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Talk given by Prof. Shahar Kvatinsky

Today’s AI applications demand tremendous computing power. However, existing hardware for AI is hitting a bottleneck in terms of speed and power. This is particularly because of the data movement required due to the separation of computing and memory in the von Neumann architecture and the large energy consumption, access time and cost of existing memory technologies. Existing AI uses devices like GPUs and dedicated hardware like TPUs and edge inference devices like ASICs. However, the brain is able to perform its processing at very low power and is particularly good at perception tasks.

In this talk, Prof. Shahar Kvatinsky explained potential ways to improve hardware for AI by brain inspired neuromorphic computing using emerging memory devices called memristors. Memristors can emulate synaptic functions and can be used to accelerate neural networks. One way is to perform Vector Matrix Multiplication (VMM) and Multiply and Accumulate (MAC) operations using crossbars of memristors. He explained training of memristive neuromorphic systems using backpropagation and stochastic gradient descent, methods for low power neuromorphic computing like low precision AI inference and trainable data converters and discussed some of the security issues and mitigation strategies. He also talked about various emerging memory devices that can be used to build these systems like Magnetic Tunnel Junction (MTJ) based devices and floating gate flash memory called Yflash, built by Tower Semiconductor. He described quantized deep neural networks with MTJ based devices and simpler neural network models like Deep Belief Networks (DBNs) built with Yflash devices.

Prof. Shahar Kvatinsky’s talk highlighted some of the devices and techniques to accelerate and improve the performance of hardware for AI applications.

Written by Rishona Daniels

field of biological machine learning, promising attempts have been made to use cortical neurons for machine learning tasks. However, these efforts often lack a strong theoretical foundation from a mathematical perspective.

Prof Saad’s talk introduced three mathematical tools to address this gap:

1. Neuronal Network Inference: Saad presented an algorithm that uses machine learning and statistical physics to infer the structure and connectivity of neuronal networks from spontaneous activities. This is crucial for understanding neuronal architecture and plasticity.
2. Visual Informatics Approach: He showcased methods to study differences in neuronal activities under various conditions using advanced dimensionality reduction techniques, retaining the global structure of high-dimensional data better than traditional methods like PCA and t-SNE.
3. Spatial Entropy Measurement: Saad emphasized using a Bayesian approach to measure the spatial entropy of neuronal activities accurately, revealing significant differences between spontaneous and stimulated activities. This provides a reliable metric for studying neuronal behaviour.

Overall, Prof Saad highlighted the potential of mathematical tools to enhance our understanding of neuronal networks and advance biological machine learning.

Presenting the consorta at the 2023 International Symposium on Nonlinear Theory and Its Applications (NOLTA) a number of the group were able to present over the week event, bring in lots of discussion and interest from others, as well as push forward ideas amongst NeU-ChiP partners in various places, even on Mount Etna. Papers and abstracts from the event can be found here.

A special symposia for NEU-ChiP ran over two days as a hybrid meeting, enabling discussion with collaborators around the world.

Around the physical sessions the NEU-ChiP team found time to discuss ideas.

Around the conference we had the chance to see a little of the city of Catania, with some great food, and an excellent conference dinner in the beautiful museum.

The NEU-ChiP team also found some time to enjoy the surrounding sites with some adventures up to Mount Etna.

The brain is one of the most complex organs in the human body, responsible for everything from our thoughts and emotions to our ability to move and sense the world around us. It is a fascinating and mysterious structure, and scientists have been studying it for centuries in an attempt to understand how it functions.

One of the most recent and exciting developments in this area is the use of mathematical models to understand the brain. Mathematical models are simplified representations of complex systems, and they can be used to predict the behavior of those systems under different conditions.

In the context of the brain, mathematical models can help us understand how neurons communicate with each other, how neural networks form, and how the brain processes information. They can also be used to simulate the effects of drugs or other interventions on the brain, which could lead to the development of new treatments for neurological disorders.

One of the most famous examples of a mathematical model of the brain is the Hodgkin-Huxley model, developed in the 1950s. This model describes the behavior of neurons and their ability to transmit electrical signals. Since then, many other mathematical models have been developed, each one building on the knowledge gained from previous models.

One of the key advantages of using mathematical models to study the brain is that they allow us to explore the behavior of the brain in a way that would be impossible with traditional experiments. For example, it would be difficult to study the behavior of millions of neurons in real-time, but a mathematical model can simulate this behavior and allow us to explore the consequences of different scenarios.

Mathematical models can also be used to test hypotheses in a more systematic way. Instead of relying on trial-and-error experiments, researchers can use mathematical models to predict the outcome of an experiment before it is conducted. This can save time and resources and lead to more efficient research.

Of course, there are also limitations to using mathematical models to study the brain. For example, mathematical models are only as good as the data that goes into them, and there is still much we don’t know about how the brain functions. Additionally, mathematical models can only provide a simplified representation of the brain, and it is important to remember that they are just one tool in the arsenal of neuroscientists.

In conclusion, the development of mathematical models to understand the brain is an exciting and rapidly evolving field of research. By using these models, scientists are gaining new insights into how the brain functions and how it can be treated when it malfunctions. While there are limitations to using mathematical models, their potential for advancing our understanding of the brain is enormous, and we can expect to see many more exciting developments in the years to come.

Creating Living Circuits with Microfabrication

Microfabrication tools have revolutionized the way we engineer biological systems, allowing us to manipulate living cells at a level of precision that was once impossible. Among the many applications of microfabrication in the field of biology is the creation of living circuits from neurons in vitro. This technique has the potential to revolutionize the field of neuroscience, by providing a platform for studying the behavior of neurons and neural networks in a controlled environment.

In this blog post, we will explore how microfabrication tools can be used to create living circuits from neurons in vitro, and discuss some of the potential applications of this technique.

What are living circuits from neurons in vitro?

Living circuits from neurons in vitro are networks of neurons that are grown in a dish and connected in a specific pattern using microfabrication tools. These circuits can be used to study the behavior of neurons in a controlled environment and to explore the properties of neural networks.

The basic idea behind living circuits is to create a pattern of microchannels on a substrate, which can be filled with a solution containing neurons. The neurons then grow in the microchannels, forming connections with each other and creating a functional network.

The process of making living circuits from neurons in vitro involves several steps, including microfabrication, cell culture, and network formation. Let’s look at each of these steps in more detail.

Microfabrication: The first step in making living circuits is to create a pattern of microchannels on a substrate. This is typically done using photolithography, a technique that uses light-sensitive materials to create patterns on a substrate. The substrate can be made of a variety of materials, including glass, silicon, or polymer.

Cell culture: Once the microchannels are created, the next step is to culture neurons in the channels. This is done by seeding the channels with a solution containing neurons. The neurons will adhere to the surface of the channels and begin to grow.

The final step is to allow the neurons to form connections with each other, creating a functional network. This is typically done by allowing the neurons to grow for several days or weeks, during which time they will form connections with each other and begin to communicate.

Living circuits from neurons in vitro have many potential applications in the field of neuroscience. One potential application is in the study of neurodegenerative diseases, such as Alzheimer’s and Parkinson’s disease. By creating living circuits from neurons in vitro, researchers can study the effects of these diseases on the behavior of neurons and neural networks, which could lead to the development of new treatments.

Another potential application of living circuits is in the development of neural prosthetics. Neural prosthetics are devices that can be implanted in the brain to restore lost function, such as the ability to move or communicate. By studying the behavior of neurons in living circuits, researchers can develop better prosthetics that are more effective and longer-lasting.

In conclusion, living circuits from neurons in vitro are an exciting new tool for studying the behavior of neurons and neural networks. By using microfabrication tools to create these circuits, researchers can study the effects of disease, develop new treatments, and create better neural prosthetics. With continued research and development, the potential applications of living circuits are endless, and we are only just beginning to scratch the surface of what is possible.