38,194 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
Evidence against the Detectability of a Hippocampal Place Code Using Functional Magnetic Resonance Imaging
Individual hippocampal neurons selectively increase their firing rates in specific spatial locations. As a population, these neurons provide a decodable representation of space that is robust against changes to sensory- and path-related cues. This neural code is sparse and distributed, theoretically rendering it undetectable with population recording methods such as functional magnetic resonance imaging (fMRI). Existing studies nonetheless report decoding spatial codes in the human hippocampus using such techniques. Here we present results from a virtual navigation experiment in humans in which we eliminated visual- and path-related confounds and statistical limitations present in existing studies, ensuring that any positive decoding results would represent a voxel-place code. Consistent with theoretical arguments derived from electrophysiological data and contrary to existing fMRI studies, our results show that although participants were fully oriented during the navigation task, there was no statistical evidence for a place code
Asymptotic Analysis on Spatial Coupling Coding for Two-Way Relay Channels
Compute-and-forward relaying is effective to increase bandwidth efficiency of
wireless two-way relay channels. In a compute-and-forward scheme, a relay tries
to decode a linear combination composed of transmitted messages from other
terminals or relays. Design for error correcting codes and its decoding
algorithms suitable for compute-and-forward relaying schemes are still
important issue to be studied. In this paper, we will present an asymptotic
performance analysis on LDPC codes over two-way relay channels based on density
evolution (DE). Because of the asymmetric nature of the channel, we employ the
population dynamics DE combined with DE formulas for asymmetric channels to
obtain BP thresholds. In addition, we also evaluate the asymptotic performance
of spatially coupled LDPC codes for two-way relay channels. The results
indicate that the spatial coupling codes yield improvements in the BP threshold
compared with corresponding uncoupled codes for two-way relay channels.Comment: 5 page
Decoder-in-the-Loop: Genetic Optimization-based LDPC Code Design
LDPC code design tools typically rely on asymptotic code behavior and are
affected by an unavoidable performance degradation due to model imperfections
in the short length regime. We propose an LDPC code design scheme based on an
evolutionary algorithm, the Genetic Algorithm (GenAlg), implementing a
"decoder-in-the-loop" concept. It inherently takes into consideration the
channel, code length and the number of iterations while optimizing the
error-rate of the actual decoder hardware architecture. We construct short
length LDPC codes (i.e., the parity-check matrix) with error-rate performance
comparable to, or even outperforming that of well-designed standardized short
length LDPC codes over both AWGN and Rayleigh fading channels. Our proposed
algorithm can be used to design LDPC codes with special graph structures (e.g.,
accumulator-based codes) to facilitate the encoding step, or to satisfy any
other practical requirement. Moreover, GenAlg can be used to design LDPC codes
with the aim of reducing decoding latency and complexity, leading to coding
gains of up to dB and dB at BLER of for both AWGN and
Rayleigh fading channels, respectively, when compared to state-of-the-art short
LDPC codes. Also, we analyze what can be learned from the resulting codes and,
as such, the GenAlg particularly highlights design paradigms of short length
LDPC codes (e.g., codes with degree-1 variable nodes obtain very good results).Comment: in IEEE Access, 201
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