Spiking Neural Networks (SNNs) draw inspiration from the biological behavior of neurons in the human brain. In biological systems, neurons communicate through electrical signals called action potentials or spikes. These spikes are the fundamental units of information transfer in the brain, enabling the transmission of signals across interconnected networks of neurons. SNNs aim to replicate this spiking behavior and the associated communication dynamics in artificial neural networks.
Neural networks have revolutionized the field of artificial intelligence, enabling significant advancements in various applications such as image recognition, natural language processing, and autonomous systems. Traditional artificial neural networks are based on continuous activations, which mimic the behavior of neurons firing at a constant rate.
However, the brain operates using discrete, spiking neural activity. Spiking Neural Networks (SNNs) offer a biologically inspired approach that models neural communication more accurately, holding the promise of enhanced computational efficiency, improved neuroplasticity, and a deeper understanding of neural dynamics.
How do Spiking Neural Networks relate to biology?
By emulating the following biological principles, Spiking Neural Network (SNN) aim to create more biologically plausible models of neural computation and provide novel solutions for various artificial intelligence applications.
1. Neuron Activation:
· Biological Neurons: Neurons in the brain receive inputs from other neurons through their dendrites. If the total input surpasses a certain threshold, the neuron generates an action potential (spike) that travels down its axon to transmit the signal to other connected neurons.
· SNNs: Similarly, in SNNs, artificial neurons accumulate inputs over time. Once the accumulated input crosses a threshold, the neuron emits a spike, simulating the firing behavior of biological neurons.
2. Temporal Coding:
· Biological Neurons: The timing of spikes is critical in conveying information in the brain. Neurons can communicate complex patterns by varying the intervals between their spikes.
· SNNs: Temporal coding is a key feature of SNNs. The precise timing of spikes carries information, allowing SNNs to capture and process time-varying patterns, such as recognizing patterns in dynamic sensory data.
3. Synaptic Plasticity:
· Biological Neurons: The strength of connections (synapses) between neurons can change over time in response to activity patterns. This phenomenon is known as synaptic plasticity, and it plays a crucial role in learning and memory.
· SNNs: SNNs emulate synaptic plasticity through mechanisms like Spike-Timing-Dependent Plasticity (STDP), where the timing of pre-synaptic and post-synaptic spikes determines whether the connection's strength should be adjusted. This allows SNNs to adapt and learn from the input patterns they receive.
4. Energy Efficiency:
· Biological Neurons: The brain is remarkably energy-efficient, as neurons only fire spikes when necessary, conserving energy.
· SNNs: SNNs share this energy-efficient property since they perform computations in an event-driven manner, firing spikes when inputs cross a threshold. This leads to reduced overall computational effort compared to continuous activation-based networks.
5. Event-Driven Processing:
· Biological Neurons: Neurons in the brain communicate through discrete, event-based spikes. This allows the brain to process information efficiently and adapt to changing inputs.
· SNNs: SNNs similarly process information in an event-driven manner, with neurons firing only when necessary. This enables SNNs to handle dynamic inputs and respond to them in real-time.
Architecture of Spiking Neural Network
The architecture of a Spiking Neural Network (SNN) is designed to mimic the biological principles of spiking neurons, synapses, and their dynamic interactions, while also accommodating the computational needs of artificial intelligence tasks.
Input Layer:
The input layer receives external stimuli or data and encodes them into spike trains. Each input neuron represents a feature or input dimension, and its spiking activity is determined by the input data.
Spiking Neurons:
The core of the SNN consists of spiking neurons. These neurons accumulate input over time and emit spikes when their internal membrane potential reaches a certain threshold.
Neurons can have different properties and behaviors, such as leaky integrate-and-fire (LIF) neurons, which simulate the gradual buildup of charge and its eventual discharge as a spike.
Synaptic Connections:
Neurons are interconnected through synapses, which transmit information from one neuron to another.
Synapses have associated weights that determine the strength of the connection. These weights are modified over time based on learning rules like Spike-Timing-Dependent Plasticity (STDP), which adjust weights depending on the timing of pre-synaptic and post-synaptic spikes.
Hidden Layers:
SNNs can have one or more hidden layers that process intermediate representations of the input data.
These layers also consist of spiking neurons connected via synapses, and they contribute to the hierarchical feature extraction and transformation of the input data.
Output Layer:
The output layer receives spikes from the hidden layers and generates the final output based on the patterns of spiking activity.
Different patterns of spikes can represent different classes or categories in classification tasks, for example.
Ferroelectric Tunnel Junction (FTJ) in Spiking Neural Networks
FTJs are nanoscale devices characterized by a thin ferroelectric layer sandwiched between two metal electrodes. The magic lies in the ferroelectric material's ability to exhibit two stable polarization states, effectively serving as the 0 and 1 of binary information. In the realm of SNNs, each neuron's state finds expression through the polarization state of an FTJ. This state mirrors the membrane potential of biological neurons, allowing for a nuanced representation of computational elements.
When a neuron receives input spikes from connected neurons, corresponding FTJs, acting as synapses, experience voltage pulses. These pulses dynamically alter the tunnelling current between the ferroelectric states, effectively modulating the synaptic strength. This dynamic modulation simulates the way biological neurons integrate signals from various sources. The FTJ plays a pivotal role in integrating these modulated synaptic inputs. As the polarization state changes in response to the integrated inputs, the artificial neuron monitors this state. Upon reaching a predetermined threshold, mirroring the firing threshold of biological neurons, the FTJ triggers a spiking event. One of the unique advantages of employing FTJs in SNNs lies in their non-volatile nature. The polarization states remain stable even when the voltage is removed, enabling the retention of information between computational steps. This non-volatility aligns with the memory retention capabilities essential for certain neural network tasks.
Patent Analysis
Spiking Neural Networks (SNNs) has seen a notable surge in patent filings in recent years, reflecting the growing interest and potential in this innovative neural network paradigm. Over the past six years, patent filings related to SNNs have increased significantly, with a nearly 2.5-fold rise in activity.
The impact of the COVID-19 pandemic has further accelerated this growth in the SNN domain. Between 2019 and 2021, there was a substantial 2-fold increase in patent filings. The pandemic underscored the importance of advanced AI techniques like SNNs in addressing scientific challenges. It prompted countries and research institutions to collaborate extensively, share data and findings, and collectively harness the power of SNNs to tackle health crises.
A boost in patent filing in the field of Spiking Neural Networks reflects the excitement and potential surrounding this innovative approach to neural computation. The diverse range of applications, technological advancements, and commercial opportunities drive stakeholders to protect their ideas, innovations, and competitive edge through patent filings. Some of the key reasons might include:
1. Novelty and Innovation: SNNs represent a departure from traditional artificial neural networks, with their focus on spiking behavior, temporal coding, and neuromorphic computing. As researchers explore new architectures, algorithms, and applications based on SNNs, they are likely to develop novel and innovative techniques that could be eligible for patent protection.
2. Commercial Applications: SNNs hold promise in a wide range of fields, including robotics, sensory processing, cognitive computing, brain-machine interfaces, and more. Companies and research institutions recognize the commercial potential of these applications and seek to protect their intellectual property by filing patents.
3. Neuroprosthetics and Medical Devices: In the realm of medical technology, SNNs have the potential to drive advancements in neuroprosthetics, neurorehabilitation, and personalized medicine. These areas are highly regulated and competitive, motivating stakeholders to secure their innovations through patents.
4. Neuromorphic Hardware: The development of specialized hardware architectures for simulating SNNs has gained traction. These hardware platforms are designed to efficiently mimic the behavior of biological neurons and can lead to breakthroughs in energy-efficient computing. Companies investing in this hardware are likely filing patents to protect their technological advancements.
5. Broader AI Landscape: SNNs are a subset of the broader artificial intelligence landscape. With the AI field rapidly evolving, stakeholders are eager to secure intellectual property that can set them apart in an increasingly competitive market.
· Top 10 players in patent filing
Qualcomm is the top player in patent filing in the field of spiking neural networks. Qualcomm has been investing in research on spiking neural networks for many years, and they have a team of world-leading experts in the field. Qualcomm has filed around 450 patents related to spiking neural networks, which gives them a strong competitive advantage; almost double, triple the number of patents filed by its other competitors such as IBM, Strong Force and Micron technology, among others.
Qualcomm is not just doing research on spiking neural networks, they are also actively developing products that use them. This shows that they are committed to the technology and believe in its potential.
Here are some specific examples of Qualcomm's work in the field of spiking neural networks:
In 2017, Qualcomm launched the Snapdragon Neural Processing Engine (SNPE), a software platform for developing and deploying spiking neural networks on mobile devices.
In 2019, Qualcomm announced the development of a new spiking neural network chip called the Cloud AI 100.
In 2020, Qualcomm announced the development of a new spiking neural network chip called the Cloud AI 100. This chip is designed for use in data centres and other high-performance computing applications.
In 2021, Qualcomm collaborated with Google AI on Neural Architecture Search (NAS) to develop a new spiking neural network architecture called the Sparse Spiking Neural Network (SSN). This architecture is designed to be more energy-efficient than traditional spiking neural networks.
In 2022, Qualcomm announced the Snapdragon X70 5G modem, which includes a new spiking neural network accelerator. This accelerator is designed to improve the performance of 5G applications that use spiking neural networks, such as augmented reality and virtual reality.
Advantages of SNN
SNNs have several advantages compared to conventional approaches to neural networking and computing:
Efficient Like Brain: Just like how brain doesn't fire all neurons at once, it responds when needed. SNNs do the same, firing only when there's important information to process. This efficiency is great for tasks to save energy, just like conserving our own mental energy.
Timing: SNNs pay close attention to the timing of events, recognizing the importance of timing in real-life situations. This is handy for tasks like understanding speech or recognizing gestures, where the sequence of events matters.
Robustness to Noise: SNNs are good at ignoring irrelevant information and focusing on what's important, making them robust in noisy and messy data environments.
Learning on the Go: SNNs are adaptable in real-time, which is fantastic for tasks that involve learning from constantly changing data, like autonomous vehicles adjusting to different driving conditions.
Learning from Experience: SNNs can be trained to learn from new data continually, making them great for applications where the world is constantly changing, much like life itself.
Network of Specialists: SNNs can have specialized neurons that excel in specific tasks, creating a network that's like team of experts collaborating on a project.
Smart, Yet Humble: SNNs can handle uncertainty better, which is like acknowledging when they're not sure about a decision, making them suitable for tasks.
Online Learning: SNNs can be designed for online learning, allowing them to adapt to changing data distributions in real-time. This makes them suitable for applications where the underlying data distribution is non-stationary and requires continuous learning.
Event-Based Processing: SNNs operate in an event-driven manner, processing information only upon the occurrence of spikes. This allows for efficient, asynchronous processing, making them suitable for tasks involving sparse and asynchronous data, such as spike trains in neurophysiology or event-based sensor data.
Neuromorphic Hardware: SNNs are often used in the development of neuromorphic hardware architectures, which aim to mimic the brain's processing capabilities. These architectures can offer advantages in terms of power efficiency and parallel processing, which is valuable in specialized applications.
Conclusion
Spiking Neural Networks (SNNs) represent a remarkable stride towards bridging the gap between artificial intelligence and the complex dynamics of the human brain. Drawing inspiration from the biological behavior of neurons, SNNs introduce event-driven computation, temporal coding, and plasticity into the realm of machine learning. Their ability to process time-varying information, exhibit energy-efficient behavior, and adapt to changing environments offers a new paradigm for solving intricate problems across various domains. The continued exploration and development of SNNs hold promise for advancing AI capabilities and deepening our understanding of neural computation. The future of Spiking Neural Networks is poised for significant growth and exploration.
