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Confidence and Second-Order Errors in Cortical Circuits: Neural Dynamics of Uncertainty Estimation


Core Concepts
Neural dynamics minimize prediction errors by integrating confidence in cortical circuits.
Abstract
Abstract: Neural dynamics minimize prediction errors by integrating confidence in cortical circuits. Introduction: Uncertainty in cortical processing benefits behavioral and neural data. Results: Energy formulation for cortical function, neuronal dynamics with confidence estimation, error-correcting synaptic learning, dynamic balancing of cortical streams, and second-order error propagation. Discussion: Theoretical framework for confidence estimation and second-order errors in cortical circuits. Materials and Methods: Derivation of energy, partial derivatives for neuronal and synaptic dynamics.
Stats
"Empirical studies on humans and other animals show that prior knowledge and data from multiple modalities are weighted by their relative uncertainty during perceptual integration [4], decision-making [5, 6] and sensorimotor control [7, 8]." "Potential implementations in the cerebral cortex have been discussed, notably in cortico-pulvinar loops [25] or more generally through neuromodulation [26, 27]." "In this work, we suppose that cortical areas must not only predict the activity in other areas and sensory streams but also jointly estimate the confidence of their predictions, where we define confidence as the (estimated) inverse expected uncertainty of the prediction." "At the equilibrium of neuronal dynamics, weights of synapses carrying predictions can be learned following the gradient." "Weights Aℓ of synapses carrying confidence can also be learned following the gradient."
Quotes
"In a forest, I know there are trees." "Across trees, structure (trunk, branches, leaves, etc.) is usually more consistent than color." "Having formulated an energy for cortical function, we formally derive gradient-based neuronal dynamics and synaptic learning rules minimizing this energy."

Key Insights Distilled From

by Arno Granier... at arxiv.org 03-28-2024

https://arxiv.org/pdf/2309.16046.pdf
Confidence and second-order errors in cortical circuits

Deeper Inquiries

How does the proposed neural dynamics model integrate uncertainty in cortical processing?

The proposed neural dynamics model integrates uncertainty in cortical processing by incorporating measures of confidence in the predictions made by cortical areas. In this model, cortical areas not only predict the activity in other areas and sensory streams but also estimate the confidence of their predictions. Confidence is defined as the inverse expected uncertainty of the prediction, and it is computed at each level of the cortical hierarchy as a function of current higher-level representations. This dynamic integration of confidence in the predictions allows for a more nuanced and context-dependent modulation of neural activity based on the level of certainty in the predictions being made.

What are the implications of second-order error propagation in cortical circuits?

Second-order error propagation in cortical circuits has significant implications for information processing and learning. In the proposed model, second-order errors compare confidence and actual performance, and these errors are propagated through the cortical hierarchy alongside classical prediction errors. By comparing confidence in predictions with the actual performance, cortical circuits can learn to adjust the weights of synapses responsible for formulating confidence. This mechanism allows for a more nuanced and adaptive learning process, where the cortex can refine its predictions based on the level of confidence in those predictions. Second-order error propagation provides a mechanism for cortical circuits to learn and adapt based on the reliability and uncertainty of their predictions.

How can the theoretical framework for confidence estimation be experimentally validated in cortical circuits?

The theoretical framework for confidence estimation in cortical circuits can be experimentally validated through a combination of electrophysiological recordings, optogenetic manipulations, and behavioral experiments. Electrophysiological Recordings: Researchers can record the activity of different types of neurons in the cortex while animals perform tasks that require confidence judgments. By analyzing the neural activity patterns, researchers can identify neural correlates of confidence estimation. Optogenetic Manipulations: Optogenetic techniques can be used to selectively manipulate the activity of specific neuron populations involved in confidence estimation. By modulating the activity of these neurons and observing the effects on behavior, researchers can gain insights into the causal role of these neurons in confidence processing. Behavioral Experiments: Behavioral experiments can be designed to test the predictions of the theoretical framework. For example, animals can be trained on tasks that require them to make decisions based on varying levels of confidence. By manipulating the experimental conditions and measuring behavioral outcomes, researchers can validate the predictions of the theoretical model. By combining these experimental approaches, researchers can provide empirical evidence for the role of confidence estimation in cortical circuits and validate the theoretical framework proposed in the study.
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