Encoding models can reveal and decode neural representations in the visual and semantic domains. However, a thorough understanding of how distributed information in auditory cortices and temporal evolution of music contribute to model performance is still lacking in the musical domain. We measured fMRI responses during naturalistic music listening and constructed a two-stage approach that first mapped musical features in auditory cortices and then decoded novel musical pieces. We then probed the influence of stimuli duration (number of time points) and spatial extent (number of voxels) on decoding accuracy. Our approach revealed a linear increase in accuracy with duration and a point of optimal model performance for the spatial extent. We further showed that Shannon entropy is a driving factor, boosting accuracy up to 95% for music with highest information content. These findings provide key insights for future decoding and reconstruction algorithms and open new venues for possible clinical applications.
Digital production, transmission and storage have revolutionized how we access and use information but have also made archiving an increasingly complex task that requires active, continuing maintenance of digital media. This challenge has focused some interest on DNA as an attractive target for information storage because of its capacity for high-density information encoding, longevity under easily achieved conditions and proven track record as an information bearer. Previous DNA-based information storage approaches have encoded only trivial amounts of information or were not amenable to scaling-up, and used no robust error-correction and lacked examination of their cost-efficiency for large-scale information archival. Here we describe a scalable method that can reliably store more information than has been handled before. We encoded computer files totalling 739 kilobytes of hard-disk storage and with an estimated Shannon information of 5.2 × 10(6) bits into a DNA code, synthesized this DNA, sequenced it and reconstructed the original files with 100% accuracy. Theoretical analysis indicates that our DNA-based storage scheme could be scaled far beyond current global information volumes and offers a realistic technology for large-scale, long-term and infrequently accessed digital archiving. In fact, current trends in technological advances are reducing DNA synthesis costs at a pace that should make our scheme cost-effective for sub-50-year archiving within a decade.
Research supports an association between extraversion and dopamine (DA) functioning. DA facilitates incentive motivation and the conditioning and incentive encoding of contexts that predict reward. Therefore, we assessed whether extraversion is related to the efficacy of acquiring conditioned contextual facilitation of three processes that are dependent on DA: motor velocity, positive affect, and visuospatial working memory. We exposed high and low extraverts to three days of association of drug reward (methylphenidate, MP) with a particular laboratory context (Paired group), a test day of conditioning, and three days of extinction in the same laboratory. A Placebo group and an Unpaired group (that had MP in a different laboratory context) served as controls. Conditioned contextual facilitation was assessed by (i) presenting video clips that varied in their pairing with drug and laboratory context and in inherent incentive value, and (ii) measuring increases from day 1 to Test day on the three processes above. Results showed acquisition of conditioned contextual facilitation across all measures to video clips that had been paired with drug and laboratory context in the Paired high extraverts, but no conditioning in the Paired low extraverts (nor in either of the control groups). Increases in the Paired high extraverts were correlated across the three measures. Also, conditioned facilitation was evident on the first day of extinction in Paired high extraverts, despite the absence of the unconditioned effects of MP. By the last day of extinction, responding returned to day 1 levels. The findings suggest that extraversion is associated with variation in the acquisition of contexts that predict reward. Over time, this variation may lead to differences in the breadth of networks of conditioned contexts. Thus, individual differences in extraversion may be maintained by activation of differentially encoded central representations of incentive contexts that predict reward.
Chemosensory neurons extract information about chemical cues from the environment. How is the activity in these sensory neurons transformed into behavior? Using Caenorhabditis elegans, we map a novel sensory neuron circuit motif that encodes odor concentration. Primary neurons, AWC(ON) and AWA, directly detect the food odor benzaldehyde (BZ) and release insulin-like peptides and acetylcholine, respectively, which are required for odor-evoked responses in secondary neurons, ASEL and AWB. Consistently, both primary and secondary neurons are required for BZ attraction. Unexpectedly, this combinatorial code is altered in aged animals: odor-evoked activity in secondary, but not primary, olfactory neurons is reduced. Moreover, experimental manipulations increasing neurotransmission from primary neurons rescues aging-associated neuronal deficits. Finally, we correlate the odor responsiveness of aged animals with their lifespan. Together, these results show how odors are encoded by primary and secondary neurons and suggest reduced neurotransmission as a novel mechanism driving aging-associated sensory neural activity and behavioral declines.
Differentiation models of recognition memory predict a strength-based mirror effect in the distributions of subjective memory strength. Subjective memory strength should increase for targets and simultaneously decrease for foils following a strongly encoded list compared with a weakly encoded list. An alternative explanation for the strength-based mirror effect is that participants adopt a stricter criterion following a strong list than a weak list. Behavioral experiments support the differentiation account. The purpose of this study was to identify the neural bases for these differences. Encoding strength was manipulated (strong, weak) in a rapid event-related fMRI paradigm. To investigate the effect of retrieval context on foils, foils were presented in test blocks containing strong or weak targets. Imaging analyses identified regions in which activity increased faster for foils tested after a strong list than a weak list. The results are interpreted in support of a differentiation account of memory and are suggestive that the angular gyrus plays a role in evaluating evidence related to the memory decision, even for new items.
- IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
- Published about 6 years ago
This paper introduces an efficient method for lossless compression of depth map images, using the representation of a depth image in terms of three entities: the crack-edges, the constant depth regions enclosed by them, and the depth value over each region. The starting representation is identical with that used in a very efficient coder for palette images, the piecewiseconstant image model (PWC) coding, but the techniques used for coding the elements of the representation are more advanced and especially suitable for the type of redundancy present in depth images. First the vertical and horizontal crack-edges separating the constant depth regions are transmitted by two-dimensional context coding using optimally pruned context trees. Both the encoder and decoder can reconstruct the regions of constant depth from the transmitted crack-edge image. The depth value in a given region is encoded by utilizing the depth values of the neighboring regions already encoded, exploiting the natural smoothness of the depth variation and the mutual exclusiveness of the values in neighboring regions. The encoding method is suitable for lossless compression of depth images, obtaining compression of about 10 to 65 times, and additionally can be used as the entropy coding stage for lossy depth compression.
We demonstrate here the first successful implementation in humans of a proof-of-concept system for restoring and improving memory function via facilitation of memory encoding using the patient’s own hippocampal spatiotemporal neural codes for memory. Memory in humans is subject to disruption by drugs, disease and brain injury, yet previous attempts to restore or rescue memory function in humans typically involved only nonspecific, modulation of brain areas and neural systems related to memory retrieval.
Prior research has linked mindfulness to improvements in attention, and suggested that the effects of mindfulness are particularly pronounced when individuals are cognitively depleted or stressed. Yet, no studies have tested whether mindfulness improves declarative awareness of unexpected stimuli in goal-directed tasks. Participants (N=794) were either depleted (or not) and subsequently underwent a brief mindfulness induction (or not). They then completed an inattentional blindness task during which an unexpected distractor appeared on the computer monitor. This task was used to assess declarative conscious awareness of the unexpected distractor’s presence and the extent to which its perceptual properties were encoded. Mindfulness increased awareness of the unexpected distractor (i.e., reduced rates of inattentional blindness). Contrary to predictions, no mindfulness×depletion interaction emerged. Depletion however, increased perceptual encoding of the distractor. These results suggest that mindfulness may foster awareness of unexpected stimuli (i.e., reduce inattentional blindness).
Training in mindfulness skills has been shown to increase autobiographical memory specificity. The aim of this study was to examine whether there is also an association between individual differences in trait mindfulness and memory specificity using a non-clinical student sample (N = 70). Also examined were the relationships between other memory characteristics and trait mindfulness, self-reported depression and rumination. Participants wrote about 12 autobiographical memories, which were recalled in response to emotion word cues in a minimal instruction version of the Autobiographical Memory Test, rated each memory for seven characteristics, and completed the Freiburg Mindfulness Inventory, the Depression, Anxiety, and Stress Scale, and the Ruminative Responses Scale. Higher rumination scores were associated with more reliving and more intense emotion during recall. Depression scores were not associated with any memory variables. Higher trait mindfulness was associated with lower memory specificity and with more intense and more positive emotion during recall. Thus, trait mindfulness is associated with memory specificity, but the association is opposite to that found in mindfulness training studies. It is suggested that this difference may be due to an influence of trait mindfulness on memory encoding as well as retrieval processes and an influence on the mode of self-awareness that leads to a greater focus on momentary rather than narrative self-reference.
Sparse approximation is a hypothesized coding strategy where a population of sensory neurons (e.g. V1) encodes a stimulus using as few active neurons as possible. We present the Spiking LCA (locally competitive algorithm), a rate encoded Spiking Neural Network (SNN) of integrate and fire neurons that calculate sparse approximations. The Spiking LCA is designed to be equivalent to the nonspiking LCA, an analog dynamical system that converges on a ℓ(1)-norm sparse approximations exponentially. We show that the firing rate of the Spiking LCA converges on the same solution as the analog LCA, with an error inversely proportional to the sampling time. We simulate in NEURON a network of 128 neuron pairs that encode 8 × 8 pixel image patches, demonstrating that the network converges to nearly optimal encodings within 20 ms of biological time. We also show that when using more biophysically realistic parameters in the neurons, the gain function encourages additional ℓ(0)-norm sparsity in the encoding, relative both to ideal neurons and digital solvers.