Plans for a Recall Referee

  • John Robert Burger
Part of the Springer Series in Cognitive and Neural Systems book series (SSCNS, volume 6)


A recall referee is essential to survival since it places the most important of a group of returns into consciousness. The version presented below accepts a group of returns from long-term memory, where each returned image goes into its own register of simulated qubits for processing. Multiple match resolution is accomplished by calculating and then choosing the highest priority for entry into conscious short-term memory. How recursive neurons can digitally compute priority for each returned memory image, and then digitally select the highest priority, is shown.

To compute a priority, selected attributes in each returned image are weighted for importance by giving them a small binary number. Attributes relating to danger would have a higher weight. Once encoded, they are all processed in parallel using a sequence code stored in long-term memory. Code details are provided in a later chapter.

Toggling occurs in selected objective elements (simulated qubits) if the values of selected controlling elements (simulated qubits) are all true. A given sequence of such operations is made to sum the individual importance weights, giving a numerical priority for each return. These are then compared.

The priority comparator of this chapter uses standard magnitude comparators based on XOR gates. A later chapter will show a method that uses controlled toggle processing to select the highest priority. A multiplexer (MUX) subsequently enables the highest priority image into conscious short-term memory.

The neural logic of this chapter is chiefly asynchronous. Presented neural circuits include a pulse selector, a load command generator, an attribute encoder, a toggle pulse generator, a controlled toggle computer, a controlled toggle sequencer, a priority magnitude comparator (using neural logic), and a MUX to forward the highest priority image.


Single Pulse Timing Diagram Magnitude Comparator Multiple Return Priority Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • John Robert Burger
    • 1
  1. 1.VenetaUSA

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