2,314 research outputs found
Optimal Population Coding, Revisited
Cortical circuits perform the computations underlying rapid perceptual decisions within a few dozen milliseconds with each neuron emitting only a few spikes. Under these conditions, the theoretical analysis of neural population codes is challenging, as the most commonly used theoretical tool – Fisher information – can lead to erroneous conclusions about the optimality of different coding schemes. Here we revisit the effect of tuning function width and correlation structure on neural population codes based on ideal observer analysis in both a discrimination and reconstruction task. We show that the optimal tuning function width and the optimal correlation structure in both paradigms strongly depend on the available decoding time in a very similar way. In contrast, population codes optimized for Fisher information do not depend on decoding time and are severely suboptimal when only few spikes are available. In addition, we use the neurometric functions of the ideal observer in the classification task to investigate the differential coding properties of these Fisher-optimal codes for fine and coarse discrimination. We find that the discrimination error for these codes does not decrease to zero with increasing population size, even in simple coarse discrimination tasks. Our results suggest that quite different population codes may be optimal for rapid decoding in cortical computations than those inferred from the optimization of Fisher information
A Neural Model of How the Brain Computes Heading from Optic Flow in Realistic Scenes
Animals avoid obstacles and approach goals in novel cluttered environments using visual information, notably optic flow, to compute heading, or direction of travel, with respect to objects in the environment. We present a neural model of how heading is computed that describes interactions among neurons in several visual areas of the primate magnocellular pathway, from retina through V1, MT+, and MSTd. The model produces outputs which are qualitatively and quantitatively similar to human heading estimation data in response to complex natural scenes. The model estimates heading to within 1.5° in random dot or photo-realistically rendered scenes and within 3° in video streams from driving in real-world environments. Simulated rotations of less than 1 degree per second do not affect model performance, but faster simulated rotation rates deteriorate performance, as in humans. The model is part of a larger navigational system that identifies and tracks objects while navigating in cluttered environments.National Science Foundation (SBE-0354378, BCS-0235398); Office of Naval Research (N00014-01-1-0624); National-Geospatial Intelligence Agency (NMA201-01-1-2016
Cortical Dynamics of Navigation and Steering in Natural Scenes: Motion-Based Object Segmentation, Heading, and Obstacle Avoidance
Visually guided navigation through a cluttered natural scene is a challenging problem that animals and humans accomplish with ease. The ViSTARS neural model proposes how primates use motion information to segment objects and determine heading for purposes of goal approach and obstacle avoidance in response to video inputs from real and virtual environments. The model produces trajectories similar to those of human navigators. It does so by predicting how computationally complementary processes in cortical areas MT-/MSTv and MT+/MSTd compute object motion for tracking and self-motion for navigation, respectively. The model retina responds to transients in the input stream. Model V1 generates a local speed and direction estimate. This local motion estimate is ambiguous due to the neural aperture problem. Model MT+ interacts with MSTd via an attentive feedback loop to compute accurate heading estimates in MSTd that quantitatively simulate properties of human heading estimation data. Model MT interacts with MSTv via an attentive feedback loop to compute accurate estimates of speed, direction and position of moving objects. This object information is combined with heading information to produce steering decisions wherein goals behave like attractors and obstacles behave like repellers. These steering decisions lead to navigational trajectories that closely match human performance.National Science Foundation (SBE-0354378, BCS-0235398); Office of Naval Research (N00014-01-1-0624); National Geospatial Intelligence Agency (NMA201-01-1-2016
Robust online estimation of biophysical neural circuits
The control of neuronal networks, whether biological or neuromorphic, relies
on tools for estimating parameters in the presence of model uncertainty. In
this work, we explore the robustness of adaptive observers for neuronal
estimation. Inspired by biology, we show that decentralization and redundancy
help recover the performance of a centralized recursive mean square algorithm
in the presence of uncertainty and mismatch on the internal dynamics of the
model.Comment: 6 pages, 5 figures, accepted at the 62nd IEEE Conference on Decision
and Contro
Weak pairwise correlations imply strongly correlated network states in a neural population
Biological networks have so many possible states that exhaustive sampling is
impossible. Successful analysis thus depends on simplifying hypotheses, but
experiments on many systems hint that complicated, higher order interactions
among large groups of elements play an important role. In the vertebrate
retina, we show that weak correlations between pairs of neurons coexist with
strongly collective behavior in the responses of ten or more neurons.
Surprisingly, we find that this collective behavior is described quantitatively
by models that capture the observed pairwise correlations but assume no higher
order interactions. These maximum entropy models are equivalent to Ising
models, and predict that larger networks are completely dominated by
correlation effects. This suggests that the neural code has associative or
error-correcting properties, and we provide preliminary evidence for such
behavior. As a first test for the generality of these ideas, we show that
similar results are obtained from networks of cultured cortical neurons.Comment: Full account of work presented at the conference on Computational and
Systems Neuroscience (COSYNE), 17-20 March 2005, in Salt Lake City, Utah
(http://cosyne.org
A brief review of neural networks based learning and control and their applications for robots
As an imitation of the biological nervous systems, neural networks (NN), which are characterized with powerful learning ability, have been employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification and patterns recognition etc. This article aims to bring a brief review of the state-of-art NN for the complex nonlinear systems. Recent progresses of NNs in both theoretical developments and practical applications are investigated and surveyed. Specifically, NN based robot learning and control applications were further reviewed, including NN based robot manipulator control, NN based human robot interaction and NN based behavior recognition and generation
Neuromorphic Robust Framework for Concurrent Estimation and Control in Dynamical Systems using Spiking Neural Networks
Concurrent estimation and control of robotic systems remains an ongoing
challenge, where controllers rely on data extracted from states/parameters
riddled with uncertainties and noises. Framework suitability hinges on task
complexity and computational constraints, demanding a balance between
computational efficiency and mission-critical accuracy. This study leverages
recent advancements in neuromorphic computing, particularly spiking neural
networks (SNNs), for estimation and control applications. Our presented
framework employs a recurrent network of leaky integrate-and-fire (LIF)
neurons, mimicking a linear quadratic regulator (LQR) through a robust
filtering strategy, a modified sliding innovation filter (MSIF). Benefiting
from both the robustness of MSIF and the computational efficiency of SNN, our
framework customizes SNN weight matrices to match the desired system model
without requiring training. Additionally, the network employs a biologically
plausible firing rule similar to predictive coding. In the presence of
uncertainties, we compare the SNN-LQR-MSIF with non-spiking LQR-MSIF and the
optimal linear quadratic Gaussian (LQG) strategy. Evaluation across a workbench
linear problem and a satellite rendezvous maneuver, implementing the
Clohessy-Wiltshire (CW) model in space robotics, demonstrates that the
SNN-LQR-MSIF achieves acceptable performance in computational efficiency,
robustness, and accuracy. This positions it as a promising solution for
addressing dynamic systems' concurrent estimation and control challenges in
dynamic systems.Comment: 12 pages, 15 figure
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