Recurrent Segmentation for Variable Computational Budgets

State-of-the-art systems for semantic image segmentation use feed-forward pipelines with fixed computational costs. Building an image segmentation system that works across a range of computational budgets is challenging and time-intensive as new architectures must be designed and trained for every computational setting. To address this problem we develop a recurrent neural network that successively improves prediction quality with each iteration. Importantly, the RNN may be deployed across a range of computational budgets by merely running the model for a variable number of iterations. We find that this architecture is uniquely suited for efficiently segmenting videos. By exploiting the segmentation of past frames, the RNN can perform video segmentation at similar quality but reduced computational cost compared to state-of-the-art image segmentation methods. When applied to static images in the PASCAL VOC 2012 and Cityscapes segmentation datasets, the RNN traces out a speed-accuracy curve that saturates near the performance of state-of-the-art segmentation methods

Complexity without chaos: Plasticity within random recurrent networks generates robust timing and motor control

It is widely accepted that the complex dynamics characteristic of recurrent neural circuits contributes in a fundamental manner to brain function. Progress has been slow in understanding and exploiting the computational power of recurrent dynamics for two main reasons: nonlinear recurrent networks often exhibit chaotic behavior and most known learning rules do not work in robust fashion in recurrent networks. Here we address both these problems by demonstrating how random recurrent networks (RRN) that initially exhibit chaotic dynamics can be tuned through a supervised learning rule to generate locally stable neural patterns of activity that are both complex and robust to noise. The outcome is a novel neural network regime that exhibits both transiently stable and chaotic trajectories. We further show that the recurrent learning rule dramatically increases the ability of RRNs to generate complex spatiotemporal motor patterns, and accounts for recent experimental data showing a decrease in neural variability in response to stimulus onset

Cortical Variability and Challenges for Modeling Approaches.

The functional role of the observed neuronal variability (the disparity in neural responses across multiple instances of the same experiment) is again receiving close attention in Computational and Systems Neuroscience (e.g., Durstewitz et al., 2010; Moreno-Bote et al., 2011; Oram, 2011; Beck et al., 2012; Churchland and Abbott, 2012; Brunton et al., 2013; Masquelier, 2013; Mattia et al., 2013; Balaguer-Ballester et al., 2014; Renart and Machens, 2014; Bujan et al., 2015; Lin et al., 2015; Pachitariu et al., 2015; Arandia-Romero et al., 2016; Doiron et al., 2016; McDonnell et al., 2016). Special consideration is currently given to understanding how spiking (Bujan et al., 2015; Deneve and Machens, 2016; Doiron et al., 2016; Hartmann et al., 2016; Landau et al., 2016) and phenomenological (Goris et al., 2014; Lin et al., 2015; Mochol et al., 2015; Arandia-Romero et al., 2016; Doiron et al., 2016) models account for the wide range of classical and new phenomena associated with trial-to-trial uncorrelated activity. Specifically, it has often been proposed that a network state characterized by largely asynchronous spike times whilst maintaining slow oscillations in the firing-rates, may represent the default spontaneous cortical mode (e.g., Sanchez-Vives and Mattia, 2014; Deneve and Machens, 2016; Sancristobal et al., 2016); and similar states could also underlie observed stimulus-driven variability in rate (Litwin Kumar and Doiron, 2012; Deneve and Machens, 2016; Hartmann et al., 2016). However, the way in which such a computationally advantageous network state for neural coding is achieved can differ substantially between modeling approaches; this challenge will be the focus of this manuscript

Towards learning inverse kinematics with a neural network based tracking controller

Learning an inverse kinematic model of a robot is a well studied subject. However, achieving this without information about the geometric characteristics of the robot is less investigated. In this work, a novel control approach is presented based on a recurrent neural network. Without any prior knowledge about the robot, this control strategy learns to control the iCub’s robot arm online by solving the inverse kinematic problem in its control region. Because of its exploration strategy the robot starts to learn by generating and observing random motor behavior. The modulation and generalization capabilities of this approach are investigated as well

Are task representations gated in macaque prefrontal cortex?

A recent paper (Flesch et al, 2022) describes behavioural and neural data suggesting that task representations are gated in the prefrontal cortex in both humans and macaques. This short note proposes an alternative explanation for the reported results from the macaque data