Biology subjects

Fiete, I.

Publications and source records attributed to Fiete, I..

21 records · Page 2Linked to original sources

bioRxiv2018

Sources of path integration error in young and aging humans

Path integration is a vital function in navigation: it enables the continuous tracking of ones position in space by integrating self-motion cues. Path integration abilities vary across individuals but tend to deteriorate in old age. The specific causes of path integration errors, however, remain poorly characterized. Here, we combined tests of path integration performance with a novel analysis based on the Langevin diffusion equation, which allowed us to decompose errors into distinct causes that can corrupt path integration computations. Across age groups, the dominant errors were due to noise and a bias in speed estimation. Noise-driven errors accumulated with travel distance not elapsed time, suggesting that the dominant noise originates in the velocity input rather than within the integrator. Age-related declines were traced primarily to a growth in this unbiased noise. Together, these findings shed light on the contributors to path integration error and the mechanisms underlying age-related navigational deficits.

neuroscience↗

bioRxiv2017

How fast is neural winner-take-all when deciding between many options?

Identifying the maximal element (max, argmax) in a set is a core computational element in inference, decision making, optimization, action selection, consensus, and foraging. We show that running sequentially through a list of N fluctuating items takes Nlog(N) time to accurately find the max, prohibitively slow for large N. The power of computation in the brain is ascribed to its parallelism, yet it is theoretically unclear whether, even on an elemental task like the max operation, leaky and noisy neurons can perform a distributed computation that cuts the required time by a factor of N, a benchmark for parallel computation. We show that conventional winner-take-all circuit models fail to realize the parallelism benchmark and worse, in the presence of noise altogether fail to produce a winner when N is large. If, however, neurons are equipped with a second nonlinearity so that weakly active neurons cannot contribute inhibition to the circuit, the network matches the accuracy of the serial strategy but does so N times faster, partially self-adjusting integration time for task difficulty and number of options and saturating the parallelism benchmark without parameter fine-tuning. Finally, in the regime of few choices (small N), the same circuit predicts Hick's law of decision making; thus Hick's law behavior is a symptom of efficient parallel computation. Our work shows that distributed computation that saturates the parallelism benchmark is possible in networks of noisy and finite-memory neurons.

neuroscience↗

bioRxiv2017

Emergence of dynamically reconfigurable hippocampal responses by learning to perform probabilistic spatial reasoning

Navigation in natural environments is computationally difficult: Location errors from motion estimation noise accumulate over time, while landmarks can be spatially extended and often look alike, thus providing ambiguous data. The brain contains a number of spatially tuned neurons coding for various navigational variables, but current models do not explain how these circuits could implement navigational computations that involve non-trivial spatial reasoning. We show, using a function-first approach, that neural circuits trained to efficiently solve spatial reasoning problems with performance on par with sequential probabilistic strategies reproduce some key properties of hippocampal coding, including heterogeneous tuning, conjunctive tuning, and low-dimensional dynamics. In addition, the models predict the emergence of tuning to key latent variables that are neither present in the input data nor trained as the end result of the task, and exhibit a spontaneous dynamical reconfiguration of tuning across time during a task as the computational demands evolve, reminiscent of some of the more complex dynamics observed in the hippocampus including a switch between location and displacement coding modes. These results provide a new functional framework for understanding the rich phenomenology and potential capabilities of navigation codes in the hippocampus and associated brain areas.

neuroscience↗