Geometry and Dynamics of Neuronal Representations in a Precisely Balanced Memory Network Modeling Olfactory Cortex

Kernekoncepter

Autoassociative memory networks with precisely balanced excitation and inhibition transform the geometry of neuronal representations, confining activity onto continuous manifolds that support pattern classification without discrete attractor dynamics.

Resumé

The study created a spiking network model of the zebrafish olfactory cortex area Dp, which is homologous to the mammalian piriform cortex. The model, pDpsim, consisted of excitatory (E) and inhibitory (I) neurons and received input from the olfactory bulb.

Key findings:

1. Networks with global inhibition exhibited discrete attractor dynamics and pattern completion, but unrealistic firing rate distributions.
2. Introducing E/I assemblies, where I neurons track the activity of E neurons, established precise synaptic balance and stabilized firing rates. These networks did not show discrete attractor states, but transformed the geometry of neuronal representations.
3. Activity in networks with E/I assemblies was locally constrained onto continuous manifolds that represented learned and related inputs. The covariance structure of these manifolds supported pattern classification, particularly for learned inputs.
4. The geometric transformations by E/I assemblies enhanced the discriminability between representations of learned and novel inputs without disrupting the continuity of the coding space. This may enable fast pattern classification, continual learning, and higher-order cognitive computations.
5. The model makes testable predictions about the geometry and dimensionality of odor representations in the olfactory cortex.

Statistik

"The mean firing rate was <0.1 Hz in the absence of stimulation and increased to ~1 Hz during odor presentation."
"The synaptic conductance during odor presentation substantially exceeded the resting conductance and inputs from other E neurons contributed >80% of the excitatory synaptic conductance."
"Shuffling spike times of inhibitory neurons resulted in runaway activity with a probability of ~80%, demonstrating that activity was indeed inhibition-stabilized."

Citater

"Unlike classical memory models, networks with E/I assemblies did not show discrete attractor dynamics. Rather, responses to learned inputs were locally constrained onto manifolds that "focused" activity into neuronal subspaces."
"The covariance structure of these manifolds supported pattern classification when information was retrieved from selected neuronal subsets."
"Networks with E/I assemblies therefore transformed the geometry of neuronal coding space, resulting in continuous representations that reflected both relatedness of inputs and an individual's experience."

Vigtigste indsigter udtrukket fra

Geometry and dynamics of representations in a precisely balanced memory network related to olfactory cortex

by Meissner-Ber... kl. www.biorxiv.org 12-13-2023

https://www.biorxiv.org/content/10.1101/2023.12.12.571272v2

Dybere Forespørgsler

How could the readout mechanisms be further optimized to efficiently extract information from the activity manifolds in networks with E/I assemblies

To optimize readout mechanisms for efficiently extracting information from the activity manifolds in networks with E/I assemblies, several strategies can be considered:

Selective Neuronal Sampling: Instead of randomly selecting neurons for readout, prioritizing assembly neurons or neurons with high information content can enhance the efficiency of the readout process. By focusing on neurons that are part of the E/I assemblies, which are known to contain more informative activity patterns, the readout mechanism can extract relevant information more effectively.

Dimensionality Reduction Techniques: Implementing dimensionality reduction algorithms such as Principal Component Analysis (PCA) or Independent Component Analysis (ICA) can help in identifying the most informative dimensions of the activity space. By reducing the dimensionality of the data while preserving the essential information, the readout mechanism can operate more efficiently on the transformed data.

Machine Learning Algorithms: Utilizing machine learning algorithms like Support Vector Machines (SVM) or Neural Networks for readout can improve the classification accuracy and speed of information extraction. These algorithms can learn the complex relationships between the activity patterns and the learned inputs, enabling more accurate and rapid classification.

Feedback Mechanisms: Incorporating feedback mechanisms that adjust the readout strategy based on the performance of previous classifications can enhance the adaptability and effectiveness of the readout process. By iteratively refining the readout based on feedback, the system can continuously improve its classification accuracy.

Sparse Coding Techniques: Implementing sparse coding methods can help in identifying the most relevant features or neurons for readout. By promoting sparsity in the representation of the activity patterns, the readout mechanism can focus on the most discriminative aspects of the data, leading to more efficient information extraction.

By integrating these strategies into the readout mechanisms of networks with E/I assemblies, the system can optimize the extraction of information from the activity manifolds, leading to improved performance in pattern classification and cognitive tasks.

What are the potential limitations of the continuous representations generated by E/I assemblies compared to discrete attractor dynamics for specific cognitive functions

Continuous representations generated by E/I assemblies have certain limitations compared to discrete attractor dynamics for specific cognitive functions:

Pattern Separability: Continuous representations may struggle with separating closely related patterns, especially when the boundaries between different patterns are not well-defined. Discrete attractor dynamics, on the other hand, can create clear boundaries between stored patterns, facilitating pattern separation and classification tasks.

Pattern Completion: Continuous representations may not exhibit robust pattern completion capabilities, where noisy or partial inputs can be accurately classified or completed to match learned patterns. Discrete attractor networks excel at pattern completion by converging to stable attractor states corresponding to learned patterns.

Memory Stability: Continuous representations may be more susceptible to interference or overlap between memories, leading to potential degradation of stored information over time. Discrete attractor networks can maintain stable memory representations without significant interference between stored patterns.

Computational Efficiency: Continuous representations may require more computational resources and complex algorithms for pattern classification and retrieval compared to discrete attractor dynamics, which can perform these tasks efficiently with simple mechanisms.

Generalization: Discrete attractor dynamics may offer better generalization capabilities, allowing the network to classify novel inputs based on similarities to learned patterns. Continuous representations may struggle with generalization, especially in cases where the input patterns are not explicitly represented in the activity space.

While continuous representations have their advantages in terms of flexibility and adaptability, they may not be as well-suited as discrete attractor dynamics for tasks that require precise pattern separation, robust pattern completion, and stable memory storage.

How could the insights from this computational model inform our understanding of the role of inhibition in shaping neuronal representations across different brain regions

The insights from this computational model can provide valuable information on the role of inhibition in shaping neuronal representations across different brain regions:

Local Manifold Formation: The model demonstrates how inhibition, particularly in the form of E/I assemblies, can lead to the formation of local manifolds in the coding space. This highlights the importance of inhibition in constraining and organizing neuronal activity patterns, which may be a common mechanism across various brain regions.

Information Encoding: The model suggests that inhibition plays a crucial role in encoding and storing information in the geometry of neuronal manifolds. By maintaining precise balance between excitation and inhibition, the network can represent learned inputs in a continuous and structured manner, emphasizing the significance of inhibition in information processing.

Pattern Classification: The model shows that inhibition within E/I assemblies can enhance pattern classification by creating distinct activity manifolds for learned inputs. This indicates that inhibition is essential for discriminating between different input patterns and facilitating cognitive computations based on stored memories.

Memory Stability: The role of inhibition in stabilizing firing rate distributions and preventing network instabilities during continual learning underscores its importance in maintaining memory stability. Inhibition within E/I assemblies ensures that memories are stored and retrieved efficiently without interference or degradation over time.

Comparative Analysis: By comparing the performance of networks with E/I assemblies to those without, the model highlights the unique contributions of inhibition in shaping neuronal representations and supporting cognitive functions. This comparative analysis can provide insights into the specific mechanisms through which inhibition influences information processing in different brain regions.