file names, logs and figures
A neuronOne of thousands of tiny dials inside each layer. It reads the text so far and produces a number, its activation, that nudges what the model writes next. in layerOne step in the stack the text passes through. Qwen3-0.6B has 28 layers, each with 3,072 neurons. 9 of Qwen3-0.6B. Its strongest snippets are mixed: they peak on different tokens. It is featuredA neuron picked for the full treatment: steering outputs and a written explanation. About 3,000 of the 86,000 neurons are featured., so it has steering outputs.
What this neuron does
This neuron responds to technical words about files and data, such as file name, log file, figure, and GeoTIFF files, especially in instructions about how to name or handle them. Turning it up gave text about numbers and tables, but the link is loose.
Written by Claude from this neuron's activations and steering outputs. Confidence: medium. Think it is wrong? Flag it (opens a prefilled issue on GitHub).
Fires most on
The text windows where this neuron's activationThe number a neuron produces for a token. Large positive means it fired hard; negative means it pushed the other way. was highest. Amber marks positive activation; the outlined tokenA piece of text the model reads at once: often a word, sometimes part of a word or a punctuation mark. is the peak. Hover a token for its strength.
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extension to be appended to the rectified images.⏎The next prompt will ask you whether a camera angle map should be produced and if yes, what should be its name.⏎After that you are asked if overwriting existing maps in the target location and
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using the same data directory and they have logging enabled, you must use the appropriate options to specify log file names that are unique to each server. Otherwise, the servers try to log to
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which should be peer-reviewed articles.⏎Write a 7–8-page paper in Word format. Apply APA standards to citation of sources. Use the following file naming convention: LastnameFirstInitial_M5_A1.doc.⏎By Saturday, August
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you will use over and over it is more convenient to create a script that can run all the commands with one message from you.⏎% filename simple.m⏎ini=1.; %set the variable ini to 1.⏎y0=ini;
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is one area of stem cell biology where human and mouse differ significantly and we never would have discovered this if we had limited our studies to using only mouse embryonic stem cells. Adult human stem cells just didn’t work for this.”⏎The research team
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,y0)%call the routine ode45⏎plot(t,y) %plot the solution⏎The results give a table of values for t and y and a figure.⏎Next consider the following two ordinary differential equations, which are a model of turbulence
SteeringAdding a push along a neuron while the model writes, to see how its output changes. Turning it up or down.
Push this neuron up or down while the model writes, and watch what changes. The outputs were generated ahead of time, so nothing runs in your browser.
Here is something I have been thinking about lately: How do I know if the expression (a^2) * (b^2) is true? I think that I know. To be a complex function, I need to know the value of the function. So, if I have
Most negative on
Where the activation went furthest below zero. Blue marks negative activation. Many neurons mean something different in each direction.
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illustrated using a single, simple differential equation:⏎Integrate this equation from t = 0 to t = 1. The exact solution can be found by quadrature and is⏎To use MATLAB we first construct an M-file that defines the equation
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. You have a set of candidate solutions (called a population). The best solutions get combined with each other (mating) and also some of those get randomly mutated. This lets the algorithm both use good solutions, but also explore new options.
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LocalSeed. This means that the same random numbers are drawn for the blocks. However, the random numbers are differently transformed according to the selected distributions (uniform and truncated normal distribution), and therefore the blocks have different output values. Simulation results are shown
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theory.⏎Genetic algorithms and neural networks⏎Genetic algorithms used to be a popular method for training neural networks. For example, one
Connections
Show on mapWhich neuronsOne of thousands of tiny dials inside each layer. It reads the text so far and produces a number, its activation, that nudges what the model writes next. in earlier layersOne step in the stack the text passes through. Qwen3-0.6B has 28 layers, each with 3,072 neurons. feed this one, and which later ones it feeds. Amber links excite, blue links suppress; longer bars are stronger.
Fed by earlier layers
- L8:2350 weakly suppresses
- L5:319 weakly excites
- L8:360 weakly suppresses
- L7:2033 weakly suppresses
- L1:2343 weakly suppresses
- L5:76 weakly suppresses
- L6:2658 weakly suppresses
- L0:794 weakly excites
- L7:2417 weakly excites
- L8:2969 weakly suppresses
- L6:2728 weakly excites
- L3:2286 weakly suppresses
- L2:2534 weakly suppresses
- L6:1996 weakly suppresses
- L7:399 weakly excites laboratory and research facility terms
- L7:2600 weakly suppresses
- L1:1818 weakly suppresses
- L2:302 weakly excites
- L6:1840 weakly suppresses
- L7:829 weakly suppresses
- L7:1286 weakly excites
- L8:5 weakly suppresses
- L8:28 weakly excites
- L6:753 weakly suppresses
Feeds later layers
- L11:2676 excites
- L21:1599 excites
- L10:1001 excites
- L12:1708 excites
- L22:1132 excites
- L10:1106 weakly excites
- L10:2369 weakly excites
- L13:3039 weakly excites
- L11:1768 weakly suppresses
- L11:1668 weakly excites
- L16:2140 weakly excites
- L16:3025 weakly excites
- L11:2768 weakly excites
- L13:1624 weakly excites
- L21:1080 weakly excites
- L17:1702 weakly excites
- L12:1802 weakly suppresses
- L15:2767 weakly excites
- L13:2176 weakly suppresses
- L11:1133 weakly excites
- L11:527 weakly excites
- L12:2809 weakly suppresses
- L10:1249 weakly excites
- L11:549 weakly suppresses
Activation histogramA chart of how often a neuron takes each activation value.
How often this neuron fires at each strength. Almost always it does close to nothing (the tall bars in the middle); the snippets above are the rare moments it fired hard, amber for positive and blue for negative.
Logit lensReading which output tokens a neuron pushes toward or away from, straight off its output weights, before the later layers have their say.
The words this neuron nudges the model toward (amber) or away from (blue) when it fires. A hint, not a label: many neurons work through later layersOne step in the stack the text passes through. Qwen3-0.6B has 28 layers, each with 3,072 neurons. instead.
Promotes
- udies
- ournal
- papers
- Files
- ashes
- /history
- udios
- .setTitle
- ampp
- ulg
Suppresses
- znal
- 动感
- irty
- 这两种
- coercion
- enforcement
- 有效性
- virt
- 机动
- →Token