Visual cortical neurons
Mobile Support Warning
This simulation was designed with a desktop interface in mind, and may not function correctly on smaller screens or mobile devices.
Sensory neurons encode information about stimuli in the timing, and rate, of action potentials. Neurons in different areas of the brain are sensitive to different stimulus properties. For example, in primary visual cortex (V1), neuronal responses are strongly affected by the orientation of a stimulus. For a single neuron, different orientations evoke different rates of spiking; and different neurons have different "preferred" orientations.
The video below shows recordings made in the 1960s from the laboratory of David Hubel and Torsten Wiesel, who first demonstrated orientation selectivity in V1, and won a Nobel Prize for related work on the visual system. You can hear the amplified ‘spiking' activity of a single cortical neuron, and see the visual stimulus that has evoked that activity. As you watch the video, think about the properties of the stimulus that evokes the weakest and strongest neuronal responses.
In the middle temporal area (MT), neuronal responses are strongly affected by orientation, but also by the direction and speed of moving objects. While each direction evokes a different average spiking rate, the responses to repetitions of the same stimulus are surprisingly variable. A further complication is that responses scale with contrast – larger responses are seen at higher contrasts.
This means that given the spiking responses of just one neuron, it is impossible to determine what stimulus was presented. For example, a spiking rate of 30 spikes in a second might be evoked by a fast, high contrast stimulus moving upwards, or a slow, low contrast stimulus moving to the right. The brain therefore relies on populations of neurons to reliably encode stimulus features.
In this simulation, you will quantify how the responses of a typical neuron in MT depend on motion direction, contrast, and some other unrevealed factors. This can be used to assess how much information a neuron conveys about the stimulus, and the reliability of that information. We can then infer how populations of neurons work together to accurately encode a diverse range of stimuli.
Instructions and experimental setup
In this simulation you will "record" from a neuron in area MT. Please enter your name in the following box and when you're ready press "Initialise neuron".
This will generate a neuron with unique tuning properties. If you return to this page, enter the same name, and you will be able to "record" from the same neuron.
In the Tuning curves tab, the left panel represents the visual stimulus that the neuron is responding to, in this case a moving grating. You can control the direction and contrast of this grating; when you press "Start", the grating on the stimulus screen will move in the direction you selected for one second.
The oscilloscope on the top right shows the amplified voltage trace recorded extracellularly from an electrode close to the cell body of the neuron. Each of the near-vertical lines represents an action potential. If you zoomed in on one of these action potentials, what would it look like? What is the difference between an intracellular and an extracellular recording?
If your speakers are on, you will also be able to hear the neuron's activity. Below the oscilloscope is a graph that automatically plots the number of spikes fired by the neuron on each trial. The data for each contrast will be coloured differently, according to the legend on the right. By clicking on a contrast legend, you can hide/show the data that you have collected for that contrast.
To download the data for further analysis, use the Download tab.
Tuning curves experiment
Please initialise your neuron in the Instructions tab before proceeding with this experiment
Now that you've understood the procedure for mapping the tuning curve of a direction sensitive neuron, choose the type of data you want to download, the details of the experimental paradigm (such as which directions and contrasts you will be showing), and how many repetitions you want to perform for each parameter combination. Then download the data to your computer for further analysis.