“fire together, wire together” – will ML ever do biology? – a conversation

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Machine learning backpropogation requires that the established weight and bias values established by the forward pass be stored for the backpropogation algorithm to work with. Biological brains use the “neurons that fire together wire together” concept to establish patterns in the brain akin to those established by backpropogation. These are very different causal paths to achieve the same result which is a neural pattern that “gets smart”. Is it possible that the biological brain is also doing a forward pass that we have yet to discover along with a process like backpropogation? Or will ML Algorithms be found to capture the “fire together, wire together” concept of biology? Comment on this with a 2000 word paper.

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The relationship between biological brains and artificial neural networks (ANNs) is one of the most fascinating and interdisciplinary topics, lying at the intersection of neuroscience, cognitive science, and machine learning. While there is still much to be understood about the inner workings of the biological brain, the comparison between backpropagation in machine learning and Hebbian learning (the “neurons that fire together wire together” concept) opens up intriguing questions. This exploration will delve into whether the biological brain might engage in processes akin to a forward pass and backpropagation, and whether machine learning algorithms could evolve to capture more of the complexity of biological learning.

Machine Learning Backpropagation: A Brief Overview

In artificial neural networks, learning occurs through a process known as backpropagation. The forward pass of a neural network takes in an input and pushes it through multiple layers of neurons, calculating weighted sums and applying non-linear activation functions to produce an output. The network’s initial weights and biases are adjusted through training, which involves the backpropagation of errors. In backpropagation, the error (or loss) between the predicted output and the actual target output is calculated, and this error is propagated backward through the network. Gradients of the error are computed with respect to the network’s weights and biases, and using optimization techniques like stochastic gradient descent (SGD), these parameters are adjusted to minimize the error.

For backpropagation to function effectively, the network must store the intermediate activations (results of the forward pass), and then these stored values are utilized during the backward pass to calculate the gradients. The process is highly mathematical, grounded in differential calculus, and can be seen as a form of optimization in a high-dimensional parameter space.

Biological Brains: Hebbian Learning and Neural Plasticity

Biological brains operate under a different set of principles. The “neurons that fire together, wire together” rule, known as Hebbian learning, postulates that synaptic connections between neurons are strengthened when they are activated simultaneously. When neurons fire in synchrony, their connection becomes stronger, leading to the formation of stable neural circuits. Over time, this form of plasticity allows the brain to adapt, learn, and encode information.

Hebbian learning is local in nature, meaning that it happens at the level of individual neurons and their synaptic connections, without needing a global error signal like the one used in backpropagation. Instead of propagating error signals across layers, Hebbian learning relies on local signals — such as the timing and intensity of neural firing — to modify synaptic strengths.

While Hebbian learning captures some aspects of biological learning, the brain’s processes are much more complex and multifaceted, involving a wide array of mechanisms like long-term potentiation (LTP), synaptic plasticity, neuromodulation, and inhibitory feedback loops. The biological brain doesn’t rely on a clear distinction between a forward pass and a backward pass, as in artificial neural networks.

Hypothesis 1: The Biological Brain May Also Perform a “Forward Pass”

The concept of a “forward pass” in biological brains is not as well-established as it is in machine learning, but there are some intriguing possibilities. It could be argued that sensory information processing in the brain follows a trajectory that resembles a forward pass, where sensory inputs travel through hierarchical layers of the brain’s neural circuits. For example, in the visual cortex, sensory information passes from the retina through several layers of processing, from lower-order areas like V1 to higher-order areas responsible for more abstract representations.

This resembles the forward pass in artificial neural networks, where raw inputs are processed layer by layer, with each layer extracting increasingly abstract features. However, unlike ANNs, the biological brain doesn’t store intermediate activations in the same way or propagate errors backward in a structured, algorithmic manner.

While error signaling and learning are crucial to biological cognition, they appear to rely on other processes like dopamine-driven reward signals and neuromodulation. These signals might play roles similar to the optimization objective of backpropagation but are not yet understood as fulfilling an exact “backward pass” function. Nevertheless, the forward-flowing processing of sensory inputs in the brain might bear some resemblance to the forward pass in machine learning.

Hypothesis 2: Biological Brains Use a Form of Backpropagation

The second hypothesis proposes that biological brains could employ a process analogous to backpropagation. One potential candidate for this function is predictive coding, a theoretical framework in neuroscience that suggests the brain constantly generates predictions about incoming sensory information. These predictions are compared to the actual input, and any mismatch (or prediction error) is propagated back through the system to update the brain’s internal model. Predictive coding operates by minimizing the prediction error, much like how backpropagation in ANNs minimizes the error between predicted and actual outputs.

In predictive coding models, prediction errors might be propagated backward along the cortical hierarchy in much the same way as error gradients are propagated backward through an artificial neural network. This framework has gained traction because it offers a compelling explanation for how the brain might use error signals to update its internal models, allowing it to make more accurate predictions in the future.

However, predictive coding models differ from backpropagation in critical ways. For one, they do not require the brain to store forward pass activations in the same way as artificial neural networks. Instead, predictive coding models rely on distributed and recurrent neural architectures, where prediction and error correction are happening simultaneously, rather than in distinct forward and backward passes. Therefore, while predictive coding offers a mechanism for learning through error correction, it doesn’t precisely replicate backpropagation’s mathematical elegance.

Hypothesis 3: Machine Learning May Evolve to Capture Hebbian Principles

It’s also possible that machine learning algorithms will evolve to better capture the principles of Hebbian learning and other forms of neural plasticity seen in biological brains. Some recent research directions in machine learning have begun to explore Hebbian-like mechanisms. For example, unsupervised learning techniques like contrastive learning, self-supervised learning, and reinforcement learning bear more resemblance to Hebbian learning principles than traditional supervised learning.

In contrastive learning, for instance, the network learns to differentiate between similar and dissimilar data points, a process somewhat analogous to Hebbian learning’s focus on strengthening connections between co-activated neurons. Similarly, reinforcement learning involves an agent interacting with an environment and updating its internal model based on reward signals, which can be seen as a more biologically plausible learning mechanism compared to backpropagation.

Another area of interest is neuromorphic computing, which seeks to develop hardware that mimics the architecture and functioning of the biological brain. Neuromorphic systems aim to replicate the principles of synaptic plasticity, including Hebbian learning, to create more energy-efficient and biologically plausible learning systems. These systems might offer new ways of incorporating Hebbian-like learning into machine learning models.

Integrating the Two Approaches: A Unified Learning Paradigm?

Ultimately, the differences between biological brains and artificial neural networks may not be as stark as they initially appear. It’s possible that the future of machine learning will integrate principles from both backpropagation and Hebbian learning, creating hybrid models that combine the mathematical rigor of artificial networks with the biological plausibility of Hebbian-like processes.

For instance, one could imagine a neural network that uses backpropagation for global optimization tasks while incorporating Hebbian learning for local adjustments at the level of individual neurons or layers. This hybrid approach might capture the best of both worlds, offering a more flexible and biologically inspired way of learning.

Another possibility is that biologically inspired learning mechanisms, such as predictive coding or neuromodulation, could be used to refine the learning process in ANNs. By incorporating error correction mechanisms that are more distributed and less reliant on global error signals, machine learning algorithms might become more efficient and biologically plausible.

Conclusion

The comparison between backpropagation in machine learning and Hebbian learning in biological brains opens up exciting new avenues for research and exploration. While the two systems currently operate under very different principles, there is potential for convergence in the future. The biological brain’s processes are likely more complex than backpropagation and Hebbian learning alone, but the study of both systems can inform one another, leading to advances in both neuroscience and machine learning.

A deeper understanding of the biological brain’s learning mechanisms — whether through the lens of predictive coding, neuromodulation, or other forms of error correction — may reveal processes that resemble a forward pass or even backpropagation in ways we have yet to fully grasp. Conversely, machine learning algorithms may evolve to incorporate Hebbian-like principles, leading to more robust, flexible, and biologically plausible models of learning. This interdisciplinary cross-pollination holds the key to unlocking the next generation of intelligent systems.