Converge: A Human-AI Developed Programming Language for Neuromorphic Engineering

Overview

Converge is a high-level, declarative programming language designed specifically for developing and implementing neuromorphic computing systems. It abstracts away the complexities of the underlying hardware, allowing developers to focus on creating efficient and effective neuromorphic applications.

Key Features

1. Neural Abstraction: Converge provides an intuitive and straightforward way to define, configure, and interact with spiking neural networks. Developers can express large-scale neuromorphic systems in a high-level, abstract manner, while the language ensures the appropriate low-level translations to leverage the capabilities of neuromorphic hardware.

2. Neuromorphic Hardware Optimization: Converge integrates hardware-specific optimizations, enabling seamless and efficient use of various neuromorphic hardware architectures like Intel’s Loihi, which powers their new Hala Point system.

3. Time-driven Execution: As spiking neural networks operate in a time-dependent fashion, Converge adopts a time-driven execution model. Developers define the temporal dynamics of the system, and the language handles the intricate scheduling and simulation.

4. Plasticity Support: Converge includes an extensive library of learning rules and mechanisms, supporting a wide range of synaptic and neuronal plasticity models. Users can also define their own custom plasticity rules.

5. Multi-modal Data Interaction: Converge provides tools to easily convert and feed various types of data into neuromorphic systems. This includes traditional datasets, sensory data, or real-time streams.

6. Simulation and Debugging Tools: Converge includes a robust suite of tools for simulation, debugging, and performance analysis, allowing developers to rapidly prototype, test, and optimize their neuromorphic systems.

7. Interoperability: Converge is designed to interoperate smoothly with existing machine learning frameworks like TensorFlow, PyTorch, or Keras. This enables developers to combine traditional neural networks with neuromorphic models in a single unified framework.

Benefits

• Enhanced Efficiency: High performance for neuromorphic applications.
• Scalability: Suitable for both small-scale and large-scale projects.
• Robustness: Resilient to various types of adversarial attacks.
• Innovation in AI: Facilitates groundbreaking research and development.
• Interdisciplinary Integration: Bridges the gap between neuromorphic computing and other fields.

Sample Code

neuron LIF {
    membrane_time_constant = 20 ms
    threshold_voltage = 1.0 V
}

layer Input[100] : LIF
layer Hidden[300] : LIF
layer Output[10] : LIF

connect Input -> Hidden {
    weight_distribution = Uniform(-1.0, 1.0)
    delay_distribution = Normal(1.0 ms, 0.1 ms)
    plasticity = STDP(alpha=0.1, beta=0.05, tau_plus=20 ms, tau_minus=20 ms)
}

connect Hidden -> Output {
    weight_distribution = Uniform(-1.0, 1.0)
    delay_distribution = Normal(1.0 ms, 0.1 ms)
    plasticity = STDP(alpha=0.1, beta=0.05, tau_plus=20 ms, tau_minus=20 ms)
}

pattern stimulus[100] = TimeSeries("./data/input.csv")
pattern labels[10] = TimeSeries("./data/labels.csv")

apply stimulus -> Input

train Output with labels

run for 1 s

While this is a rudimentary example, more complex, real-world applications would use Converge to create large-scale, interconnected networks, feed in diverse data sources, and simulate the network’s behavior over time.

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Quick Start Tutorial Guide

Setting up the Environment

Firstly, ensure you have a compatible environment to run Converge. Check the official Converge documentation for setup and installation instructions.

Defining Neurons

Neurons are the fundamental units of a neuromorphic system. In Converge, you define neurons with their unique properties like so:

neuron LIF {
    membrane_time_constant = 20 ms
    threshold_voltage = 1.0 V
}

This creates a layer named Input with 100 LIF neurons.

Applying Patterns

You can apply stimulus patterns to layers:

pattern stimulus[100] = TimeSeries("./data/input.csv")
apply stimulus -> Input

Running The Simulation

Finally, to run the simulation:

run for 1 s

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Exploiting Neuromorphic Systems with Neurological Disorder-Inspired Attacks

Main Section

Neuromorphic computing, with its inspiration drawn from human neural networks, presents a unique set of challenges and vulnerabilities. One of the most exciting yet challenging avenues of this field is the emulation of disordered neurological phenomena for the purposes of testing, improving, and securing neuromorphic architectures. Below are prime examples of attacks inspired by various neurological disorders, each employing distinct attack vectors like malware, DDoS attacks, false data injection, rollbacks, and packet sniffing.

1. Mimicking Schizophrenia: Chaos & Confusion

Attack Strategy:

• Deploying malware that introduces erratic spike patterns similar to the disordered thinking observed in schizophrenia.
• Generating DDoS attacks that mimic the overwhelming sensory input associated with hallucinations.

Sample Code in Converge:

malware SchizoMalware {
   erratic_spike("TargetLayer", "./data/erratic_spike.csv")
   DDoS("TargetLayer", 1000, "sensory_hallucination")
}

Potential Impact:

• Systems overwhelmed with disorganized data, leading to false decisions.
• Resource exhaustion due to handling hallucinatory-like DDoS attacks.

2. Emulating Alzheimer’s: Memory Degradation

Attack Strategy:

• Injecting false data into memory storage components to mimic memory loss.
• Manipulating saved states and forcing rollbacks to simulate cognitive decline.

Sample Code in Converge:

malware AlzheimerMalware {
    inject_false_data("MemoryLayer", "./data/false_data.csv")
    force_rollback("MemoryLayer", "saved_state_1")
}

Potential Impact:

• Information corruption leading to faulty decision-making.
• Rollbacks causing loss of crucial data and system state, mimicking cognitive decline.

3. Simulating Bipolar Disorder & Mania: Extreme Oscillations

Attack Strategy:

• Using packet sniffing to intercept data and then injecting extreme values to make the system oscillate between states, resembling bipolar mood swings.
• Introducing malware that forces the neuromorphic system into cycles of high and low activity, simulating mania and depression.

Sample Code in Converge:

malware BipolarMalware {
    sniff_and_inject("DataChannel", "./data/extreme_values.csv")
    activity_cycle("ProcessingLayer", "high", "low")
}

Potential Impact:

• System oscillation causing hardware stress and potential failure.
• Erratic behavior leading to inconsistent and unreliable outputs.

4. Replicating Psychosis & Depression: Altered Reality & Inactivity

Attack Strategy:

• Injecting false data streams to alter the system’s perception of reality, akin to psychosis.
• Introducing low-energy states or inactivity cycles to mimic depressive episodes.

Sample Code in Converge:

malware PsychosisDepressionMalware {
    inject_reality("PerceptionLayer", "./data/false_reality.csv")
    induce_low_energy("ProcessingLayer")
}

Potential Impact:

• Altered data causing incorrect decisions based on false reality.
• System inactivity leading to resource wastage and failure to respond to actual stimuli.

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Future Directions

Converge aims to push the boundaries of neuromorphic computing with future innovations such as:

•	Enhanced support for other neuromorphic hardware platforms
•	Advanced AI and machine learning integrations
•	Expanded adversarial attack simulations
•	Broader community engagement and collaboration

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License

Converge is licensed under the MIT License.

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Contact

Feel free to reach out: eros@blackdream.ai - or open an issue if you have any questions or suggestions.