Is Biological Neural Networks Learning Based on Stochastic Gradient Descent? A Stochastic Process Analysis

핵심 개념

Stochastic gradient descent may indeed play a role in optimizing biological neural networks, even though the learning process relies only on local information.

초록

The paper analyzes a stochastic model for supervised learning in biological neural networks (BNNs). It starts by reviewing the Schmidt-Hieber model, which shows that the local updating rule in BNNs corresponds to a zero-order optimization procedure on average.

The authors then propose a modification to the model, where each learning opportunity triggers a large number of spikes and parameter updates, rather than just one. With this change, the authors show that the updates approximately correspond to a continuous gradient descent step. This suggests that stochastic gradient descent may indeed be a plausible mechanism for learning in BNNs, even though the learning process relies only on local information and does not explicitly compute gradients.

The key insights are:

• The original Schmidt-Hieber model shows that the local updates in BNNs correspond to a zero-order optimization method on average, which has slow convergence.
• By assuming many updates per learning opportunity, the authors show that the updates approximate a continuous gradient descent step.
• This indicates that stochastic gradient descent may play a role in optimizing BNNs, even without explicitly computing gradients.
• The randomness in the updates is a crucial component, as it allows the system to converge to the gradient descent dynamics.

통계

None.

인용구

None.

핵심 통찰 요약

Is Learning in Biological Neural Networks based on Stochastic Gradient Descent? An analysis using stochastic processes

by Söre... 게시일 arxiv.org 04-11-2024

https://arxiv.org/pdf/2309.05102.pdf

더 깊은 질문

How might the proposed mechanism of many small updates per learning opportunity be implemented and regulated in biological neural networks?

The proposed mechanism of many small updates per learning opportunity in biological neural networks can be implemented through spike-timing-dependent plasticity (STDP). STDP is a biological mechanism that adjusts the synaptic weights between neurons based on the timing of their spikes. By repeatedly updating the weights in response to the timing of spikes, the network can gradually optimize its connections. This process mimics the concept of making many small updates per learning opportunity, similar to stochastic gradient descent in artificial neural networks.
To regulate this mechanism in biological neural networks, the brain can utilize various feedback loops and neuromodulatory systems. For example, neurotransmitters like dopamine can play a crucial role in reinforcing or weakening synaptic connections based on the success of learning tasks. Additionally, the brain can employ homeostatic mechanisms to maintain a balance between synaptic potentiation and depression, ensuring stable and efficient learning over time. By integrating these regulatory mechanisms with STDP, biological neural networks can adapt and learn in a dynamic and controlled manner.

What are potential limitations or drawbacks of the stochastic gradient descent-like optimization in biological neural networks compared to artificial neural networks?

While the stochastic gradient descent-like optimization mechanism in biological neural networks shows promise, it also comes with certain limitations and drawbacks compared to artificial neural networks. One significant limitation is the inherent noise and variability present in biological systems. The randomness associated with spike timings and synaptic plasticity can introduce fluctuations in the learning process, leading to slower convergence and potentially less stable optimization compared to the deterministic nature of artificial neural networks.
Another drawback is the complexity of biological neural networks compared to artificial ones. The intricate interplay of various neurotransmitters, neuromodulators, and feedback loops in the brain adds layers of complexity to the learning process. This complexity can make it challenging to model and optimize learning algorithms based on stochastic gradient descent in biological systems.
Furthermore, the energy efficiency and computational speed of biological neural networks may not match the performance of artificial neural networks optimized for specific tasks. The biological constraints of neural networks, such as limited resources and biological noise, can hinder the scalability and efficiency of stochastic gradient descent-like optimization in biological systems.

What other biological mechanisms or principles, beyond stochastic gradient descent, might contribute to efficient learning in biological neural networks?

In addition to stochastic gradient descent-like optimization, several other biological mechanisms and principles contribute to efficient learning in biological neural networks. One such mechanism is Hebbian learning, which states that neurons that fire together wire together. By strengthening synaptic connections between neurons that are frequently activated together, Hebbian learning facilitates associative memory and pattern recognition in the brain.
Another crucial principle is homeostasis, which maintains the stability and balance of neural activity in the face of changing inputs and demands. Homeostatic mechanisms regulate the excitability of neurons and the strength of synaptic connections, ensuring that the network remains adaptable while preventing runaway excitation or inhibition.
Neuromodulation, the process by which neurotransmitters and hormones modulate neural activity, also plays a vital role in learning and plasticity. Neuromodulators like dopamine, serotonin, and acetylcholine can influence synaptic plasticity, attention, motivation, and reinforcement learning, shaping the overall learning process in biological neural networks.
Moreover, structural plasticity, the ability of neural networks to reorganize their physical connections, and metaplasticity, the plasticity of synaptic plasticity, are essential mechanisms that contribute to efficient learning and adaptation in biological systems. These mechanisms work in concert with stochastic gradient descent-like optimization to enable robust and flexible learning in biological neural networks.