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Thinking Chips: Crafting Brain-Inspired AI

Beyond Von Neumann: The Dawn of Neuromorphic AI

The relentless pursuit of more intelligent and efficient artificial intelligence has led developers to peer beyond conventional computing paradigms. We’re at the cusp of a revolutionary shift: Neuromorphic Computing. This field, deeply inspired by the intricate, energy-efficient architecture of the human brain, fundamentally redesigns how computers process information. Unlike traditional Von Neumann architectures, which separate processing and memory, leading to the infamous “bottleneck,” neuromorphic systems integrate these functions, mimicking the brain’s parallel, event-driven, and highly distributed nature.

Photo by Bill Fairs on Unsplash

Neuromorphic computing offers a compelling alternative for tackling challenges where conventional AI struggles: achieving high energy efficiency for complex cognitive tasks, enabling real-time learning at the edge, and processing sparse, event-driven data streams with unprecedented speed. For developers, understanding and engaging with neuromorphic architectures is not just about staying current; it’s about unlocking new frontiers in AI, from ultra-low-power embedded systems to advanced robotics and novel approaches to deep learning. This article serves as your indispensable guide, providing practical insights and actionable steps to navigate this fascinating domain.

Your First Neurons: Kicking Off Neuromorphic Development

Diving into neuromorphic computing doesn’t require immediate access to specialized hardware. The journey often begins with high-level software frameworks and simulators that abstract away the complex underlying physics, allowing developers to design, simulate, and understand spiking neural networks (SNNs). The primary programming language for this domain, like much of AI, is Python, due to its rich ecosystem of scientific libraries.

For beginners, a fantastic entry point is the Nengoframework. Nengo is an open-source tool for building and simulating large-scale brain models. It’s designed to be hardware-agnostic, meaning models built in Nengo can run on various platforms, including neuromorphic hardware like Intel’s Loihi.

Let’s start with a rudimentary example: simulating a single spiking neuron.

Step-by-Step Guidance with Nengo:

1. Environment Setup: Begin by creating a dedicated Python virtual environment to manage dependencies:

   python3 -m venv neuromorphic-env
   source neuromorphic-env/bin/activate # On Windows: .\neuromorphic-env\Scripts\activate
   

2. Install Nengo: Once your environment is active, install the Nengo library:

   pip install nengo nengo-gui
   

   nengo-gui provides a visual interface for simulating and understanding your networks, which is invaluable for beginners.

3. Basic Spiking Neuron Simulation: Create a Python file, e.g., single_neuron.py, and add the following code:

   import nengo
   import numpy as np # Define the neuron type (LIF neuron is common for SNNs)
   # nengo.LIF() represents a Leaky Integrate-and-Fire neuron
   neuron_type = nengo.LIF() # Create a Nengo network model
   with nengo.Network(label="Single Spiking Neuron") as model: # Create an input node, providing a constant stimulus # Here, we feed a constant value of 1 into the neuron stimulus = nengo.Node(1) # Create an ensemble (collection) of one neuron # 'dimensions=1' means it processes a single scalar value # 'neuron_type=neuron_type' specifies the neuron model neuron = nengo.Ensemble(n_neurons=1, dimensions=1, neuron_type=neuron_type) # Connect the stimulus to the neuron # 'synapse=0.005' applies a low-pass filter to the signal, smoothing it nengo.Connection(stimulus, neuron) # Define probes to record data from the simulation # 'spikes=True' records the spiking activity of the neuron # 'output=True' records the neuron's membrane potential (internal state) neuron_spikes = nengo.Probe(neuron.neurons, "spikes") neuron_output = nengo.Probe(neuron.output) # Simulate the network
   with nengo.Simulator(model) as sim: # Run the simulation for 1 second sim.run(1) # Plot the results using nengo_gui's plotting capabilities (or matplotlib) print("Simulation complete. Use nengo_gui to visualize (if installed and run interactively).") # For basic programmatic plotting (requires matplotlib): try: import matplotlib.pyplot as plt # Plot spikes plt.figure(figsize=(12, 6)) plt.subplot(2, 1, 1) plt.plot(sim.trange(), sim.data[neuron_spikes]) plt.title("Neuron Spikes") plt.ylabel("Spike Activity") plt.xlabel("Time (s)") # Plot decoded output (approximate firing rate) plt.subplot(2, 1, 2) plt.plot(sim.trange(), sim.data[neuron_output]) plt.title("Neuron Decoded Output") plt.ylabel("Decoded Value") plt.xlabel("Time (s)") plt.tight_layout() plt.show() except ImportError: print("Matplotlib not found. Install with 'pip install matplotlib' to plot results.") 

4. Run the Simulation: Execute the script:

   python single_neuron.py
   

   If you have matplotlib installed, you’ll see plots illustrating the neuron’s spiking behavior and its decoded output (representing its firing rate). This basic example introduces you to defining a network, adding inputs, specifying neuron types, connecting components, and probing data. It’s a fundamental building block for understanding how information is encoded and processed in SNNs. From here, you can explore adding more neurons, creating inhibitory connections, and implementing basic computations.

Essential Gear for Brain-Inspired Computing

Venturing deeper into neuromorphic development requires familiarity with a specialized toolkit, encompassing frameworks, simulators, and understanding the target hardware. While the field is rapidly evolving, a core set of resources forms the backbone of current development.

Programming Languages and Frameworks

• Python:The undisputed champion for AI and scientific computing, Python remains central to neuromorphic development. Its extensive libraries for numerical computation (NumPy), data manipulation (Pandas), and visualization (Matplotlib) are invaluable.
• Nengo:As introduced, Nengo is a high-level framework that allows developers to design SNNs without worrying about the specifics of the underlying neuromorphic hardware. It supports various neuron models (LIF, Adaptive LIF, etc.) and offers excellent tools for visualization and analysis. It compiles models to run on multiple backends, including Intel’s Loihi.
  • Installation:pip install nengo
  • Usage:Define models using Python, simulate locally, or compile for hardware.
• Intel’s Lava Framework:For those targeting Intel’s Loihi and Loihi 2 neuromorphic research chips, Lava is the direct programming model. It’s a Python-based open-source framework designed for developing brain-inspired applications and mapping them efficiently onto neuromorphic hardware. Lava provides a rich set of libraries and tools for creating SNNs, managing their state, and interfacing with the Loihi chips.
  • Installation:Typically via pip from Intel’s GitHub or PyPI, e.g., pip install lava-dl (for deep learning extensions). Access to Loihi hardware often requires joining Intel’s Neuromorphic Research Community.
  • Usage:Define processes, connect them, and run on Loihi. Lava encourages a modular, process-oriented design, reflecting the distributed nature of neuromorphic computation.
• SpiNNaker (Spiking Neural Network Architecture):Developed by the University of Manchester, SpiNNaker is another large-scale neuromorphic hardware platform. It uses many ARM processors (over a million in its full configuration) to simulate SNNs in real-time. Developers interact with SpiNNaker through various software layers, often using Python-based tools like PyNN or directly through SpiNNaker’s specific API.
  • Installation/Usage:Involves specific setup for accessing the SpiNNaker machine. PyNN is a common interface for defining SNNs independent of the simulator or hardware.

Simulators and Tools

• NEST (Neural Simulation Tool): A widely used simulator for SNNs, particularly for computational neuroscience research. While not directly a neuromorphic hardware tool, NEST is excellent for understanding the dynamics of large-scale SNNs and is written primarily in C++ with a Python interface (PyNEST).
  • Installation:Often compiled from source or installed via package managers. pip install PyNEST for the Python interface.
• Brian2:Another popular SNN simulator, known for its flexibility and user-friendliness, allowing researchers to specify neuron and synapse models at a high level. It’s written in Python.
  • Installation:pip install brian2
• VS Code & Jupyter Notebooks:For code editing and interactive development, Visual Studio Code with its Python extensions (Pylance, Jupyter, etc.) is highly recommended. Jupyter Notebooks are particularly useful for prototyping SNNs, visualizing results, and sharing experiments due to their interactive nature.
  • Extensions:Look for extensions that support .ipynb files, code linting (e.g., Pylint, Black), and integrated debugging.
• Version Control (Git):Essential for any development project, Git ensures proper tracking of code changes, collaboration, and experimentation. Host your repositories on platforms like GitHub or GitLab.

Code Quality and Best Practices

• Modular Design:Break down complex SNNs into smaller, manageable components (e.g., populations of neurons, input/output interfaces, learning rules). This improves readability, testability, and reusability.
• Parameterization:Use clear, descriptive variable names and organize network parameters (e.g., neuron thresholds, synaptic weights, time constants) for easy modification and experimentation.
• Visualization:Leverage tools like nengo-gui, matplotlib, or plotly to visualize neuron activity, membrane potentials, and network connectivity. Understanding “what’s happening” inside an SNN is crucial for debugging and optimization.
• Event-Driven Mindset:Remember that SNNs are fundamentally event-driven. Think about information flow in terms of spikes and timing, rather than continuous values or fixed clock cycles. This paradigm shift is key to effective neuromorphic programming.

From Sensors to Synapses: Practical Neuromorphic Patterns

Neuromorphic computing excels in scenarios where energy efficiency, real-time processing, and local learning are paramount. Its event-driven nature makes it particularly suitable for processing sparse, dynamic data common in sensory applications.

Photo by Hacı Elmas on Unsplash

Code Examples: Spiking Digit Recognition

Let’s expand on our Nengo example to demonstrate a basic spiking neural network for digit recognition, a classic machine learning task. This example will use a pre-trained Nengo model or illustrate the principles of SNNs for classification. Training SNNs from scratch is more complex than ANNs and often involves converting pre-trained ANNs or using specialized learning rules like Spike-Timing-Dependent Plasticity (STDP).

For simplicity, we’ll demonstrate a conceptual framework using Nengo that can be extended for tasks like MNIST digit classification.

import nengo
import numpy as np
import matplotlib.pyplot as plt # --- 1. Define Network Parameters ---
N_INPUT = 784 # For MNIST 28x28 pixels
N_HIDDEN = 100 # Number of neurons in the hidden layer
N_OUTPUT = 10 # For 10 digits (0-9)
SIM_TIME = 1 # seconds # --- 2. Create the Neuromorphic Model ---
with nengo.Network(label="Spiking Digit Classifier") as model: # Input Layer: Represents the pixel data # Here, we'll use a placeholder, in a real scenario this would be a noisy input or pre-processed data input_node = nengo.Node(lambda t: np.random.rand(N_INPUT) if t < 0.1 else np.zeros(N_INPUT), size_out=N_INPUT, label="Input Pixels") # Hidden Layer: A population of LIF neurons hidden_ensemble = nengo.Ensemble(n_neurons=N_HIDDEN, dimensions=1, neuron_type=nengo.LIF(), label="Hidden Layer") # Output Layer: Another population, typically decoding information # For classification, we might decode a representative value for each digit class output_ensemble = nengo.Ensemble(n_neurons=N_OUTPUT 5, dimensions=N_OUTPUT, neuron_type=nengo.LIF(), label="Output Digits") # --- 3. Connections --- # Input to Hidden: A simple connection, weights would be learned or pre-defined # In a real model, this connection would have sophisticated weights, # possibly derived from converted ANNs or STDP learning. nengo.Connection(input_node, hidden_ensemble.neurons, transform=nengo.dists.Uniform(-1e-3, 1e-3)) # Small random weights for illustration # Hidden to Output: Another connection with potentially learned weights nengo.Connection(hidden_ensemble, output_ensemble, transform=nengo.dists.Uniform(-1e-3, 1e-3)) # Small random weights for illustration # Probes for visualization input_probe = nengo.Probe(input_node) hidden_spikes = nengo.Probe(hidden_ensemble.neurons, "spikes") output_decoded = nengo.Probe(output_ensemble, synapse=0.01) # Low-pass filter to smooth spikes into a decoded value # --- 4. Simulate the Network ---
print("Building and simulating network...")
with nengo.Simulator(model) as sim: sim.run(SIM_TIME) # --- 5. Visualize Results ---
plt.figure(figsize=(14, 8)) # Input visualization (simplified)
plt.subplot(3, 1, 1)
plt.plot(sim.trange(), sim.data[input_probe][:, 0]) # Plot first input dimension
plt.title("Input Signal (placeholder)")
plt.ylabel("Value")
plt.xlabel("Time (s)") # Hidden Layer Spikes
plt.subplot(3, 1, 2)
# Only show a subset of neurons for clarity
plt.plot(sim.trange(), sim.data[hidden_spikes][:, :50])
plt.title("Hidden Layer Spikes (first 50 neurons)")
plt.ylabel("Neuron Index")
plt.xlabel("Time (s)") # Output Decoded Value
plt.subplot(3, 1, 3)
plt.plot(sim.trange(), sim.data[output_decoded])
plt.title("Output Layer Decoded Values (Representing Digit Probabilities)")
plt.ylabel("Decoded Value")
plt.xlabel("Time (s)") plt.tight_layout()
plt.show() print("\nThis simulation provides a conceptual framework. Real-world SNN digit recognition involves:")
print("1. Event-based encoding of images (e.g., using Address-Event Representation - AER sensors).")
print("2. Specialized training algorithms for SNNs (e.g., STDP, backpropagation for SNNs).")
print("3. Careful tuning of neuron and synapse parameters for optimal performance and efficiency.")

This example sketches the structure of an SNN classifier. The input nengo.Node would typically receive event-based data from a neuromorphic sensor or pre-processed image features. The nengo.Ensemble represents populations of spiking neurons. The nengo.Connection with a transform carries the synaptic weights, which would be learned. The nengo.Probe allows us to observe the raw spikes and the smoothed, decoded output from the network.

Practical Use Cases

• Edge AI and IoT:Neuromorphic chips’ ultra-low power consumption and real-time processing capabilities make them ideal for always-on sensor analytics (e.g., anomaly detection in industrial equipment, voice activation in smart devices, gesture recognition).
• Robotics:For autonomous robots, neuromorphic systems can process sensory input (vision, touch, hearing) with low latency and adapt to changing environments through on-chip learning, enabling faster reactions and more energy-efficient navigation.
• Real-time Sensor Data Processing:Event cameras (DVS cameras) produce sparse, event-driven data streams that are perfectly aligned with neuromorphic processing. This enables high-speed tracking, motion detection, and gesture recognition without significant computational load.
• Associative Memory and Pattern Recognition:The brain’s ability to recall complete patterns from partial cues can be replicated in neuromorphic systems, leading to robust search engines, recommendation systems, and content-addressable memory.
• Medical Applications:Processing biological signals like EEG or EKG for real-time diagnostics, seizure detection, or prosthetic control can benefit from neuromorphic speed and efficiency.

Best Practices

1. Embrace Event-Driven Design:Shift your thinking from synchronous, clock-driven operations to asynchronous, event-driven processing. Optimize for spike sparsity and timing.
2. Quantization and Fixed-Point Arithmetic:Neuromorphic hardware often uses fixed-point numbers and limited precision for energy efficiency. Design your models with this in mind to avoid accuracy degradation.
3. Local Learning Rules:Explore Spike-Timing-Dependent Plasticity (STDP) and other bio-inspired learning rules that enable on-chip, unsupervised, or semi-supervised learning without the need for vast datasets and backpropagation.
4. Hardware-Software Co-design:While starting with simulators, be aware of the target neuromorphic hardware’s constraints and capabilities. Optimizing for a specific chip (e.g., Loihi’s core structure) can yield significant performance gains.
5. Benchmarking and Profiling:Carefully measure the power consumption, latency, and throughput of your neuromorphic applications. Tools provided by hardware vendors (e.g., Intel’s Loihi tools) are crucial here.

Common Patterns

• Spiking Feature Extraction:Using convolutional SNN layers to extract features from sensory data, similar to conventional CNNs, but with spikes.
• Recurrent SNNs:Implementing short-term memory and temporal processing by connecting neurons back to themselves or other neurons in the network.
• State-Dependent Computation:Neurons’ behavior changing based on their internal state (e.g., membrane potential, adaptation currents), allowing for complex dynamic computations.
• Sparse Coding:Representing information using only a small fraction of active neurons, leading to energy efficiency.

When to Go Neuromorphic: Comparing Brain-Inspired Approaches

Deciding whether to adopt neuromorphic computing for a project involves understanding its unique strengths and contrasting them with established technologies like traditional CPUs, GPUs, and specialized AI accelerators (TPUs). This isn’t a “replacement” discussion but rather a “complementary” one, identifying the optimal use cases for each.

Neuromorphic vs. Traditional Von Neumann Architectures (CPUs)

• CPUs:
  • Strengths:Highly versatile, excellent for sequential tasks, complex control logic, and general-purpose computation. Mature development ecosystem, vast software libraries.
  • Weaknesses:“Von Neumann bottleneck” – data constantly moved between CPU and memory, consuming significant energy and time for AI tasks. Poor parallelism for dense, brain-like computations.
• Neuromorphic Architectures:
  • Strengths:
    • Energy Efficiency:Orders of magnitude more efficient for certain AI tasks due to event-driven processing and memory-compute co-location. Only active neurons and synapses consume power.
    • Parallelism and Scalability:Highly parallel, localized computation. Scales efficiently for large neural networks.
    • Real-time Processing:Designed for low-latency, real-time responses to sensory inputs.
    • Local Learning/Adaptation:Can perform on-chip learning and adaptation, reducing reliance on cloud-based training and enabling continuous learning in dynamic environments.
  • Weaknesses:
    • Niche Applications:Currently best suited for specific types of tasks (event-driven data, sparse computations). Not a general-purpose processor.
    • Immature Ecosystem:Fewer tools, frameworks, and developer experience compared to CPUs/GPUs.
    • Programming Complexity:Requires a different programming paradigm (SNNs, event streams) which can be a learning curve.

Neuromorphic vs. GPUs/TPUs (Deep Learning Accelerators)

• GPUs/TPUs:
  • Strengths:
    • Dense Matrix Operations:Unparalleled performance for matrix multiplications and additions, which are the backbone of conventional Artificial Neural Networks (ANNs) and deep learning.
    • High Throughput:Excellent for batch processing large datasets during training.
    • Mature DL Frameworks:TensorFlow, PyTorch, etc., are optimized for these architectures.
  • Weaknesses:
    • Energy Consumption:High power draw, especially during intense computations, making them less suitable for edge devices.
    • Latency for Spiky Data:Not optimized for sparse, event-driven data, leading to inefficiencies when processing asynchronous events.
    • Limited On-Chip Learning:Primarily inference engines, with training often offloaded to larger systems.
• Neuromorphic Architectures:
  • Strengths:(As above) Particularly shine where GPUs/TPUs falter:
    • Event-Driven Data:Superior for data from dynamic vision sensors (DVS cameras) or other event-based sensors.
    • Low-Power Inference and Training at the Edge:Enables sophisticated AI on battery-powered devices.
    • Continuous Learning:The ability to adapt weights locally, without needing to re-train entire models in the cloud.
  • Weaknesses:
    • Not for Dense ANNs:Less efficient for running traditional, dense deep learning models that rely heavily on floating-point matrix operations.
    • Sparse Data Requirement:Performance benefits are maximized when data is sparse and event-driven.
    • Limited Data Types:Often optimized for fixed-point or integer arithmetic, not floating-point.

Practical Insights: When to Leverage Neuromorphic Computing

• Choose Neuromorphic when:

  • Energy Efficiency is CRITICAL:Deploying AI on battery-powered devices, IoT sensors, or embedded systems where power budgets are extremely tight.
  • Real-time Responsiveness is Key:Applications requiring immediate, low-latency reactions to sensory input, such as autonomous vehicles, drones, or industrial control systems.
  • Event-Driven Data is Dominant:Working with specialized sensors like DVS cameras, audio event streams, or radar data that naturally generate sparse, asynchronous events.
  • On-Device Learning/Adaptation is Required:Systems that need to continuously learn or adapt to new patterns in their environment without constant re-training from a central server.
  • Sparse Computation is Preferred:Tasks where information can be effectively processed with only a small percentage of neurons active at any given time.

• Stick with Traditional Architectures (CPUs/GPUs) when:

  • Large-scale Batch Training of Dense ANNs:Training complex, general-purpose deep learning models like large language models or image recognition networks on massive datasets.
  • High Throughput of Traditional Operations:Applications dominated by dense matrix algebra, floating-point arithmetic, and clock-synchronous operations.
  • Maturity and Ecosystem are Paramount:When development speed, access to extensive libraries, and a large developer community are primary concerns.
  • General-Purpose Computation:For tasks that are not specifically AI-focused or don’t fit the event-driven paradigm.

In essence, neuromorphic computing is a powerful arrow in the quiver of AI developers, particularly for specialized, resource-constrained, and real-time cognitive tasks. It’s not meant to replace GPUs for large-scale deep learning but to open up entirely new categories of AI applications.

The Future is Spiking: Embracing Brain-Inspired Systems

Neuromorphic computing stands as a testament to humanity’s ongoing quest to unravel the brain’s mysteries and harness its unparalleled efficiency for artificial intelligence. We’ve explored how this paradigm shifts from traditional clock-driven, memory-separated systems to event-driven, memory-integrated architectures, mimicking the brain’s fundamental operational principles. For developers, this isn’t just an academic curiosity; it’s a call to action to prepare for a future where AI is more pervasive, more efficient, and more responsive than ever before.

By diving into frameworks like Nengo and Intel’s Lava, experimenting with spiking neural networks, and understanding the unique advantages of neuromorphic hardware, you’re not just learning a new technology – you’re mastering a new way of thinking about computation. The practical examples and comparisons provided highlight that neuromorphic systems are not a direct replacement for existing AI accelerators but a powerful complement, especially for edge AI, real-time sensory processing, and scenarios demanding ultra-low power consumption and continuous adaptation.

The journey ahead involves refining development tools, standardizing programming models, and expanding the scope of applications. As neuromorphic hardware matures and becomes more accessible, the developers who have embraced this brain-inspired approach will be at the forefront of innovation, architecting AI solutions that were previously constrained by power, latency, or processing limitations. The future of AI is undeniably spiking, and by equipping yourself with these skills today, you’re building the intelligence of tomorrow.

Spiking Knowledge: FAQs on Brain-Inspired Systems

Is neuromorphic computing ready for widespread commercial use?

While the technology is rapidly advancing, widespread commercial adoption is still nascent. Current neuromorphic chips like Intel’s Loihi and IBM’s TrueNorth are primarily research platforms. However, companies like BrainChip (with Akida) are making strides toward commercializing neuromorphic IP for edge AI. Expect to see more specialized applications and integrated solutions emerge before it becomes a general-purpose computing staple.

What programming languages are used for neuromorphic development?

Python is the dominant language for high-level development, model definition, and simulation, thanks to its rich ecosystem of AI and scientific libraries. Frameworks like Nengo and Intel’s Lava leverage Python extensively. Lower-level interactions or custom hardware drivers might involve C/C++, but most application development occurs in Python.

How does neuromorphic computing compare to quantum computing?

They are fundamentally different paradigms. Neuromorphic computing aims to mimic the brain’s architecture and processing style for AI tasks, often focusing on energy efficiency and event-driven computation with classical physics. Quantum computing, on the other hand, utilizes principles of quantum mechanics (superposition, entanglement) to solve certain computational problems exponentially faster than classical computers, particularly for specific types of optimization or cryptographic tasks. They address different challenges and are not direct competitors.

What are the main challenges in neuromorphic development?

Key challenges include:

1. Programming Paradigm Shift:Moving from traditional imperative programming to event-driven, asynchronous SNNs.
2. Training SNNs:Developing efficient and robust training algorithms for SNNs, which is often more complex than backpropagation for ANNs.
3. Hardware-Software Integration:Optimizing models to run efficiently on specific neuromorphic hardware platforms, which can have unique constraints.
4. Tooling and Ecosystem Maturity:The ecosystem is still evolving compared to traditional AI development.
5. Benchmarking and Metrics:Establishing standardized ways to measure performance, power, and efficiency across diverse neuromorphic architectures.

Can I convert existing neural networks to run on neuromorphic hardware?

Yes, this is an active area of research and development. Techniques exist to convert trained Artificial Neural Networks (ANNs) into Spiking Neural Networks (SNNs) for deployment on neuromorphic hardware. This often involves quantizing weights and activations and finding spike-rate equivalents for ANN activations. Frameworks like Lava (with lava-dl) are explicitly designed to facilitate this conversion and deployment process, albeit with some potential loss in accuracy or requiring careful tuning.

Essential Technical Terms Defined:

1. Spiking Neural Networks (SNNs):A type of artificial neural network that more closely mimics biological neural networks. Unlike traditional ANNs, SNNs communicate using discrete events (spikes) rather than continuous values, processing information based on the timing of these spikes.
2. Synapse:In biological and neuromorphic systems, a synapse is a junction between two neurons that allows the transmission of signals. In neuromorphic hardware, it typically represents a memory element storing a weight that modulates the influence of one neuron’s spike on another.
3. Neuron:The fundamental processing unit in a neural network. In neuromorphic computing, this often refers to a “spiking neuron model” (e.g., Leaky Integrate-and-Fire - LIF), which accumulates input, generates a spike when a threshold is reached, and then resets.
4. Plasticity:The ability of synapses to change their strength over time in response to activity. This is the basis of learning and memory in biological brains and is implemented in neuromorphic hardware through local learning rules like Spike-Timing-Dependent Plasticity (STDP).
5. Loihi:Intel’s research chip designed for neuromorphic computing. It features thousands of configurable neuromorphic cores, each with its own memory and ability to simulate multiple spiking neurons and synapses, optimized for energy-efficient, event-driven computation.

Published on November 20, 2025