Mingze Li

Top-down feedback matters: Functional impact of brainlike connectivity motifs on audiovisual integration

Artificial neural networks (ANNs) are an important tool for studying neural computation, but many features of the brain are not captured by … (see more)standard ANN architectures. One notable missing feature in most ANN models is top-down feedback, i.e. projections from higher-order layers to lower-order layers in the network. Top-down feedback is ubiquitous in the brain, and it has a unique modulatory impact on activity in neocortical pyramidal neurons. However, we still do not understand its computational role. Here we develop a deep neural network model that captures the core functional properties of top-down feedback in the neocortex, allowing us to construct hierarchical recurrent ANN models that more closely reflect the architecture of the brain. We use this to explore the impact of different hierarchical recurrent architectures on an audiovisual integration task. We find that certain hierarchies, namely those that mimic the architecture of the human brain, impart ANN models with a light visual bias similar to that seen in humans. This bias does not impair performance on the audiovisual tasks. The results further suggest that different configurations of top-down feedback make otherwise identically connected models functionally distinct from each other, and from traditional feedforward-only models. Altogether our findings demonstrate that modulatory top-down feedback is a computationally relevant feature of biological brains, and that incorporating it into ANNs can affect their behavior and helps to determine the solutions that the network can discover.

2025-01-08

bioRxiv (preprint)

doi.org

Top-down feedback matters: Functional impact of brainlike connectivity motifs on audiovisual integration

2024-10-03

bioRxiv (preprint)

doi.org

Top-down feedback matters: Functional impact of brainlike connectivity motifs on audiovisual integration

Mashbayar Tugsbayar

Mingze Li

Eilif B Muller

Blake Richards

Artificial neural networks (ANNs) are an important tool for studying neural computation, but many features of the brain are not captured by … (see more)standard ANN architectures. One notable missing feature in most ANN models is top-down feedback, i.e. projections from higher-order layers to lower-order layers in the network. Top-down feedback is ubiquitous in the brain, and it has a unique modulatory impact on activity in neocortical pyramidal neurons. However, we still do not understand its computational role. Here we develop a deep neural network model that captures the core functional properties of top-down feedback in the neocortex, allowing us to construct hierarchical recurrent ANN models that more closely reflect the architecture of the brain. We use this to explore the impact of different hierarchical recurrent architectures on an audiovisual integration task. We find that certain hierarchies, namely those that mimic the architecture of the human brain, impart ANN models with a light visual bias similar to that seen in humans. This bias does not impair performance on the audiovisual tasks. The results further suggest that different configurations of top-down feedback make otherwise identically connected models functionally distinct from each other, and from traditional feedforward and laterally recurrent models. Altogether our findings demonstrate that modulatory top-down feedback is a computationally relevant feature of biological brains, and that incorporating it into ANNs affects their behavior and constrains the solutions it’s likely to discover.

2024-10-02

bioRxiv (preprint)

doi.org

Connectome-based reservoir computing with the conn2res toolbox

Laura E. Suárez

Agoston Mihalik

Filip Milisav

Kenji Marshall

Mingze Li

Petra E. Vértes

Guillaume Lajoie

Bratislav Mišić