Latent computing by biological neural networks: A dynamical systems framework (2502.14337v1)

Abstract: Although individual neurons and neural populations exhibit the phenomenon of representational drift, perceptual and behavioral outputs of many neural circuits can remain stable across time scales over which representational drift is substantial. These observations motivate a dynamical systems framework for neural network activity that focuses on the concept of \emph{latent processing units,} core elements for robust coding and computation embedded in collective neural dynamics. Our theoretical treatment of these latent processing units yields five key attributes of computing through neural network dynamics. First, neural computations that are low-dimensional can nevertheless generate high-dimensional neural dynamics. Second, the manifolds defined by neural dynamical trajectories exhibit an inherent coding redundancy as a direct consequence of the universal computing capabilities of the underlying dynamical system. Third, linear readouts or decoders of neural population activity can suffice to optimally subserve downstream circuits controlling behavioral outputs. Fourth, whereas recordings from thousands of neurons may suffice for near optimal decoding from instantaneous neural activity patterns, experimental access to millions of neurons may be necessary to predict neural ensemble dynamical trajectories across timescales of seconds. Fifth, despite the variable activity of single cells, neural networks can maintain stable representations of the variables computed by the latent processing units, thereby making computations robust to representational drift. Overall, our framework for latent computation provides an analytic description and empirically testable predictions regarding how large systems of neurons perform robust computations via their collective dynamics.

Analyzing Latent Computing in Biological Neural Networks: A Dynamical Systems Approach

The paper "Latent computing by biological neural networks: A dynamical systems framework" presents a comprehensive exploration into how biological neural networks execute complex computations despite continuous changes in neuronal activity, a phenomenon known as representational drift. The authors propose a theoretical framework rooted in dynamical systems theory to understand and predict the stable computational dynamics within neural networks, focusing on what they term "latent processing units" (LPUs).

Framework Overview

The framework articulates that although individual neurons may exhibit unstable and changing coding properties over time, low-dimensional latent variables—or LPUs—embedded within these networks can remain stable and robust, providing a mechanism for consistent computational output. The authors describe these LPUs as low-dimensional dynamical systems embedded within high-dimensional neural activities through a theoretical construction that involves both encoding and embedding maps.

Key Attributes of the Framework

1. Dimensionality of Neural Dynamics: Neural networks, while generating high-dimensional activity patterns, operate on low-dimensional computational processes. This feature highlights coding efficiency embedded in the high-dimensional neural dynamics.
2. Coding Redundancy: The framework specifies that the manifolds formed by neural dynamical trajectories inherently exhibit coding redundancy, a consequence of the universal computing capabilities of underlying neural dynamics.
3. Sufficiency of Linear Readouts: One striking claim is that linear decoders can optimally subserve downstream circuits, maintaining the capability to trigger required behavioral outputs.
4. Scalability with Neuron Count: While recordings from thousands of neurons might be adequate for optimal instantaneous neural decoding, predicting neural ensemble trajectories over longer timescales might necessitate recordings from millions of neurons.
5. Robustness to Representational Drift: The authors propose that despite the individual variability in neuronal activity, LPUs offer stable representations of the variables being computed, thus demonstrating robustness against representational drift.

Implications and Future Directions

This framework has significant implications for both theoretical neuroscience and the practical development of artificial intelligence. Theoretically, it offers a robust description of how biological neural networks achieve consistent computational performance despite inherent instability in their components. Practically, understanding and leveraging the principles underlying LPUs could steer advances in neural network architectures used in AI, particularly in creating models that can adapt and remain stable over time.

Further research could delve into refining the framework to align more intricately with empirical observations, potentially leading to empirical tests that examine the existence and properties of LPUs under diverse experimental conditions. Additionally, the theoretical insights gained from this framework could inspire the design of new algorithms that embody the robust, efficient, and adaptive properties of biological neural networks.

In conclusion, the latent computation framework offers a compelling window into how biological neural networks sustain robust computations, challenging traditional views on neural encoding and computation, and laying the groundwork for future explorations into biologically inspired computational models.