RT Journal Article
SR Electronic
T1 Robust parallel decision-making in neural circuits with nonlinear inhibition
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 231753
DO 10.1101/231753
A1 Kriener, Birgit
A1 Chaudhuri, Rishidev
A1 Fiete, Ila R.
YR 2019
UL http://biorxiv.org/content/early/2019/01/09/231753.abstract
AB Identifying the maximal element (max,argmax) in a set is a core computational element in inference, decision making, optimization, action selection, consensus, and foraging. Running sequentially through a list of N fluctuating items takes N log(N) time to accurately find the max, prohibitively slow for large N. The power of computation in the brain is ascribed in part to its parallelism, yet it is theoretically unclear whether leaky and noisy neurons can perform a distributed computation that cuts the required time of a serial computation by a factor of N, a benchmark for parallel computation. We show that conventional winner-take-all neural networks fail the parallelism benchmark and in the presence of noise altogether fail to produce a winner when N is large. We introduce the nWTA network, in which neurons are equipped with a second nonlinearity that prevents weakly active neurons from contributing inhibition. Without parameter fine-tuning or re-scaling as the number of options N varies, the nWTA network converges N times faster than the serial strategy at equal accuracy, saturating the parallelism benchmark. The nWTA network self-adjusts integration time with task difficulty to maintain fixed accuracy without parameter change. Finally, the circuit generically exhibits Hick's law for decision speed. Our work establishes that distributed computation that saturates the parallelism benchmark is possible in networks of noisy, finite-memory neurons.