Advanced Neural Network Simulation

A state-of-the-art neural network simulation incorporating modern neuroscience discoveries, including dendritic computation, synaptic plasticity, neuromodulation, and homeostatic mechanisms.

Table of Contents

• Overview
• New Features
• Requirements
• Installation
• Usage
• Configuration
• Technical Details
• Output Files
• Data Analysis
• Contributing
• License

Overview

This advanced simulation implements a biologically-detailed neural network model that incorporates recent discoveries in neuroscience. The model features:

• Realistic dendritic computation
• Multiple forms of synaptic plasticity
• Neuromodulatory systems
• Homeostatic scaling
• Burst detection and processing
• Correlated neural noise
• Advanced calcium dynamics

New Features

1. Dendritic Computation

• Multiple dendritic segments per neuron
• Local NMDA spikes
• Nonlinear dendritic integration
• Compartmentalized synaptic plasticity
• Branch-specific calcium dynamics

2. Advanced Synaptic Plasticity

• Spike-Timing-Dependent Plasticity (STDP)
• Metaplasticity mechanisms
• Homeostatic scaling
• Activity-dependent modification
• Synaptic trace mechanisms

3. Neuromodulation

typedef struct {
    double dopamine;
    double serotonin;
    double noradrenaline;
    double acetylcholine;
} Neuromodulators;

• Implementation of major neuromodulatory systems
• Burst-dependent modulation
• Plasticity modification
• Behavioral state simulation

4. Homeostatic Mechanisms

typedef struct {
    double threshold_baseline;
    double adaptation_rate;
    double target_activity;
    double current_activity;
} HomeostaticScaling;

• Target activity maintenance
• Adaptive threshold adjustment
• Synaptic scaling
• Metaplasticity regulation

5. Burst Detection

• Real-time burst analysis
• Burst-triggered plasticity
• Neuromodulator release
• Pattern detection capabilities

Requirements

System Requirements

• GCC compiler (version 8.0 or higher)
• Make build system
• Minimum 16GB RAM (32GB recommended)
• Multi-core processor recommended
• POSIX-compliant operating system

Dependencies

• C Standard Library
• Math Library (libm)
• Standard C Time Library
• OpenMP (optional, for parallelization)
• GSL (GNU Scientific Library) for advanced mathematical functions

Installation

1. Clone the repository:

git clone https://github.com/yourusername/advanced-neural-simulation.git
cd advanced-neural-simulation

2. Configure build options:

./configure --enable-openmp --with-gsl

3. Compile the source code:

make

4. Run tests:

make test

Usage

Basic Usage

./neural_sim [options]

Command Line Arguments

Options:
  -c, --config <file>      Specify configuration file
  -o, --output <dir>       Specify output directory
  -t, --time <seconds>     Simulation duration
  -s, --seed <number>      Random seed
  -n, --neurons <number>   Number of neurons
  -d, --dendrites <number> Dendrites per neuron
  -p, --plasticity <type>  Plasticity rule (stdp/hebbian/homeostatic)
  -m, --modulation <bool>  Enable neuromodulation
  -b, --burst <bool>       Enable burst detection
  -v, --verbose           Enable verbose output
  --gpu                    Use GPU acceleration (if available)

Example Configurations

1. Basic Simulation:

./neural_sim --config basic.conf --time 10.0

2. Advanced Features:

./neural_sim --config advanced.conf --dendrites 20 --plasticity stdp --modulation true

3. Large-Scale Simulation:

./neural_sim --neurons 1000 --dendrites 50 --gpu --output large_sim/

Configuration

Advanced Network Parameters

[Network]
num_pyramidal = 1000
num_inhibitory = 200
dt = 0.00001
connection_rate = 0.1

[Dendrites]
num_dendrites = 20
num_synapses_per_dendrite = 100
dendrite_coupling = 0.5
nmda_threshold = 0.8

[Plasticity]
stdp_window = 0.020
stdp_rate = 0.01
meta_plasticity_rate = 0.001
homeostatic_tau = 1000.0

[Neuromodulation]
dopamine_baseline = 1.0
serotonin_baseline = 1.0
modulator_decay = 0.100
burst_threshold = -50.0

[Homeostasis]
target_rate = 10.0
adaptation_rate = 0.01
scaling_tau = 1000.0

Technical Details

Dendritic Computation Model

The simulation implements a multi-compartmental dendritic model:

1. Local Potential Evolution:

dV_dend/dt = (-g_leak*(V_dend-E_leak) + I_syn + I_NMDA + I_coupling)/C_dend

2. NMDA Spike Generation:

I_NMDA = g_NMDA * s_NMDA * (V_dend-E_NMDA) * Mg_block(V_dend)

3. Dendritic Integration:

V_soma = sum(w_i * V_dend_i) + V_baseline

Advanced Plasticity Rules

1. STDP with Metaplasticity:

dw/dt = A+ * exp(-Δt/τ+) * M(w)  for Δt > 0
dw/dt = -A- * exp(Δt/τ-) * M(w)  for Δt < 0

2. Homeostatic Scaling:

dθ/dt = (r_current - r_target)/τ_homeo

Neuromodulation Effects

The impact of neuromodulators on synaptic plasticity:

Δw_effective = Δw_base * (1 + α_DA * [DA] + α_ACh * [ACh])

Output Files

The simulation generates several detailed output files:

1. neuron_activity.txt:

   time neuron_id voltage calcium burst_state modulator_levels
   

2. synaptic_weights.txt:

   time pre_id post_id dendrite_id weight meta_plasticity
   

3. population_statistics.txt:

   time mean_rate burst_frequency average_weight modulator_levels
   

Data Analysis

Example Python script for analyzing burst patterns:

import numpy as np
import matplotlib.pyplot as plt
from scipy import stats

def analyze_bursts(filename):
    # Load data
    data = np.loadtxt(filename)
    time = data[:, 0]
    voltage = data[:, 1]
    
    # Detect bursts
    burst_threshold = -50
    burst_indices = np.where(voltage > burst_threshold)[0]
    
    # Analyze burst patterns
    burst_intervals = np.diff(time[burst_indices])
    
    # Plot results
    plt.figure(figsize=(12, 6))
    plt.hist(burst_intervals, bins=50, density=True)
    plt.xlabel('Inter-burst Interval (ms)')
    plt.ylabel('Probability Density')
    plt.title('Burst Interval Distribution')
    plt.show()
    
    return burst_intervals

# Usage
intervals = analyze_bursts('neuron_activity.txt')
print(f"Mean burst interval: {np.mean(intervals):.2f} ms")

Contributing

We welcome contributions! Areas of particular interest:

1. New Features

   • Additional neuron types
   • More complex dendrite morphologies
   • Glial cell interactions
   • Extracellular field effects
   • Detailed ion channel dynamics

2. Performance Improvements

   • GPU acceleration
   • Parallel processing
   • Memory optimization
   • Numerical method improvements

3. Analysis Tools

   • Real-time visualization
   • Advanced analytics
   • Machine learning integration
   • Pattern detection algorithms

Development Process

1. Fork the repository
2. Create a feature branch
3. Implement your changes
4. Add tests and documentation
5. Submit a pull request

Coding Standards

• Follow C99 standard
• Use consistent indentation (4 spaces)
• Document all functions and complex code sections
• Include unit tests for new features
• Follow neuroscientific naming conventions

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use this software in your research, please cite:

@software{advanced_neural_sim_2024,
  title = {Advanced Neural Network Simulation},
  author = {Author, A.},
  year = {2024},
  url = {https://github.com/yourusername/advanced-neural-simulation},
  version = {2.0.0}
}

---

For more information, bug reports, or feature requests, please:

• Open an issue in the GitHub repository
• Contact the maintainers
• Check our documentation