Biological Basis of the Computational Model

The provided code represents a computational model aimed at simulating neural network dynamics, with a focus on synaptic connectivity and network structure. Here are the key biological aspects being modeled:

1. Neuron and Synapse Characteristics

• Synaptic Weights (data.I, data.J, data.G):
  • These arrays are initialized using a log-normal distribution, which suggests an attempt to capture biological variability in synaptic weights. This variability is a well-observed phenomenon in neural systems, where synaptic strength can differ greatly across connections.
  • I and J might represent different types of inputs or synaptic efficacy between neurons, while G could be related to conductance properties of the synapses.

2. Network Structure

• Connectivity Matrices (data.C, data.C_RL):
  • The model includes two main connectivity structures: data.C for general excitatory connections and data.C_RL for local recurrent connections within the network.
  • These matrices are constructed with random permutations and a specified percentage of connectivity (pars.N_perc_conn, pars.N_perc_RL_conn), mimicking the sparse and probabilistic nature of neuronal connections observed in biological networks. Such randomness mirrors real-world neuroanatomy where individual connectivity patterns can appear stochastic within larger organizational constraints.

3. Stochastic Processes

• Randomness (data.rnd):
  • The model employs various randomization processes (randperm, randn) to simulate the inherent stochasticity of biological neural systems. This captures the unpredictability in synapse formation and firing patterns seen in vivo.

4. Active Neurons (data.I_act, data.RL_act)

• Activation:
  • All neurons are set as active (data.I_act, data.RL_act), indicating that each neuron has the potential to participate in network dynamics. This reflects the potential for recruitment of neurons based on afferent input and network demands, a characteristic of neural plasticity.

Conclusion

In conclusion, the code models a biologically inspired neural network emphasizing stochastic synaptic connectivity and inherent variability in synaptic properties. These elements aim to reflect the complex, distributed, and probabilistic nature of neural connectivity and processing in biological systems. By integrating these features, the computational model provides a simplified yet biologically plausible framework for exploring neural dynamics.