Predictive coding networks for temporal prediction.

One of the key problems the brain faces is inferring the state of the world from a sequence of dynamically changing stimuli, and it is not yet clear how the sensory system achieves this task. A well-established computational framework for describing perceptual processes in the brain is provided by the theory of predictive coding. Although the original proposals of predictive coding have discussed temporal prediction, later work developing this theory mostly focused on static stimuli, and key questions on neural implementation and computational properties of temporal predictive coding networks remain open. Here, we address these questions and present a formulation of the temporal predictive coding model that can be naturally implemented in recurrent networks, in which activity dynamics rely only on local inputs to the neurons, and learning only utilises local Hebbian plasticity. Additionally, we show that temporal predictive coding networks can approximate the performance of the Kalman filter in predicting behaviour of linear systems, and behave as a variant of a Kalman filter which does not track its own subjective posterior variance. Importantly, temporal predictive coding networks can achieve similar accuracy as the Kalman filter without performing complex mathematical operations, but just employing simple computations that can be implemented by biological networks. Moreover, when trained with natural dynamic inputs, we found that temporal predictive coding can produce Gabor-like, motion-sensitive receptive fields resembling those observed in real neurons in visual areas. In addition, we demonstrate how the model can be effectively generalized to nonlinear systems. Overall, models presented in this paper show how biologically plausible circuits can predict future stimuli and may guide research on understanding specific neural circuits in brain areas involved in temporal prediction.

Dissociable Roles of the Auditory Midbrain and Cortex in Processing the Statistical Features of Natural Sound Textures.

Sound texture perception takes advantage of a hierarchy of time-averaged statistical features of acoustic stimuli, but much remains unclear about how these statistical features are processed along the auditory pathway. Here, we compared the neural representation of sound textures in the inferior colliculus (IC) and auditory cortex (AC) of anesthetized female rats. We recorded responses to texture morph stimuli that gradually add statistical features of increasingly higher complexity. For each texture, several different exemplars were synthesized using different random seeds. An analysis of transient and ongoing multiunit responses showed that the IC units were sensitive to every type of statistical feature, albeit to a varying extent. In contrast, only a small proportion of AC units were overtly sensitive to any statistical features. Differences in texture types explained more of the variance of IC neural responses than did differences in exemplars, indicating a degree of "texture type tuning" in the IC, but the same was, perhaps surprisingly, not the case for AC responses. We also evaluated the accuracy of texture type classification from single-trial population activity and found that IC responses became more informative as more summary statistics were included in the texture morphs, while for AC population responses, classification performance remained consistently very low. These results argue against the idea that AC neurons encode sound type via an overt sensitivity in neural firing rate to fine-grain spectral and temporal statistical features.

Hierarchical temporal prediction captures motion processing along the visual pathway.

Visual neurons respond selectively to features that become increasingly complex from the eyes to the cortex. Retinal neurons prefer flashing spots of light, primary visual cortical (V1) neurons prefer moving bars, and those in higher cortical areas favor complex features like moving textures. Previously, we showed that V1 simple cell tuning can be accounted for by a basic model implementing temporal prediction - representing features that predict future sensory input from past input (Singer et al., 2018). Here, we show that hierarchical application of temporal prediction can capture how tuning properties change across at least two levels of the visual system. This suggests that the brain does not efficiently represent all incoming information; instead, it selectively represents sensory inputs that help in predicting the future. When applied hierarchically, temporal prediction extracts time-varying features that depend on increasingly high-level statistics of the sensory input.

Burnout and Self-Perceived Stress in Workers in Essential Services after the Impact of Hurricanes Irma and Maria.

OBJECTIVE: To examine the levels of burnout and self-perceived stress in workers at the Puerto Rico Electric Power Authority, who experienced major disasters: Hurricanes Irma and Maria. METHODS: A quantitative descriptive cross-sectional study with a non-probabilistic convenience sample was conducted in workers at the Puerto Rico Electric Power Authority. A structured questionnaire was administered to a sample of 163 eligible participants, aged 21 years and older, who participated voluntarily. Using employees of the electric company, the study examined the relationships between burnout and several characteristics (years of employment, existing health conditions, and coping strategies) both before and after Hurricanes Irma and Maria. Burnout was assessed with Gil-Monte's Spanish Burnout Inventory, and self-perceived stress was assessed with the 14-item Perceived Stress Scale. RESULTS: Before the hurricanes, 16.6% of the workers reported high levels of burnout syndrome, while, after the hurricanes, the proportion increased to one-fifth (20.9%). Prior to the 2 hurricanes, more than one-fourth (23.4%) of the sample reported being extremely stressed; after the hurricanes, that proportion increased to 55%. Factors such as years of employment, counseling, and self-perceived stress showed significant statistical associations (P < .05) with burnout. CONCLUSION: From a public health standpoint, priority should be given to this population, thereby preventing burnout and any other negative effects of the aftermath (i.e., the lengthy response, recovery, and reconstruction) of these kinds of major disaster.

Cortical adaptation to sound reverberation.

In almost every natural environment, sounds are reflected by nearby objects, producing many delayed and distorted copies of the original sound, known as reverberation. Our brains usually cope well with reverberation, allowing us to recognize sound sources regardless of their environments. In contrast, reverberation can cause severe difficulties for speech recognition algorithms and hearing-impaired people. The present study examines how the auditory system copes with reverberation. We trained a linear model to recover a rich set of natural, anechoic sounds from their simulated reverberant counterparts. The model neurons achieved this by extending the inhibitory component of their receptive filters for more reverberant spaces, and did so in a frequency-dependent manner. These predicted effects were observed in the responses of auditory cortical neurons of ferrets in the same simulated reverberant environments. Together, these results suggest that auditory cortical neurons adapt to reverberation by adjusting their filtering properties in a manner consistent with dereverberation.

Sensitivity of neural responses in the inferior colliculus to statistical features of sound textures.

Previous psychophysical studies have identified a hierarchy of time-averaged statistics which determine the identity of natural sound textures. However, it is unclear whether the neurons in the inferior colliculus (IC) are sensitive to each of these statistical features in the natural sound textures. We used 13 representative sound textures spanning the space of 3 statistics extracted from over 200 natural textures. The synthetic textures were generated by incorporating the statistical features in a step-by-step manner, in which a particular statistical feature was changed while the other statistical features remain unchanged. The extracellular activity in response to the synthetic texture stimuli was recorded in the IC of anesthetized rats. Analysis of the transient and sustained multiunit activity after each transition of statistical feature showed that the IC units were sensitive to the changes of all types of statistics, although to a varying extent. For example, we found that more neurons were sensitive to the changes in variance than that in the modulation correlations. Our results suggest that the sensitivity of the statistical features in the subcortical levels contributes to the identification and discrimination of natural sound textures.

Exploring the distribution of statistical feature parameters for natural sound textures.

Sounds like "running water" and "buzzing bees" are classes of sounds which are a collective result of many similar acoustic events and are known as "sound textures". A recent psychoacoustic study using sound textures has reported that natural sounding textures can be synthesized from white noise by imposing statistical features such as marginals and correlations computed from the outputs of cochlear models responding to the textures. The outputs being the envelopes of bandpass filter responses, the 'cochlear envelope'. This suggests that the perceptual qualities of many natural sounds derive directly from such statistical features, and raises the question of how these statistical features are distributed in the acoustic environment. To address this question, we collected a corpus of 200 sound textures from public online sources and analyzed the distributions of the textures' marginal statistics (mean, variance, skew, and kurtosis), cross-frequency correlations and modulation power statistics. A principal component analysis of these parameters revealed a great deal of redundancy in the texture parameters. For example, just two marginal principal components, which can be thought of as measuring the sparseness or burstiness of a texture, capture as much as 64% of the variance of the 128 dimensional marginal parameter space, while the first two principal components of cochlear correlations capture as much as 88% of the variance in the 496 correlation parameters. Knowledge of the statistical distributions documented here may help guide the choice of acoustic stimuli with high ecological validity in future research.

Simple transformations capture auditory input to cortex.

Sounds are processed by the ear and central auditory pathway. These processing steps are biologically complex, and many aspects of the transformation from sound waveforms to cortical response remain unclear. To understand this transformation, we combined models of the auditory periphery with various encoding models to predict auditory cortical responses to natural sounds. The cochlear models ranged from detailed biophysical simulations of the cochlea and auditory nerve to simple spectrogram-like approximations of the information processing in these structures. For three different stimulus sets, we tested the capacity of these models to predict the time course of single-unit neural responses recorded in ferret primary auditory cortex. We found that simple models based on a log-spaced spectrogram with approximately logarithmic compression perform similarly to the best-performing biophysically detailed models of the auditory periphery, and more consistently well over diverse natural and synthetic sounds. Furthermore, we demonstrated that including approximations of the three categories of auditory nerve fiber in these simple models can substantially improve prediction, particularly when combined with a network encoding model. Our findings imply that the properties of the auditory periphery and central pathway may together result in a simpler than expected functional transformation from ear to cortex. Thus, much of the detailed biological complexity seen in the auditory periphery does not appear to be important for understanding the cortical representation of sound.

Auditory cortical representation of music favours the perceived beat.

Previous research has shown that musical beat perception is a surprisingly complex phenomenon involving widespread neural coordination across higher-order sensory, motor and cognitive areas. However, the question of how low-level auditory processing must necessarily shape these dynamics, and therefore perception, is not well understood. Here, we present evidence that the auditory cortical representation of music, even in the absence of motor or top-down activations, already favours the beat that will be perceived. Extracellular firing rates in the rat auditory cortex were recorded in response to 20 musical excerpts diverse in tempo and genre, for which musical beat perception had been characterized by the tapping behaviour of 40 human listeners. We found that firing rates in the rat auditory cortex were on average higher on the beat than off the beat. This 'neural emphasis' distinguished the beat that was perceived from other possible interpretations of the beat, was predictive of the degree of tapping consensus across human listeners, and was accounted for by a spectrotemporal receptive field model. These findings strongly suggest that the 'bottom-up' processing of music performed by the auditory system predisposes the timing and clarity of the perceived musical beat.

Author Correction: The importance of Antarctic krill in biogeochemical cycles.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The importance of Antarctic krill in biogeochemical cycles.

Antarctic krill (Euphausia superba) are swarming, oceanic crustaceans, up to two inches long, and best known as prey for whales and penguins - but they have another important role. With their large size, high biomass and daily vertical migrations they transport and transform essential nutrients, stimulate primary productivity and influence the carbon sink. Antarctic krill are also fished by the Southern Ocean's largest fishery. Yet how krill fishing impacts nutrient fertilisation and the carbon sink in the Southern Ocean is poorly understood. Our synthesis shows fishery management should consider the influential biogeochemical role of both adult and larval Antarctic krill.

A dynamic network model of temporal receptive fields in primary auditory cortex.

Auditory neurons encode stimulus history, which is often modelled using a span of time-delays in a spectro-temporal receptive field (STRF). We propose an alternative model for the encoding of stimulus history, which we apply to extracellular recordings of neurons in the primary auditory cortex of anaesthetized ferrets. For a linear-non-linear STRF model (LN model) to achieve a high level of performance in predicting single unit neural responses to natural sounds in the primary auditory cortex, we found that it is necessary to include time delays going back at least 200 ms in the past. This is an unrealistic time span for biological delay lines. We therefore asked how much of this dependence on stimulus history can instead be explained by dynamical aspects of neurons. We constructed a neural-network model whose output is the weighted sum of units whose responses are determined by a dynamic firing-rate equation. The dynamic aspect performs low-pass filtering on each unit's response, providing an exponentially decaying memory whose time constant is individual to each unit. We find that this dynamic network (DNet) model, when fitted to the neural data using STRFs of only 25 ms duration, can achieve prediction performance on a held-out dataset comparable to the best performing LN model with STRFs of 200 ms duration. These findings suggest that integration due to the membrane time constants or other exponentially-decaying memory processes may underlie linear temporal receptive fields of neurons beyond 25 ms.

STRFs in primary auditory cortex emerge from masking-based statistics of natural sounds.

We investigate how the neural processing in auditory cortex is shaped by the statistics of natural sounds. Hypothesising that auditory cortex (A1) represents the structural primitives out of which sounds are composed, we employ a statistical model to extract such components. The input to the model are cochleagrams which approximate the non-linear transformations a sound undergoes from the outer ear, through the cochlea to the auditory nerve. Cochleagram components do not superimpose linearly, but rather according to a rule which can be approximated using the max function. This is a consequence of the compression inherent in the cochleagram and the sparsity of natural sounds. Furthermore, cochleagrams do not have negative values. Cochleagrams are therefore not matched well by the assumptions of standard linear approaches such as sparse coding or ICA. We therefore consider a new encoding approach for natural sounds, which combines a model of early auditory processing with maximal causes analysis (MCA), a sparse coding model which captures both the non-linear combination rule and non-negativity of the data. An efficient truncated EM algorithm is used to fit the MCA model to cochleagram data. We characterize the generative fields (GFs) inferred by MCA with respect to in vivo neural responses in A1 by applying reverse correlation to estimate spectro-temporal receptive fields (STRFs) implied by the learned GFs. Despite the GFs being non-negative, the STRF estimates are found to contain both positive and negative subfields, where the negative subfields can be attributed to explaining away effects as captured by the applied inference method. A direct comparison with ferret A1 shows many similar forms, and the spectral and temporal modulation tuning of both ferret and model STRFs show similar ranges over the population. In summary, our model represents an alternative to linear approaches for biological auditory encoding while it captures salient data properties and links inhibitory subfields to explaining away effects.

Sensory cortex is optimized for prediction of future input.

Neurons in sensory cortex are tuned to diverse features in natural scenes. But what determines which features neurons become selective to? Here we explore the idea that neuronal selectivity is optimized to represent features in the recent sensory past that best predict immediate future inputs. We tested this hypothesis using simple feedforward neural networks, which were trained to predict the next few moments of video or audio in clips of natural scenes. The networks developed receptive fields that closely matched those of real cortical neurons in different mammalian species, including the oriented spatial tuning of primary visual cortex, the frequency selectivity of primary auditory cortex and, most notably, their temporal tuning properties. Furthermore, the better a network predicted future inputs the more closely its receptive fields resembled those in the brain. This suggests that sensory processing is optimized to extract those features with the most capacity to predict future input.

Midbrain adaptation may set the stage for the perception of musical beat.

The ability to spontaneously feel a beat in music is a phenomenon widely believed to be unique to humans. Though beat perception involves the coordinated engagement of sensory, motor and cognitive processes in humans, the contribution of low-level auditory processing to the activation of these networks in a beat-specific manner is poorly understood. Here, we present evidence from a rodent model that midbrain preprocessing of sounds may already be shaping where the beat is ultimately felt. For the tested set of musical rhythms, on-beat sounds on average evoked higher firing rates than off-beat sounds, and this difference was a defining feature of the set of beat interpretations most commonly perceived by human listeners over others. Basic firing rate adaptation provided a sufficient explanation for these results. Our findings suggest that midbrain adaptation, by encoding the temporal context of sounds, creates points of neural emphasis that may influence the perceptual emergence of a beat.

'Modern' distal femoral locking plates allow safe, early weight-bearing with a high rate of union and low rate of failure: five-year experience from a United Kingdom major trauma centre.

AIMS: Fractures of the distal femur can be challenging to manage and are on the increase in the elderly osteoporotic population. Management with casting or bracing can unacceptably limit a patient's ability to bear weight, but historically, operative fixation has been associated with a high rate of re-operation. In this study, we describe the outcomes of fixation using modern implants within a strategy of early return to function. PATIENTS AND METHODS: All patients treated at our centre with lateral distal femoral locking plates (LDFLP) between 2009 and 2014 were identified. Fracture classification and operative information including weight-bearing status, rates of union, re-operation, failure of implants and mortality rate, were recorded. RESULTS: A total of 127 fractures were identified in 122 patients. The mean age was 72.8 years (16 to 101) and 92 of the patients (75%) were female. A consultant performed the operation in 85 of the cases, (67%) with the remainder performed under direct consultant supervision. In total 107 patients (84%) were allowed to bear full weight immediately. The rate of clinical and radiological union was 81/85 (95%) and only four fractures of 127 (3%) fractures required re-operation for failure of surgery. The 30-day, three- and 12-month mortality rates were 6 (5%), 17 (15%) and 25 (22%), respectively. CONCLUSION: Our study suggests an exponential increase in the incidence of a fracture of the distal femur with age, analogous to the population suffering from a proximal femoral fracture. Allowing immediate unrestricted weight-bearing after LDFLP fixation in these elderly patients was not associated with failure of fixation. There was a high rate of union and low rate of re-operation. Cite this article: Bone Joint J 2017;99-B:951-7.

Evaluation of convalescent whole blood for treating Ebola Virus Disease in Freetown, Sierra Leone.

BACKGROUND: Convalescent blood therapy has been a promising form of treatment for Ebola Virus Disease (EVD), but less attention has been focused on it for treatment. METHOD: We assessed the effectiveness of convalescent whole blood (CWB) in the treatment of consented EVD patients. We recruited 69 subjects in December 2014 up to April 2015, at the 34 Military Hospital in Wilberforce and the PTS 1 Ebola Treatment Unit in Hastings, Freetown. Forty-four were given CWB, and 25 who consented but preferred to be exempted from the CWB treatment were used to compare clinical outcomes. All were given routine treatment used at the Ebola Treatment Unit. RESULTS: One of 44 subjects treated with CWB dropped out of the study and 31 recovered while 12 succumbed to the disease with a case fatality rate of 27.9%. For the group that was given routine treatment without CWB, 11 died with a case fatality rate of 44%. There was a significant difference between admission viral load and viral load after the first 24 h of treatment with convalescent whole blood (P < 0.01). The odds ratio for survival with CWB was 2.3 (95% CI, 0.8-6.5). CONCLUSION: CWB is promising for treating EVD in resource-poor settings, especially in the early phases of outbreaks when resource-mobilization is done. Even though our sample size was small and the evaluation was not randomised, our results contribute to existing evidence that convalescent whole blood could be considered as a useful candidate for treating EVD. Further studies that are randomised will be required to further assess the efficacy of CWB as treatment option during any EVD outbreak.

Network Receptive Field Modeling Reveals Extensive Integration and Multi-feature Selectivity in Auditory Cortical Neurons.

Cortical sensory neurons are commonly characterized using the receptive field, the linear dependence of their response on the stimulus. In primary auditory cortex neurons can be characterized by their spectrotemporal receptive fields, the spectral and temporal features of a sound that linearly drive a neuron. However, receptive fields do not capture the fact that the response of a cortical neuron results from the complex nonlinear network in which it is embedded. By fitting a nonlinear feedforward network model (a network receptive field) to cortical responses to natural sounds, we reveal that primary auditory cortical neurons are sensitive over a substantially larger spectrotemporal domain than is seen in their standard spectrotemporal receptive fields. Furthermore, the network receptive field, a parsimonious network consisting of 1-7 sub-receptive fields that interact nonlinearly, consistently better predicts neural responses to auditory stimuli than the standard receptive fields. The network receptive field reveals separate excitatory and inhibitory sub-fields with different nonlinear properties, and interaction of the sub-fields gives rise to important operations such as gain control and conjunctive feature detection. The conjunctive effects, where neurons respond only if several specific features are present together, enable increased selectivity for particular complex spectrotemporal structures, and may constitute an important stage in sound recognition. In conclusion, we demonstrate that fitting auditory cortical neural responses with feedforward network models expands on simple linear receptive field models in a manner that yields substantially improved predictive power and reveals key nonlinear aspects of cortical processing, while remaining easy to interpret in a physiological context.

Meta-adaptation in the auditory midbrain under cortical influence.

Neural adaptation is central to sensation. Neurons in auditory midbrain, for example, rapidly adapt their firing rates to enhance coding precision of common sound intensities. However, it remains unknown whether this adaptation is fixed, or dynamic and dependent on experience. Here, using guinea pigs as animal models, we report that adaptation accelerates when an environment is re-encountered-in response to a sound environment that repeatedly switches between quiet and loud, midbrain neurons accrue experience to find an efficient code more rapidly. This phenomenon, which we term meta-adaptation, suggests a top-down influence on the midbrain. To test this, we inactivate auditory cortex and find acceleration of adaptation with experience is attenuated, indicating a role for cortex-and its little-understood projections to the midbrain-in modulating meta-adaptation. Given the prevalence of adaptation across organisms and senses, meta-adaptation might be similarly common, with extensive implications for understanding how neurons encode the rapidly changing environments of the real world.

Reexamining Echidna Physiology: The Big Picture for Tachyglossus aculeatus acanthion.

The early divergence of monotremes and therian mammals has resulted in considerable interest in the comparative physiology of the short-beaked echidna (Tachyglossus aculeatus), the most common and widespread living monotreme. However, there are many and varied interpretations of its physiology, reflecting the many and varied studies, limitations and uncertainties of aspects of some previous studies, and potential differences between the various subspecies. Consequently, we thoroughly examine here the standardized physiology of the most widely distributed subspecies of short-beaked echidna (T. aculeatus acanthion) over a wide range of ambient temperatures to definitively assess its physiology in a comparative context. We conclude that the low and variable body temperature of the short-beaked echidna is physiologically "primitive," but it also reflects adaptation to its myrmecophagous niche. Other aspects of its physiology are more typically mammalian. A low metabolic rate reflects its low body temperature, and ventilatory variables are matched to accommodate a modest gas exchange requirement. Thermal conductance is typical for a mammal of equivalent mass. In contrast to previous studies, we demonstrate that short-beaked echidnas can enhance evaporative water loss above thermoneutrality, like other mammals, with a similar capacity for evaporative heat loss. Cooling of their nasal blood sinus with nasal mucous may contribute to this enhanced evaporative cooling. Their capacity to evaporatively cool explains how their distribution can include habitats where ambient temperature, even in shelters, exceeds their supposed critical thermal limit.