). You discover that these neurons are much more likely to fire when the rat is at certain locations along track (i.e., you determine the neurons' place fields). The goal of this project is to determine how well you can decode the rat's location by recording the neurons' spikes.