Feedback-gated weight modulation enables state-dependent computation in recurrent neural networks

Recurrent neural networks (RNNs) are useful models of sequential processing in cortical circuits but omit a defining feature of cortical organization: top-down feedback that dynamically regulates synaptic integration in lower order regions. In cortical pyramidal neurons, feedforward drive tends to arrive at proximal dendrites, whereas feedback carrying contextual and behaviorally relevant information often targets the distal apical dendrites. Nonlinear interactions between these streams can selectively reshape neuronal responses. To emulate this organization, we introduce a new RNN architecture, Gating on Weights with Feedback (GaWF), in which a standard RNN receives feedback gating signals computed from the subsequent layer's activations. These gates modulate individual weights on both the input and hidden weight matrices. This stands in contrast to gated RNN architectures, such as LSTMs and GRUs, which dynamically control information flow through gates computed on feedforward inputs and local hidden states that then act on unit-level representations. We evaluated GaWF on a new dynamic cluttered target-tracking task that requires simultaneous inference of target identity and location. Both the target and distractors could change dynamically. GaWF consistently outperformed conventional gated RNNs and other recurrent and state-space models, achieving higher held-out accuracy with less overfitting. Its gates produced sparse weight matrices that track the current target. Further mechanistic analyses revealed a functional division: input-weight gating predominantly tracked target location, whereas recurrent-weight gating represented target identity. Neither LSTMs nor GRUs segregated their gating signals based on the properties of the tracked target; instead, their gates remained largely undifferentiated, which may have contributed to their overfitting. These findings identify top-down connection-specific feedback gating as an effective and interpretable mechanism for context-dependent recurrent computation and highlight the functional importance of the interaction between hierarchical feedback in cortical circuits and dendritic computation.