Related papers: Recurrent networks improve neural response prediction and provide insights into underlying cortical circuits

Recurrent networks improve neural response prediction and provide insights into underlying cortical circuits

URL: http://arxiv.org/abs/2110.00825v2
Date: Sun, 13 Nov 2022 21:08:18 GMT
Title: Recurrent networks improve neural response prediction and provide insights into underlying cortical circuits
Authors: Yimeng Zhang, Harold Rockwell, Sicheng Dai, Ge Huang, Stephen Tsou, Yuanyuan Wei, Tai Sing Lee
Abstract summary: CNN models have proven themselves as state-of-the-art models for predicting single-neuron responses to natural images in early visual cortical neurons. We extend these models with recurrent convolutional layers, reflecting the well-known massive recurrence in the cortex. We find that the hidden units in the recurrent circuits of the appropriate models, when trained on long-duration wide-field image presentations, exhibit similar temporal response dynamics and classical contextual modulations as observed in V1 neurons.
Score: 3.340380180141713
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Feedforward CNN models have proven themselves in recent years as state-of-the-art models for predicting single-neuron responses to natural images in early visual cortical neurons. In this paper, we extend these models with recurrent convolutional layers, reflecting the well-known massive recurrence in the cortex, and show robust increases in predictive performance over feedforward models across thousands of hyperparameter combinations in three datasets of macaque V1 and V2 single-neuron responses. We propose the recurrent circuit can be conceptualized as a form of ensemble computing, with each iteration generating more effective feedforward paths of various path lengths to allow a combination of solutions in the final approximation. The statistics of the paths in the ensemble provide insights to the differential performance increases among our recurrent models. We also assess whether the recurrent circuits learned for neural response prediction can be related to cortical circuits. We find that the hidden units in the recurrent circuits of the appropriate models, when trained on long-duration wide-field image presentations, exhibit similar temporal response dynamics and classical contextual modulations as observed in V1 neurons. This work provides insights to the computational rationale of recurrent circuits and suggests that neural response prediction could be useful for characterizing the recurrent neural circuits in the visual cortex.

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