Biological Basis of the Code

The code provided is intended to model synaptic plasticity in a multi-layered neural network, specifically focusing on a mechanism known as Spike-Timing-Dependent Plasticity (STDP). Below, I describe the biological concepts and processes that this code is modeling.

Spike-Timing-Dependent Plasticity (STDP)

STDP is a biological process that adjusts the strength of connections (synaptic weights) between neurons based on the relative timing of their spikes (action potentials). It is a form of Hebbian learning, which hypothesizes that synaptic efficacy increases if a presynaptic neuron's firing is consistently followed by postsynaptic firing, and decreases if the order is reversed.

1. Long-Term Potentiation (LTP) and Depression (LTD):

   • LTP occurs when the presynaptic neuron fires before the postsynaptic neuron within a short time window. This is modeled by Layers(i).LTRates.LTP, affecting the rate and magnitude of weight increases.
   • LTD occurs when the postsynaptic neuron fires before the presynaptic neuron. This is captured by Layers(i).LTRates.LTD, influencing the rate and magnitude of weight decreases.

2. Timing and Synaptic Modification:

   • The code uses latency information (InitialInputs) and processes it through each layer. The timing of spikes (latencies) is crucial for determining the changes in synaptic weights, mimicking biological STDP.

Neural Circuit Parameters

The code accounts for various dynamic factors within a hypothetical neural circuit:

• Neuronal Layer Structure:

  • The model consists of multiple layers of neurons (Layers(i).NNeurones), similar to layers found in biological neural networks, like the cerebral cortex.
  • Each neuron within these layers has synaptic weights (Layers(i).Weight), which are subject to change via the STDP process.

• Inhibition Strategies:

  • Lateral inhibition (Layers(i).InhibStrategy.Type) is modeled, reflecting biological inhibitory processes that help sharpen selectivity and refine responses within neural circuits.

• Thresholds:

  • Each layer or neuron has a firing threshold (Layers(i).Threshold), which determines the minimum input required to generate an output spike, analogous to the membrane potential threshold for action potentials in real neurons.

Diagnostic and Plotting

• Diagnostics: The code can store diagnostic data (SaveDiagnostic), which could relate to biological experimentation where neural activity and synaptic changes are recorded over time for analysis.

• Plotting: The optional plotting (Plot) aids in visualizing weight changes and spike latencies, similar to data visualization in experimental neuroscience.

Binocular Processing

The term "binocularLayer" suggests a focus on processing inputs from two different origins, akin to the integration of visual signals from both eyes in the visual cortex.

Conclusion

In summary, this code models STDP, a fundamental learning mechanism in the brain, using synthetic neural networks structured in layers. It simulates the biological complexities of neural systems, including timing-dependent synaptic plasticity, lateral inhibition, and multi-layer processing, offering insights into how biological neural circuits might compute and adapt over time.