We propose a new technique for stereo image compression that is based on Distributed Source Coding (DSC). In our setting, two cameras transmit their image back to a processing unit. Naively doing so requires each camera to compress and transmit its image independently. However, the images are correlated because they observe the same scene, and our goal is to take advantage of this fact. In our solution, one camera, assume the left camera, sends its image to the processing unit, as before. The right camera, on the other hand, transmits its image conditioned on the left image, even though the two cameras do not communicate. The processing unit can then decode the right image, using the left image. The solution is based on a two level Auto-Encoder (AE). During training, the first level AE learns a standard single image compression code. The second level AE further compresses the code of the right image, conditioned on the code of the left image. During inference, the left camera uses the first level AE to transmit its image to the processing unit. The right camera, on the other hand, uses the encoders of both levels to transmit its code to the processing unit. The processing unit uses the top level decoder to recover the left image, and the decoders of both levels, as well as the recovered left image, to recover the right image. The system achieves state of the art results in image compression on several popular datasets.

Optimality principles have been useful in explaining many aspects of biological systems. In the context of neural encoding in sensory areas, optimality is naturally formulated in a Bayesian setting as neural tuning which minimizes mean decoding error. Many works optimize Fisher information, which approximates the minimum mean square error (MMSE) of the optimal decoder for long encoding time but may be misleading for short encoding times. We study MMSE-optimal neural encoding of a multivariate stimulus by uniform populations of spiking neurons, under firing rate constraints for each neuron as well as for the entire population. We show that the population-level constraint is essential for the formulation of a well-posed problem having finite optimal tuning widths and optimal tuning aligns with the principal components of the prior distribution. Numerical evaluation of the two-dimensional case shows that encoding only the dimension with higher variance is optimal for short encoding times. We also compare direct MMSE optimization to optimization of several proxies to MMSE: Fisher information, maximum likelihood estimation error, and the Bayesian Cramér-Rao bound. We find that optimization of these measures yields qualitatively misleading results regarding MMSE-optimal tuning and its dependence on encoding time and energy constraints.

Neural decoding may be formulated as dynamic state estimation (filtering) based on point-process observations, a generally intractable problem. Numerical sampling techniques are often practically useful for the decoding of real neural data. However, they are less useful as theoretical tools for modeling and understanding sensory neural systems, since they lead to limited conceptual insight into optimal encoding and decoding strategies. We consider sensory neural populations characterized by a distribution over neuron parameters. We develop an analytically tractable Bayesian approximation to optimal filtering based on the observation of spiking activity that greatly facilitates the analysis of optimal encoding in situations deviating from common assumptions of uniform coding. Continuous distributions are used to approximate large populations with few parameters, resulting in a filter whose complexity does not grow with population size and allowing optimization of population parameters rather than individual tuning functions. Numerical comparison with particle filtering demonstrates the quality of the approximation. The analytic framework leads to insights that are difficult to obtain from numerical algorithms and is consistent with biological observations about the distribution of sensory cells' preferred stimuli.

The process of dynamic state estimation (filtering) based on point process observations is in general intractable. Numerical sampling techniques are often practically useful, but lead to limited conceptual insight about optimal encoding/decoding strategies, which are of significant relevance to Computational Neuroscience. We develop an analytically tractable Bayesian approximation to optimal filtering based on point process observations, which allows us to introduce distributional assumptions about sensory cell properties, that greatly facilitates the analysis of optimal encoding in situations deviating from common assumptions of uniform coding. The analytic framework leads to insights which are difficult to obtain from numerical algorithms, and is consistent with experiments about the distribution of tuning curve centers. Interestingly, we find that the information gained from the absence of spikes may be crucial to performance.

Abstract Cell/B.E. is a heterogeneous multicore processor that was designed for the efficient execution of parallel and vectorizable applications with high computation and memory requirements. The transition to multicores introduces the challenge of providing tools that help programmers tune the code running on these architectures. Tracing tools, in particular, often help locate performance problems related to thread and process communication. A major impediment to implementing tracing on Cell is the absence of a common clock that can be accessed at low cost from all cores. The OS clock is costly to access from the auxiliary cores and the hardware timers cannot be simultaneously set on all the cores. In this paper, we describe an offline trace analysis algorithm that assigns wall‐clock time to trace records based on their thread‐local time stamps and event order. Our experiments on several Cell SDK workloads show that the indeterminism in assigning wall‐clock time to events is low, on average 20–40 clock ticks (translating into 1.4–2.8 µs on the system used in our experiments). We also show how various practical problems, such as the imprecision of time measurement, can be overcome. Copyright © 2009 John Wiley & Sons, Ltd.