How does the structure of a neural circuit shape its function? @neuralreckoning.bsky.social & I explore this in our new preprint: doi.org/10.1101/2025... 🤖🧠🧪 🧵1/9

We start from an artificial neural network with 3 sets of units and 9 possible weight matrices (or pathways). By keeping the two feedforward pathways (W_ih, W_ho) and adding the other 7 in any combination, we can generate 2^7 distinct architectures. All 128 are shown in the post above. 🧵2/9

This allows us to interpolate between: Feedforward - with no additional pathways. Fully recurrent - with all nine pathways. We term the 126 architectures between these two extremes *partially recurrent neural networks* (pRNNs), as signal propagation can be bidirectional, yet sparse. 🧵3/9

To compare pRNN function, we introduce a set of multisensory navigation tasks we call *multimodal mazes*. In these tasks, we simulate networks as agents with noisy sensors, which provide local clues about the shortest path through each maze. We add complexity by removing cues or walls. 🧵4/9

We trained over 25,000 pRNNs on these tasks. And measured their: 📈 Fitness (task performance) 💹 Learning speed 📉 Robustness to various perturbations (e.g. increasing sensor noise) From these data, we reach three main conclusions. 🧵5/9

First, across tasks and functional metrics, many pRNN architectures perform as well as the fully recurrent architecture. Despite having less pathways and as few as ¼ the number of parameters. This shows that pRNNs are efficient, yet performant. 🧵6/9

Second, to isolate how each pathway changes network function, we compare pairs of circuits which differ by one pathway. Across pairs, we find that pathways have context dependent effects. E.g. here hidden-hidden connections decrease learning speed in one task but accelerate it in another. 🧵7/9