Deuron8-Go

Deuron 8 written in Go

Simulation

The simulation makes two "passes" through the network for each time step. Each step equals 10? microseconds.

Each simulation step works on the previous input. Each component maintains an internal current-state and next-state.

The first pass exercises the inputs on each neuron.

The second pass moves the results of the first pass to the neuron's output. Note: the output of one neuron is the input of another, except for stimulus.

Folders

• Deuron8-Go
  • api: the main api for the network approach
  • config:
  • examples:
  • log:
  • neuron_simulation: see folder readme.md
  • simulation:
  • tests:
  • tools:

Tasks

Synaptic properties

At the start of a simulation the model is configured according to the initializer choice. The model is then copied to the runtime and the simulation is started. Once the simulation is complete the runtime is copied back to the model. The model can then be saved to synapses.json

Types of initializations possible:

• Preset: the synaptic properties are reset back to a set of Presets values from a fixed synapsed_presets_N.json.
• Current: the synaptic properties are not changed but continue forward. Any changes made to the model are used in the next simulation.
• Random: the synaptic properties are randomly changed prior to simulation. The only property currently supported is Weight.

Applications

Generators

A generator program will create json files for synapses. It reads a configuration json file to drive the generator. The generated file can be used by the simulator as a preset.

Neuron simulator

This application simulates a single neuron. It is located in the neuron_simulation/app folder Executed as:

go run .

Notes:

Learning

The goal is to try and learn a pattern out of a Set. The Set size is 2 ot 3 patterns.

• Synapse #2 properties may be a good representative for other synapses
• Add two or more stimulus patterns

Short term plasticity (STP)

STD/STF ... Articles:

• x

Memory retention

• Add memory retention functionality. See https://www.desmos.com/calculator/aj3e3r7ajf for an equation.

Retension can last for 10s ms to days or even permanent. Memory can be at the Soma or Synapse

Misc

The weights are typically the short term memory until learning Effort is increased. The other parameters, for example Tao, are controlled by a Meta system (EA for example). They are the slower changing parms that represent the charateristics of the neuron but also represent information at a meta level.

We have two json files: A Preset that can't be saved over and a version that is overlayed at the end of a simulation.

add random property generators, for example, randomly generate weights centered around a fixed value with a mean variance. Zero is a typical center value. maybe have a listbox that you choose a property then select a max variance and center value. There could be presets.

Each time the sim starts we check if we use the fixed defaults, the current weights or random values.