Note

This documentation is for a development version. Click here for the latest stable release (v1.3.0).

Examples

See the following examples for demonstrations of how nengo_spa works.

Note

In most of the examples, we set seeds on the network and vocabulary objects. This is done for consistency; all the examples should work across a wide variety of seeds, but occasionally a “bad seed” will cause the results to be different from what we describe. We set the seeds to avoid this problem. In practice, such adverse results can typically be avoided by using more dimensions per semantic pointer (at least 64). We often use smaller numbers of dimensions in the examples so that they run quickly, but this makes bad seeds somewhat more common.

• Convolution network
  • Create and run the model
  • Analyze the results
• Associative Memory
  • Step 1: Creating an Associative Memory
  • Step 2: Clean-up Memory
    • Thresholding Memory
  • WTA Memory
  • Step 3: Heteroassociative relationships
• Learning
  • Method 1: Represent the Semantic Pointers in single, large ensembles
    • Considerations
  • Method 2: Create multiple, pairwise connections between the state ensembles
    • Considerations
• Simple question answering
  • Step 1: Define the vocabulary dimension and network
  • Step 2: Define the input
  • Step 3: Create the model
  • Step 4: Probe the output
  • Step 4: Run the model
  • Step 5: Plot the results
• Controlled question answering
  • Create the model
  • Probe the output
  • Run the model
  • Plot the results
• Simple question answering with memory
  • Define the input functions
  • Create the model
  • Probe the output
  • Run the model
  • Plot the results
• Routing through a sequence
  • Step 1: Create the model
  • Step 3: Probe the output
  • Step 4: Run the model
  • Step 5: Plot the results
• Routed sequencing
  • Step 1: Create the model
  • Step 3: Probe the output
  • Step 4: Run the model
  • Step 5: Plot the results
• Parsing simple commands
  • Step 1: Create the model
  • Step 2: Probe the output
  • Step 3: Run the model
  • Step 4: Plot the results
• Vocabulary casting
  • Reinterpretation
  • Translation
  • Further options