Learning Synaptic Plasticity Rules to Embed Stochastic Dynamics of the Environment in Spontaneous Neural Activity

Core Concepts

The brain learns an internal model of the environment through sensory experiences, which is reflected in the statistical properties of spontaneous neural activity. This study proposes biologically plausible synaptic plasticity rules that allow a recurrent spiking neural network to learn and spontaneously replay the stochastic dynamics of sensory inputs.

Abstract

The study investigates how the brain can learn an internal model of the environment through sensory experiences, which is reflected in the statistical properties of spontaneous neural activity. The authors propose a computational model of a recurrent spiking neural network with distinct excitatory and inhibitory populations.
Key highlights:

The model uses different plasticity rules for excitatory and inhibitory synapses. Excitatory synapses are modified to minimize the discrepancy between stimulus-evoked and internally predicted activity, while inhibitory synapses maintain the excitatory-inhibitory balance.
After learning, the network exhibits spontaneous stochastic transitions between cell assemblies, where the transition statistics closely match those of the evoked dynamics.
The learned excitatory synaptic weights encode the transition probabilities between the evoked patterns, while inhibitory plasticity is crucial for generating structured spontaneous activity.
The model can adapt to changes in the transition structure of the external inputs, demonstrating flexibility in learning internal models.
The model is able to learn complex stochastic sequences and reproduce experimental findings on the relationship between transition uncertainty and the predictability of neural responses in songbirds.

The proposed plasticity rules allow the network to learn the statistical structure of sensory experiences and generate spontaneous activity that reflects the learned internal model of the environment.

Stats

The network model consists of NE excitatory and NI inhibitory neurons.
The initial value of synaptic weights Wab were uniformly set to Wee = 0.1 and Wei = -0.1.

Quotes

"Our results show that the prediction-based plasticity rule allows the model to learn and spontaneously replays the transition statistics of evoked patterns."
"Consistent with this bias between transition probabilities, we found that assembly 3 was driven much strongly than assembly 2."
"Comparison between transition probabilities of stimulus patterns and that of the reactivated assemblies revealed a clear alignment of temporal statistics."

Key Insights Distilled From

Embedding stochastic dynamics of the environment in spontaneous activity by prediction-based plasticity

by Asabuki,T., ... at www.biorxiv.org 05-01-2023

https://www.biorxiv.org/content/10.1101/2023.05.01.538909v1

Deeper Inquiries

How can the proposed plasticity rules be extended to learn non-Markovian or hierarchical transition structures observed in animal behavior?

To extend the proposed plasticity rules to learn non-Markovian or hierarchical transition structures, modifications would be necessary to account for the temporal dependencies and complex relationships present in such structures. One approach could involve incorporating working memory mechanisms into the model to capture the history of previous states and transitions. By introducing a memory component that retains information about past events, the network could learn and predict non-Markovian transitions more effectively. Additionally, hierarchical structures could be learned by organizing the synaptic connections in layers that represent different levels of abstraction or complexity in the transition sequences. This hierarchical organization would allow the network to capture the nested relationships between states and transitions, enabling the learning of more intricate and multi-level structures observed in animal behavior.

What are the potential functional benefits of learning stochastic internal models compared to deterministic ones, and how could this influence higher-level cognitive processes?

Learning stochastic internal models offers several functional benefits compared to deterministic models. One key advantage is the ability to capture the uncertainty and variability inherent in real-world environments. Stochastic models can generate a range of possible outcomes, allowing for more flexible and adaptive responses to unpredictable situations. This variability can be beneficial in decision-making processes, as it enables the brain to consider multiple potential scenarios and outcomes. Additionally, stochastic models can better represent the probabilistic nature of sensory inputs and events, leading to more realistic and nuanced internal representations of the environment. This enhanced representation can improve the brain's predictive capabilities and decision-making processes, ultimately supporting higher-level cognitive functions such as planning, problem-solving, and adaptive behavior.

Could the interplay between excitatory and inhibitory plasticity mechanisms revealed in this study provide insights into the neural basis of working memory and its role in building internal models of the environment?

The interplay between excitatory and inhibitory plasticity mechanisms uncovered in this study could indeed offer insights into the neural basis of working memory and its role in constructing internal models of the environment. Working memory is essential for temporarily storing and manipulating information to guide behavior and decision-making. The balance between excitatory and inhibitory synaptic connections is crucial for maintaining stable and flexible representations in working memory. In the context of building internal models, this balance ensures that relevant information is retained while irrelevant or outdated information is suppressed. The proposed plasticity rules, which focus on minimizing prediction errors and maintaining excitation-inhibition balance, align with the principles of working memory function. By regulating the strength and connectivity of synapses based on predictive accuracy and balance, the brain can effectively encode and update internal models of the environment, supporting cognitive processes such as learning, memory, and decision-making.