Psychedelic drug use and schizotypy in young adults.

Despite recently resurrected scientific interest in classical psychedelics, few studies have focused on potential harms associated with abuse of these substances. In particular, the link between psychedelic use and psychotic symptoms has been debated while no conclusive evidence has been presented. Here, we studied an adult population (n = 1032) with a special focus on young (18-35 years) and healthy individuals (n = 701) to evaluate the association of psychedelic drug use with schizotypy and evidence integration impairment typically observed in psychosis-spectrum disorders. Experimental behavioural testing was performed in a subsample of the subjects (n = 39). We observed higher schizotypy scores in psychedelic users in the total sample. However, the effect size was notably small and only marginally significant when considering young and healthy subjects (Cohen's d = 0.13). Controlling for concomitant drug use, none of our analyses found significant associations between psychedelic use and schizotypal traits. Results from experimental testing showed that total exposure to psychedelics (frequency and temporal proximity of use) was associated with better evidence integration (Cohen's d = 0.13) and a higher sensitivity of fear responses (Cohen's d = 1.05) to the effects instructed knowledge in a reversal aversive learning task modelled computationally with skin conductance response and pupillometry. This effect was present even when controlling for demographics and concomitant drug use. On a group level, however, only difference in sensitivity of fear responses to instructed knowledge reached statistical significance. Taken together, our findings suggest that psychedelic drug use is only weakly associated with psychosis-like symptoms, which, in turn, is to a large extent explained by psychiatric comorbidities and use of other psychoactive substances. Our results also suggest that psychedelics may have an effect on flexibility of evidence integration and aversive learning processes, that may be linked to recently suggested therapeutic effects of psychedelic drugs in non-psychotic psychiatric populations.

Index assignment for progressive transmission of full-search vector quantization.

The authors study codeword index assignment to allow for progressive image transmission of fixed rate full-search vector quantization (VQ). They develop three new methods of assigning indices to a vector quantization codebook and formulate these assignments as labels of nodes of a full-search progressive transmission tree. The tree is used to design intermediate codewords for the decoder so that full-search VQ has a successive approximation character. The binary representation for the path through the tree represents the progressive transmission code. The methods of designing the tree that they apply are the generalized Lloyd algorithm, minimum cost perfect matching from optimization theory, and a method of principal component partitioning. Their empirical results show that the final method gives intermediate signal-to-noise ratios (SNRs) that are close to those obtained with tree-structured vector quantization, yet they have higher final SNRs.

Recurrent neural networks and robust time series prediction.

We propose a robust learning algorithm and apply it to recurrent neural networks. This algorithm is based on filtering outliers from the data and then estimating parameters from the filtered data. The filtering removes outliers from both the target function and the inputs of the neural network. The filtering is soft in that some outliers are neither completely rejected nor accepted. To show the need for robust recurrent networks, we compare the predictive ability of least squares estimated recurrent networks on synthetic data and on the Puget Power Electric Demand time series. These investigations result in a class of recurrent neural networks, NARMA(p,q), which show advantages over feedforward neural networks for time series with a moving average component. Conventional least squares methods of fitting NARMA(p,q) neural network models are shown to suffer a lack of robustness towards outliers. This sensitivity to outliers is demonstrated on both the synthetic and real data sets. Filtering the Puget Power Electric Demand time series is shown to automatically remove the outliers due to holidays. Neural networks trained on filtered data are then shown to give better predictions than neural networks trained on unfiltered time series.

Composite matched filtering with error correction.

Information theory shows that one can communicate with arbitrarily low error probability over a noisy channel. By recognizing that some types of computations can be cast as communications problems, it may be possible to compute accurately with an inexact processor. Traditional analog optical processors, for example, offer advantages of parallelism and speed but suffer from significant inaccuracies. We propose an algorithm whereby the accuracy of matched filter processors can be improved significantly at the cost of a modest increase in computational resources. Errors that are due to noisy data and/or inexact computing can be detected and in some cases corrected.

Optical-processor architectures for alternating-projection neural networks.

Optical-processor architectures for various forms of the alternating-projection neural network are considered. Required iteration is performed by passive optical feedback. No electronics or slow optics (e.g., phase conjugators) are used in the feedback path. The processor can be taught a new training vector by viewing it only once. If the desired outputs are trained to be either +/-1, then the network can be configured to converge in one iteration.

Composite matched filter output partitioning.

A common pattern recognition problem is finding a library element closest, in some sense, to a given reception. In many scenarios, optimal detection requires N matched filters for N library elements. Since N can often be quite large, there is a need for suboptimal techniques that base their decisions on a reduced number of filters. The use of composite matched filters (CMFs) (also called synthetic discriminant functions or linear combination filters) is one technique to achieve this reduction. For two level CMF outputs, the reduction is from N to log(2)N matched filters. Previously, the coefficients of the CMF output were restricted to positive values-often 0 and 1. We refer to such filters as binary CMFs. An alternative approach is to use -1 and +1 for filter coefficients. This alternative filter will be called a bipolar CMF. This paper demonstrates how the extension from a binary to a bipolar CMF greatly improves the detection performance while still maintaining the reduced computational requirements of the binary CMF. Furthermore, the bipolar CMF is invariant to scale: multiplying the input by a positive constant gives the same processor output. This desirable behavior does not exist for the binary CMF.

Synchronous vs asynchronous behavior of Hopfield's CAM neural net.

The performance of Hopfield's neural net operating in synchronous and asynchronous modes is contrasted. Two interconnect matrices are considered: (1) the original Hopfield interconnect matrix; (2) the original Hopfield interconnect matrix with self-neural feedback. Specific attention is focused on techniques to maximize convergence rates and avoid steady-state oscillation. We identify two oscillation modes. Vertical oscillation occurs when the net's energy changes during each iteration. A neural net operated asynchronously cannot oscillate vertically. Synchronous operation, on the other hand, can change a net's energy either positively or negatively and vertical oscillation can occur. Horizontal oscillation occurs when the net alternates between two or more states of the same energy. Certain horizontal oscillations can be avoided by adopting appropriate thresholding rules. We demonstrate, for example, that when (1) the states of neurons with an input sum of zero are assigned the complement of their previous state, (2) the net is operated asynchronously, and (3) nonzero neural autoconnects are allowed, the net will not oscillate either vertically or horizontally.

Performance analysis of associative memories with nonlinearities in the correlation domain.

A matched filter-based architecture for associative memories (MFAMs) has been proposed by many researchers. The correlation from a leg of a matched filter bank, after being altered nonlinearly, weights its corresponding library vector. The weighted vectors are summed and clipped to give an estimate of the library vector closest to the input. We analyze the performance of such architectures for binary and/or bipolar inputs and libraries. Sufficient conditions are derived for the correlation nonlinearity so that the MFAM outputs the correct result. If, for example, N bipolar library vectors are stored, theicorrelation nonlinearity Z(x) = N(x/2) will always result in that library vector closest to the input in the Hamming sense.

Conventional and composite matched filters with error correction: a comparison.

A common pattern recognition problem is finding a library object which most closely matches a received image. For additive white Gaussian input noise, optimal detection performance is obtained using a matched filter for each of the N possible library objects. The use of composite matched filters (CMFs) (also called synthetic discriminant functions or linear combination filters) is one technique of reducing the number of filters required for the recognition problem. For two-level composite matched filter outputs, the reduction is from N to Q = log(2) (N) filters. The CMF's performance, however, can be suboptimum. Using CMFs with bipolar (+1,-1) outputs, this paper examines the detection performance improvement obtained by using error correcting codes. Use of varying levels of error correction is shown to allow trade-off between detection probability and the number of bank filters. Also, we show that in the case of inexact processing, the CMF can perform better than the conventional matched filter.