|Property name||Property description|
|Adaptation index||Rate at which firing speeds up or slows down during a stimulus|
|Average firing rate (spikes/s)||Average firing rate across the entire stimulus interval|
|Average ISI (ms)||Average interspike interval duration|
|F-I curve slope (spikes/s/pA)||Slope of linear fit to the frequency response of the cell versus stimulus intensity curve|
|Input resistance (MΩ)||Resistance of cell membrane as measured by a linear fit to responses to hyperpolarizing current steps|
|Membrane time constant (ms)||Time constant of exponential fit to responses to hyperpolarizing current steps|
|Ramp spike time (s)||Time to first spike evoked by a slow current ramp|
|Resting potential (mV)||Average of the pre-stimulus membrane potential|
|Rheobase (pA)||Minimum current amplitude of one-second-long steps that evoked an action potential|
|Upstroke:Downstroke||Ratio between the peak upstroke (rate of rise of the action potential) to peak downstroke (rate of fall of the action potential)|
|Average contraction||Average ratio between Euclidean distance of a branch and its path length|
|Maximum euclidean distance (μm)||Maximum Euclidean distance from the soma to all nodes|
|Normalized cortical depth||Depth of the cell soma normalize between pia (0) and white matter (1)|
|Bifurcations||Number of points where a process splits into two daughter processes|
|Soma stems||Number of processes attached to the soma|
|Parent:Daughter||Average ratio between the diameter of a daughter branch and its parent branch|
Cell Feature Filters
While many distinct electrophysiological features were recorded and/or calculated from each cell, only a few features were called out in the web application. To see the other features, please read the Electrophysiology Overview whitepaper in the "Documentation" tab and/or download the data from the Allen Brain Atlas API. These features were selected not only by what they were able to tell us about the intrinsic properties of the cells, but also in their ability to differentiate between putative cell types in a non-biased clustering analysis.
The ratio between the absolute values of the action potential peak upstroke and the action potential peak downstroke. Action potential peak upstroke: The maximum rate of change between the action potential threshold and the action potential peak. Action potential peak downstroke: The minimum rate of change between the action potential peak and the action potential trough.
This value gives us an indication of the amount of time a cell takes to recover after an Action Potential. An example of the difference between a high and a low ratio is illustrated here.
This parameter is used to distinguish between spiny (putatively excitatory) and aspiny (putatively inhibitory) groups of cells in a non-biased clustering analysis.
Not only are isolated action potentials of interest, but the rates of firing are likely to be important in inter-neuron communication. The adaptation index is the rate at which action potential firing changes during a stimulus. This index is used to characterize a neuron's response to a sustained input, a one second square pulse in this case.
A high adaptation index means the neuron spikes at the beginning of the stimulus, but then the spiking slows or stops altogether. These neurons therefore likely transmit the initiation of an upstream input. Conversely, a neuron that does not accommodate (has a low adaptation index) gives a consistent output during the sustained input and might also be used to tell when a stimulus ends. The adaptation index likely involves ion channel proteins that change properties on the time scale of hundreds of milliseconds to seconds.
This value is the amount of stimulus current (long square current in pA) required to initiate a single action potential. This parameter gives us an indication of the excitability and the membrane resistance of the cell.
Membrane Time Constant
The membrane time constant, also referred to as tau, reflects the time it takes for a neuron to charge its membrane. This is one of the passive or subthreshold properties of the cell and is the product of the input resistance and the cell capacitance. The membrane time constant in this study was estimated by fitting voltage responses to negative current injection with an exponential.
The firing rate is simply the speed at which a neuron fires multiple action potentials in sequence given a sustained current injection. A neuron that fires at a faster rate could influence a downstream neuron more strongly than a neuron firing with a slower rate. The rate that a neuron fires given a sustained input is determined by the ion channel proteins it expresses and whether and how fast those ion channel proteins recover from the previous action potential. In general, a higher firing rate is seen in neurons with individual action potentials that are more narrow and have a deeper after hyperpolarization. This is illustrated in the parallel coordinate plot where cells with a greater Upstroke/Downstroke ratio show a lower firing rate.
The input resistance is the baseline resistance of the neuron membrane, and like tau is one of the passive or subthreshold properties of the cell. A cell with high input resistance leads to a large voltage response (V=IR) and is referred to as "tight" as current doesn't easily leak out. Conversely, "leaky" neurons are those with low input resistance. The input resistance was calculated by measuring the slope of the V/I plot at subthreshold potentials.
A subset of cells with electrophysiological recordings was manually selected for 3-D reconstruction. Similar to the electrophysiology features, many morphology features were calculated in the reconstruction, but only three are called out in the web application; Normalized Cortical Depth, Max Distance and # Stems. To see the entire array of morphological features, please read the Morphology Overview whitepaper in "Documentation" or download the data from the Allen Brain Atlas API.
The three features represented in the web application were the top three most discriminative features (ranked by a classic machine learning technique) that best separates major morphological categories of neurons currently included in the database.
Normalized Cortical Depth
As the thickness of the visual cortex changes naturally and can also be affected by non-biological factors like tissue shrinkage during histology, we compute the relative depth of the soma with respect to the pia and white matter using the cortical coordinate space in the CCF (see whitepaper in Documentation).
This is the maximum
Number of Stems
|number of stems|
Filtering of the dataset results in a list of experiments ranked (by default) by Upstroke:Downstroke. You can alter the sort parameters from the Filters menu.