Neuro Science    

 

 

 

Dish Neuron / In Vitro Neuron

In-vitro neurons are brain cells grown in a lab, not inside a body. Scientists do this to learn more about how these cells work on their own. This method lets them see how neurons communicate, respond to different situations, and change over time without the complexity of the whole brain. This research helps us understand how the brain functions, what goes wrong in brain diseases, and how to find new treatments. It's also used in creating new technologies that combine living cells with electronic devices.

What is this technology for ?

The use of in-vitro neurons, or brain cells grown in a lab, has many important uses. It helps scientists learn more about how our brain works and find new ways to treat brain diseases. This technology is key in discovering new medicines by testing how different drugs affect these cells. Also, it plays a big role in connecting the human brain with computers, leading to new inventions that can help people communicate or think better. This research is not just about treating illnesses but also about making new discoveries in technology and understanding more about how we think and feel.

  • Neuroscience Research: It provides a detailed understanding of how neurons work, communicate, and develop. This is crucial for unraveling the complexities of the brain and nervous system.
  • Drug Discovery and Testing: In-vitro neurons allow for the testing of new drugs and treatments for neurological disorders in a controlled environment, speeding up the development of effective therapies.
  • Disease Modeling: Researchers can model neurological diseases using in-vitro neurons, helping them study disease mechanisms and identify potential therapeutic targets.
  • Regenerative Medicine: This technology contributes to the development of stem cell therapies aimed at repairing or replacing damaged neurons in conditions like spinal cord injuries or neurodegenerative diseases.
  • Brain-Computer Interfaces (BCIs): By integrating in-vitro neurons with electronic devices, scientists are paving the way for advanced BCIs that could help restore lost sensory or motor functions.
  • Educational Tools: In-vitro neuron cultures serve as valuable educational tools for students and researchers learning about neurobiology and the functioning of the nervous system.
  • Synthetic Biological Intelligence: As seen in the DishBrain system, in-vitro neurons can be used to create synthetic biological intelligence, opening new avenues in computing and artificial intelligence research.

What is drawback  ?

Growing brain cells in a lab, using in-vitro neuron technology, is a big step forward in science. However, it's important to look at the problems and questions it brings up. Here, we talk about the difficulties, like how complex and expensive the process is, and the ethical concerns about using human cells. We also discuss why what works in the lab might not always work in real life. Understanding these issues helps us see the full picture of this technology and what needs to be done to make it better and more useful for everyone.

Here comes with its set of drawbacks and challenges:

  • Complexity and Cost: Culturing neurons in-vitro requires sophisticated equipment, controlled environments, and significant expertise, making the process complex and costly.
  • Limited Context: While in-vitro models allow for controlled study of neuronal behavior, they lack the full complexity of living organisms. This absence of a complete biological context can limit the understanding of how neurons operate within the natural environment of a living brain.
  • Scalability: Scaling up in-vitro neuron cultures for large-scale applications or treatments poses significant challenges, including maintaining cell health and function over time.
  • Translation to Clinical Use: There is always a gap between laboratory findings and their application in real-world clinical settings. Results observed in cultured neurons might not always translate directly to effective treatments in humans due to the complexity of the human body and brain.
  • Longevity and Stability: Maintaining the viability and functionality of in-vitro neurons over extended periods is challenging, which can limit long-term studies or applications.
  • Artificial Environment: The artificial environment in which in-vitro neurons are grown may not perfectly mimic the conditions within a living brain, potentially affecting the cells' behavior and responses in ways that are not fully representative of natural neuronal function.
  • Ethical Concerns: The use of human neuronal cells raises ethical questions, especially when it comes to sourcing these cells, consent, and the potential for creating consciousness or pain perception in vitro.

Playing Pong by Dish Brain ?

This shows a dish of cultured neurons (in-vitro neurons) that are part of a biohybrid system interfacing with a computer simulation of the game "Pong." The diagram illustrates how the neurons receive sensory input and produce motor outputs to interact with the game, with prediction errors and sensory perceptions being critical components of the system. The setup aims to study how these neurons can adapt and learn within this simulated environment.

How the test works ?

NOTE : Check out this video : Lab-Grown "Mini-Brain" Learns Pong - Is This Biological Neural Network "Sentient"?  if you have difficulties understanding how this works just by reading.

This is what and how this setup works. (NOTE : most of the details here came from In vitro neurons learn and exhibit sentience when embodied in a simulated game-world )

  • Sensory Area and Electrodes: The neurons were grown on a high-density multielectrode array with a predefined sensory area made up of 8 electrodes. These electrodes provided the neurons with information about the game.
  • Motor Regions and Paddle Movement: Two separate motor regions were identified, which corresponded to the movement of the paddle in the Pong game. One region controlled the paddle's upward movement and the other controlled the downward movement.
  • Playing the Game: The electrophysiological activity (the neurons' activity) was used in real-time to control the paddle. If the neurons' activity led to the paddle hitting the ball, the game would continue.
  • Feedback Through Stimulation:
    • If the neurons failed to intercept the ball with the paddle, an unpredictable stimulus was applied: a 150mV voltage at 5Hz for 4 seconds. This was meant to simulate a form of "punishment" for missing the ball.
    • If the paddle successfully hit the ball, a predictable stimulus was given across all electrodes simultaneously at 100Hz for 10ms. This served as a "reward" for successfully hitting the ball.
    • NOTE : how neuron can recognize the unpredictable stimulus as punishment and predictable stimulus as reward when they don't have any high level cognitive capability ?  
      • Unpredictable Stimulus as Punishment: An unpredictable stimulus is considered a punishment because it disrupts the pattern of stimuli that the neurons are starting to understand or predict. It serves as a negative feedback signal, indicating that the previous action did not have the desired outcome. This unpredictability is key because it signals to the neurons that their current behavior is not achieving the intended effect, prompting them to adjust their firing patterns.
      • Predictable Stimulus as Reward: A predictable stimulus acts as a reward because it confirms the neurons' predictions about the environment. When neurons successfully predict the sensory consequences of their actions, the predictable nature of the feedback reinforces that behavior. In the case of DishBrain, successfully hitting the ball with the paddle led to a brief, predictable electrical pulse, which would be 'perceived' by the neurons as a positive outcome.
    • NOTE : How we can implement 'unpredictability' ?
      • Based on common practices in similar experiments, unpredictability could be implemented in several ways:
      • Random Timing: The stimulus could be delivered at random intervals rather than in a consistent pattern.
      • Random Intensity: The strength of the electrical signals could vary unpredictably in amplitude.
      • Random Location: The electrical pulses could be delivered to random electrodes rather than the same electrode each time.
      • Random Frequency: The frequency of the pulses could change, so the neurons cannot anticipate when the next pulse will occur.
  • Learning and Adaptation: The idea was that the neurons would self-organize and adapt their firing patterns to better control the paddle and hit the ball more consistently, thus learning to play Pong through a process of trial and error, similar to how a human or animal might learn through consequences.
  • Testing and Results: The experiment included tests with different configurations of the motor regions to ensure that there was no bias in the neurons' response due to the setup. The neurons demonstrated the ability to hit the ball more consistently over time, indicating a form of learning and adaptation.
  • Self-Organization: The fixed layout of the motor regions meant that the neurons had to develop distinct firing patterns on their own to control the paddle, raising questions about the extent of self-organization that could occur

Why this test ?

The DishBrain experiment sought to bridge the gap between biological intelligence and computational models, offering a new way to look at how neurons function individually and as part of larger networks. This experiment was multi-faceted as follows:

  • To Demonstrate Adaptive Behavior In-Vitro: The experiment aimed to show that biological neural networks (BNNs), when placed in a simulated environment, could adapt their behavior in real-time in response to stimuli. This is a fundamental aspect of learning that is well established in living organisms (in vivo), but this was the first time it was established in a lab setting (in vitro) for goal-directed behavior​​.
  • Investigation of Neuronal Computation: The system was used to investigate the fundamentals of how biological neurons process information and compute responses to stimuli. This could help understand how neurons carry out complex tasks such as spatial and non-spatial problem-solving, which had been previously modeled in silico (using computer simulations)​​.
  • Technical Advancement: The experiment represented a significant technical step forward by creating a closed-loop environment for BNNs. A closed-loop system is one in which the outputs (or responses) of the system are fed back into it as inputs, creating a self-regulatory system. This setup allows for the dynamic interaction between the neural activity and the environment, which is essential for adaptive learning​​.
  • Data Generation on Neuronal Processing: The DishBrain platform allowed researchers to test different conditions, such as cell types, drug effects, and feedback scenarios, to gather new data on how cells process information and respond to changes in their environment. This data could provide insights that were not previously attainable due to the limitations of in silico models or the inability to observe such phenomena directly in vivo

Reference

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