Homeostatic Synaptic Scaling Optimizes Learning in Neural Population Code Models

Core Concepts

Homeostatic synaptic normalization during the learning of sparse random projection models results in more accurate, efficient, and biologically-plausible models of neural population codes.

Abstract

The content presents a new family of statistical models for large neural populations that is based on sparse and random non-linear projections of the population activity. These "Reshaped Random Projections" (Reshaped RP) models are more accurate and efficient than the previously proposed Random Projections (RP) models. Key highlights: Reshaping the randomly selected sparse non-linear projections, rather than just tuning the weights of these projections, results in more accurate and efficient models. Incorporating homeostatic synaptic normalization during the reshaping process further improves the performance of the models. The homeostatic models exhibit lower firing rates and correlations between the projection neurons, making them more energy-efficient. The homeostatic models are robust to the specific connectivity structure of the initial random projections, suggesting that the brain may not need to know the optimal circuit connectivity to learn efficient population code models. The homeostatic synaptic normalization regulates the firing rates of the projection neurons, providing a computational benefit in addition to the commonly attributed role of maintaining neuronal homeostasis. Overall, the findings suggest that homeostatic synaptic scaling can play a dual role in neural circuits: maintaining spiking and synaptic homeostasis while concurrently optimizing network performance and efficiency in encoding information and learning.

Stats

"The mean log-likelihood of the models is shown as a function of the total available synaptic budget of the different models." "The mean log-likelihood of the models is shown as a function of the total used budget." "The mean correlation between the activity of the projection neurons, shown as a function of the model cost." "The firing rates of the projection neurons, shown as a function of the model cost."

Quotes

"Reshaping of the projections gave even more accurate and efficient models in terms of synaptic weights of the neural circuit that implements the model, and was optimal for random and sparse initial connectivity, surpassing fully connected network models." "The homeostatic synaptic normalization regulates the firing rates of the projection neurons, providing a computational benefit in addition to the commonly attributed role of maintaining neuronal homeostasis."

Key Insights Distilled From

Homeostatic synaptic normalization optimizes learning in network models of neural population codes

by Mayzel,J., S... at www.biorxiv.org 03-05-2023

https://www.biorxiv.org/content/10.1101/2023.03.05.530392v2

Deeper Inquiries

How could the homeostatic synaptic normalization mechanisms be implemented in real biological neural circuits?

The implementation of homeostatic synaptic normalization mechanisms in real biological neural circuits involves regulating the strength of synaptic connections to maintain stability and balance in neural activity. One way this could be achieved is through feedback mechanisms that monitor the firing rates of neurons and adjust synaptic weights accordingly. For example, if a neuron's firing rate is too high, the synaptic weights connected to that neuron could be weakened to bring the activity back to a more optimal level. Conversely, if a neuron's firing rate is too low, the synaptic weights could be strengthened to increase activity. Additionally, homeostatic mechanisms could involve global rules that govern the overall balance of synaptic strengths within a neural circuit. For instance, setting a limit on the total synaptic weight incoming to each neuron or ensuring that the total synaptic weight of the entire circuit remains constant could help regulate firing rates across the network. These mechanisms could be implemented through complex signaling pathways and molecular processes that sense and respond to changes in neural activity.

What are the potential drawbacks or limitations of the homeostatic reshaping approach compared to other neural network optimization techniques?

While homeostatic reshaping offers benefits in terms of efficiency, accuracy, and biological plausibility, there are also potential drawbacks and limitations to consider. One limitation is the complexity of implementing and maintaining homeostatic mechanisms in neural circuits. The intricate feedback loops and regulatory processes required for synaptic normalization may introduce computational overhead and resource demands that could impact the overall performance of the network. Another drawback is the potential for homeostatic reshaping to converge to suboptimal solutions or get stuck in local minima during the learning process. The non-convex nature of the optimization problem in reshaping random projections models may lead to challenges in finding the global optimum, especially in large-scale neural networks. Furthermore, the specific constraints and rules imposed by homeostatic reshaping may limit the flexibility and adaptability of the neural network. While these constraints can promote stability and robustness, they may also restrict the network's ability to learn and adapt to new information or changing environments.

Could the insights from this work on efficient population code models be applied to improve learning in artificial neural networks beyond just the homeostatic normalization aspect?

Yes, the insights from efficient population code models, such as the Reshaped Random Projections models, can be applied to enhance learning in artificial neural networks in various ways beyond just the homeostatic normalization aspect. One application could be in optimizing the structure and connectivity of neural networks to improve efficiency and performance. By incorporating sparse and random projections, similar to the Reshaped RP models, artificial neural networks could achieve higher accuracy with fewer parameters, reducing computational complexity and memory requirements. Additionally, the concept of reshaping projections based on learning could inspire new optimization techniques for training artificial neural networks. By dynamically adjusting the connections between neurons during the learning process, networks could adapt more effectively to complex data patterns and improve generalization capabilities. Furthermore, the idea of incorporating biological features, such as synaptic normalization, into artificial neural networks could lead to more biologically plausible and efficient learning algorithms. By mimicking the regulatory mechanisms found in real neural circuits, artificial networks could exhibit improved stability, robustness, and adaptability in response to changing inputs and environments.