Encoding Of Information Research Articles

Simultaneous structures in sign languages: Acquisition and emergence.

The visual-gestural modality affords its users simultaneous movement of several independent articulators and thus lends itself to simultaneous encoding of information. Much research has focused on the fact that sign languages coordinate two manual articulators in addition to a range of non-manual articulators to present different types of linguistic information simultaneously, from phonological contrasts to inflection, spatial relations, and information structure. Children and adults acquiring a signed language arguably thus need to comprehend and produce simultaneous structures to a greater extent than individuals acquiring a spoken language. In this paper, we discuss the simultaneous encoding that is found in emerging and established sign languages; we also discuss places where sign languages are unexpectedly sequential. We explore potential constraints on simultaneity in cognition and motor coordination that might impact the acquisition and use of simultaneous structures.

Encoding, Decoding, and Rendering Information in DNA Nanoswitch Libraries.

DNA-based construction allows the creation of molecular devices that are useful in information storage and processing. Here, we combine the programmability of DNA nanoswitches and stimuli-responsive conformational changes to demonstrate information encoding and graphical readout using gel electrophoresis. We encoded information as 5-bit binary codes for alphanumeric characters using a combination of DNA and RNA inputs that can be decoded using molecular stimuli such as a ribonuclease. We also show that a similar strategy can be used for graphical visual readout of alphabets on an agarose gel, information that is encoded by nucleic acids and decoded by a ribonuclease. Our method of information encoding and processing could be combined with DNA actuation for molecular computation and diagnostics that require a nonarbitrary visual readout.

Effects of Faces and Voices on the Encoding of Biographic Information.

There are multiple forms of knowledge about people. Whether diverse person-related data interact is of interest regarding the more general issue of integration of multi-source information about the world. Our goal was to examine whether perception of a person's face or voice enhanced the encoding of their biographic data. We performed three experiments. In the first experiment, subjects learned the biographic data of a character with or without a video clip of their face. In the second experiment, they learned the character's data with an audio clip of either a generic narrator's voice or the character's voice relating the same biographic information. In the third experiment, an audiovisual clip of both the face and voice of either a generic narrator or the character accompanied the learning of biographic data. After learning, a test phase presented biographic data alone, and subjects were tested first for familiarity and second for matching of biographic data to the name. The results showed equivalent learning of biographic data across all three experiments, and none showed evidence that a character's face or voice enhanced the learning of biographic information. We conclude that the simultaneous processing of perceptual representations of people may not modulate the encoding of biographic data.

Input correlations impede suppression of chaos and learning in balanced firing-rate networks.

Neural circuits exhibit complex activity patterns, both spontaneously and evoked by external stimuli. Information encoding and learning in neural circuits depend on how well time-varying stimuli can control spontaneous network activity. We show that in firing-rate networks in the balanced state, external control of recurrent dynamics, i.e., the suppression of internally-generated chaotic variability, strongly depends on correlations in the input. A distinctive feature of balanced networks is that, because common external input is dynamically canceled by recurrent feedback, it is far more difficult to suppress chaos with common input into each neuron than through independent input. To study this phenomenon, we develop a non-stationary dynamic mean-field theory for driven networks. The theory explains how the activity statistics and the largest Lyapunov exponent depend on the frequency and amplitude of the input, recurrent coupling strength, and network size, for both common and independent input. We further show that uncorrelated inputs facilitate learning in balanced networks.

Regional specialization of the tongue revealed by gustatory ganglion imaging.

Gustatory information is relayed from the anterior tongue by geniculate ganglion neurons and from the posterior tongue by neurons of the petrosal portion of the jugular/nodose/petrosal ganglion complex. Here, we use invivo calcium imaging in mice to compare the encoding of taste information in the geniculate and petrosal ganglia, at single-neuron resolution. Our data support an anterior/posterior specialization of taste information coding from the tongue to the ganglia, with petrosal neurons more responsive to umami or bitter and less responsive to sweet or salty stimuli than geniculate neurons. We found that umami (50mM MPG+ 1mM IMP) promotes salivation when applied to the posterior, but not anterior, tongue. This suggests a functional taste map of the mammalian tongue where the anterior and posterior taste pathways are differentially responsive to specific taste qualities, and differentially regulate downstream physiological functions of taste, such as promoting salivation.

Subcortical glutamatergic inputs exhibit a Hebbian form of long-term potentiation in the dentate gyrus

The hippocampus receives glutamatergic and GABAergic inputs from subcortical regions. Despite the important roles of these subcortical inputs in the regulation of hippocampal circuit, it has not been explored whether associative activation of the subcorticohippocampal pathway induces Hebbian plasticity of subcortical inputs. Here, we demonstrate that the hypothalamic supramammillary nucleus (SuM) to the dentate granule cell (GC) synapses, which co-release glutamate and GABA, undergo associative long-term potentiation (LTP) of glutamatergic, but not GABAergic, co-transmission. This LTP is induced by pairing of SuM inputs with GC spikes. We found that this Hebbian LTP is input-specific, requires NMDA receptors and CaMKII activation, and is expressed postsynaptically. By the net increase in excitatory drive of SuM inputs following LTP induction, associative inputs of SuM and the perforant path effectively discharge GCs. Our results highlight the important role of associative plasticity at SuM-GC synapses in the regulation of dentate gyrus activity and for the encoding of SuM-related information.

OCTOPUS: Overcoming Performance and Privatization Bottlenecks in Distributed Learning

The diversity and quantity of data warehouses, gathering data from distributed devices such as mobile devices, can enhance the success and robustness of machine learning algorithms. Federated learning enables distributed participants to collaboratively learn a commonly shared model while holding data locally. However, it is also faced with expensive communication and limitations due to the heterogeneity of distributed data sources and lack of access to global data. In this paper, we investigate a practical distributed learning scenario where multiple downstream tasks (e.g., classifiers) could be efficiently learned from dynamically updated and non-iid distributed data sources while providing local data privatization. We introduce a new distributed/collaborative learning scheme to address communication overhead via latent compression, leveraging global data while providing privatization of local data without additional cost due to encryption or perturbation. This scheme divides learning into (1) informative feature encoding, and transmitting the latent representation of local data to address communication overhead; (2) downstream tasks centralized at the server using the encoded codes gathered from each node to address computing overhead. Besides, a disentanglement strategy is applied to address the privatization of sensitive components of local data. Extensive experiments are conducted on image and speech datasets. The results demonstrate that downstream tasks with the compact latent representations with the privatization of local data can achieve comparable accuracy to centralized learning.

Transformer-Based Reinforcement Learning for Pickup and Delivery Problems With Late Penalties

Pickup and delivery problems with late penalties can be adopted to model a wide range of practical situations in the field of transportation and logistics. However, the restrictions on the multiple vehicles’ service sequences and non-linearity caused by the late penalties make it time-consuming to solve this problem. To overcome this difficulty, we propose a novel reinforcement learning framework inspired by transformer architecture to generate tours instantly after offline training. This framework, as trained through the policy gradient method, consists of the information encoder process which can extract the coupling relationships among the pickup and delivery customers, and the decoder process with multi-vehicle attention network to allocate reasonable orders to each vehicle. Validated on Sioux Falls network, the proposed method yields the improvement of 2.4%-8.0% on the solution quality compared with Google OR-Tools and several heuristic algorithms. Notably, the baselines require dozens of minutes to achieve a lesser result on the case with 100 customers while the well-trained model based on our method can be deployed to provide a high-quality solution within seconds. Furthermore, the proposed model also shows good generalization ability in different scenarios with various scale problems, and the obtained results are shown to be quite robust to counter the fluctuation of travel time.

Reconfigurable dynamics of optical soliton molecular complexes in an ultrafast thulium fiber laser

Dissipative optical solitons of particle-like structures can form soliton molecules with striking molecule-like interactions. However, the experimental investigation of soliton molecules in ultrafast lasers is still largely confined to bound states of only two solitons and is far from being explored for soliton molecular complexes. Here, the reconfigurable switching of soliton molecular complexes originating from the binding of two soliton-pair molecules was demonstrated around 2 μm, simultaneously opening an emerging window in the longer wavelength. The periodic switching of soliton molecules with dual-stability can be resolved. Moreover, the single and multiple successively switching of soliton molecules can be triggered by the collision of drifting soliton or control over saturable absorption parameters. Further, the consecutive reversible switching with high fidelity is verified in soliton molecules with periodic pump modulation. These results unveil the dynamical soliton interaction and motivate potential applications in ultrafast spectroscopy, information encoding, and computations.

Peptide-based sensing of Pb2+, molecular logic computing, information encoding, cryptography, and steganography

Peptides are a class of powerful molecules and building blocks similar to DNA. However, peptides with composition diversity and molecular recognition ability are rarely used for multipurpose integrated applications (especially in sensing, information processing, communication, and security). Herein, peptide-based sensing of Pb2+, logic computing, information encoding, and security applications were comprehensively demonstrated. Our fluorescently labeled Pb2+-binding peptide exhibited different responses to various metal ions but was specific to Pb2+, which were attributed to the fluorescence static quenching and the transformation of the peptide conformation induced by Pb2+. This peptide probe was successfully used to quantitatively detect Pb2+ (detection limit 3.38 nM) in actual water samples and to perform logic calculations. In addition, due to natural concealment of molecules, encoding, encryption, and hiding of 2 kinds of dimensional information (such as text and images), were displayed by using peptide sequences and its selectivity. This information encoding method based on molecules and their subsidiary properties shows great flexibility and scalability and provides a new option for information representation and protection. Inspired by this idea, more interesting and creative molecular sensing and information systems will be developed to promote connectivity, programmability, and intelligence of the molecular world.

Logic and Circuitry of a Memory Element to Ensure the Reliability Unitary Code and Speed of Ring Counters

Binary encoding of information is prone to errors. The simplest code is a unitary code. Modern coding theory does not allow us to exclude all possible errors in the unitary code. As a result of errors in a bistable memory cell, the unitary code may be corrupted. The properties and features of simple memory elements based on a bistable cell for computer-aided design in CAD are considered. The logic of the prohibition of indeterminate states of a memory element with negative feedback, in contrast to synchronous and self-synchronous logic, is analyzed. The proposed logic is modeled by blocking combined memory elements on the example of a ring counter. The complexity and speed of the ring counter are analyzed, the reliability of its unitary code is evaluated by the proportion of detected errors. The purpose of the study is to analyze the circuit complexity and performance of a synchronous-asynchronous memory element with negative feedback and the principle of error prevention in the synchronous-asynchronous mode of complex combined memory elements using the example of a ring counter with an assessment of the reliability of its output equidistant unitary code by the proportion of detected errors. As a result of the study, using the example of a counter, the possibility of simplifying and improving the performance of digital serial devices with the exception of errors in triggering their discharges, with the exception of those that are not blocked, is shown. The use of forbidding logic to exclude an undefined state of memory during encoding allows the code to be equidistant. The introduction of the prohibition logic into memory is the fact of a sufficient reduction in the number of coding errors. The proposed logic is justified by the simplicity of synchronous-asynchronous memory management. The performance indicators of the developed memory element are comparable to the performance indicators of self-synchronous logic circuits. The logic of a synchronous-asynchronous memory element with negative feedback expands the capabilities of digital devices. The reliability of the unitary code of the proposed ring counter is close to unity. The considered synchronous-asynchronous logic, the possibility of increasing the speed and preventing errors in the memory element will allow choosing more effective ways to develop promising information processing tools using CAD.

Information encoding: different types of artificial sign systems

The purpose of the article is to systematise and characterise the types of records of artificial sign systems as information products that are the result of a purposeful implementation by a person or a group of persons of certain ways of solving communicative tasks, which is their main difference from natural sign records. The research methodology is based on the application of a cultural, systematic, structural and functional approach, general scientific and special methods of cognition, and the method of ascent from the abstract to the concrete, which enable comprehensive consideration of possible types of records of artificial sign systems. The scientific novelty of the article consists in the generalisation and systematisation of records in artificial sign systems that can be used in information and communication, as well as in information and analytical activities. The features of using records in each of the given artificial sign systems are determined. Conclusions. Records in artificial sign systems, including special record systems, record systems in formalised, informational, and programming languages, as well as international and fictional languages, as a result of information encoding, can serve to establish, simplify and protect documentary communications in certain areas of science and technology. The practical application of artificial sign systems is important, especially in cases where the use of natural languages is complicated or in the case of communication with a machine if there is a need to index documents, prepare information requests, and describe facts for their further search and storage. It is extremely necessary in cases of using records to prepare an algorithm for their processing, since it will affect the speed and perfection of decision-making, contribute to the effectiveness of communication and the provision of high-quality information services taking into account the information requests of the target audience. Keywords: artificial languages, sign systems, records, varieties, systematisation, generalization.

Time-frequency analysis of brain activity in response to directional and non-directional visual stimuli: an event related spectral perturbations (ERSP) study

Objective. A large part of the cerebral cortex is dedicated to the processing of visual stimuli and there is still much to understand about such processing modalities and hierarchies. The main aim of the present study is to investigate the differences between directional visual stimuli (DS) and non-directional visual stimuli (n-DS) processing by time-frequency analysis of brain electroencephalographic activity during a visuo-motor task. Electroencephalography (EEG) data were divided into four regions of interest (ROIs) (frontal, central, parietal, occipital). Approach. The analysis of the visual stimuli processing was based on the combination of electroencephalographic recordings and time-frequency analysis. Event related spectral perturbations (ERSPs) were computed with spectrum analysis that allow to obtain the average time course of relative changes induced by the stimulus presentation in spontaneous EEG amplitude spectrum. Main results. Visual stimuli processing enhanced the same pattern of spectral modulation in all investigated ROIs with differences in amplitudes and timing. Additionally, statistically significant differences in occipital ROI between the DS and n-DS visual stimuli processing in theta, alpha and beta bands were found. Significance. These evidences suggest that ERSPs could be a useful tool to investigate the encoding of visual information in different brain regions. Because of their simplicity and their capability in the representation of brain activity, the ERSPs might be used as biomarkers of functional recovery for example in the rehabilitation of visual dysfunction and motor impairment following a stroke, as well as diagnostic tool of anomalies in brain functions in neurological diseases tailored to personalized treatments in clinical environment.

Distributing entanglement in first-generation discrete- and continuous-variable quantum repeaters

Quantum repeaters are used to overcome the exponential photon loss scaling that quantum states acquire as they are transmitted over long distances. While repeaters for discrete-variable encodings of quantum information have existed for some time, approaches for continuous-variable encoding quantum repeaters have only recently been proposed. In this work, we present a method of using a discrete-variable repeater protocol to distribute continuous-variable states and utilize it to compare the rates of continuous-variable entanglement distribution between first-generation continuous- and discrete-variable quantum repeaters. Such a comparison allows us to begin to benchmark the two quite different approaches.

Temporal Dynamics of Competition between Statistical Learning and Episodic Memory in Intracranial Recordings of Human Visual Cortex.

The function of long-term memory is not just to reminisce about the past, but also to make predictions that help us behave appropriately and efficiently in the future. This predictive function of memory provides a new perspective on the classic question from memory research of why we remember some things but not others. If prediction is a key outcome of memory, then the extent to which an item generates a prediction signifies that this information already exists in memory and need not be encoded. We tested this principle using human intracranial EEG as a time-resolved method to quantify prediction in visual cortex during a statistical learning task and link the strength of these predictions to subsequent episodic memory behavior. Epilepsy patients of both sexes viewed rapid streams of scenes, some of which contained regularities that allowed the category of the next scene to be predicted. We verified that statistical learning occurred using neural frequency tagging and measured category prediction with multivariate pattern analysis. Although neural prediction was robust overall, this was driven entirely by predictive items that were subsequently forgotten. Such interference provides a mechanism by which prediction can regulate memory formation to prioritize encoding of information that could help learn new predictive relationships.SIGNIFICANCE STATEMENT When faced with a new experience, we are rarely at a loss for what to do. Rather, because many aspects of the world are stable over time, we rely on past experiences to generate expectations that guide behavior. Here we show that these expectations during a new experience come at the expense of memory for that experience. From intracranial recordings of visual cortex, we decoded what humans expected to see next in a series of photographs based on patterns of neural activity. Photographs that generated strong neural expectations were more likely to be forgotten in a later behavioral memory test. Prioritizing the storage of experiences that currently lead to weak expectations could help improve these expectations in future encounters.

Decoding of speech information using EEG in children with dyslexia: Less accurate low-frequency representations of speech, not “Noisy” representations

The amplitude envelope of speech carries crucial low-frequency acoustic information that assists linguistic decoding. The sensory-neural Temporal Sampling (TS) theory of developmental dyslexia proposes atypical encoding of speech envelope information < 10 Hz, leading to atypical phonological representations. Here a backward linear TRF model and story listening were employed to estimate the speech information encoded in the electroencephalogram in the canonical delta, theta and alpha bands by 9-year-old children with and without dyslexia. TRF decoding accuracy provided an estimate of how faithfully the children’s brains encoded low-frequency envelope information. Between-group analyses showed that the children with dyslexia exhibited impaired reconstruction of speech information in the delta band. However, when the quality of speech encoding for each child was estimated using child-by-child decoding models, then the dyslexic children did not differ from controls. This suggests that children with dyslexia encode neither “noisy” nor “normal” representations of the speech signal, but different representations.

Non-orthogonal polarization encoding/decoding assisted by structured optical pattern recognition.

The complex vector beams yield up an abundance of polarization information that has not yet been well utilized in information encoding. In this paper, we propose a polarization encoding scheme with the non-orthogonal polarization states using a stationary vector beam. Recognizing those non-orthogonal polarization states is assisted by the structured patterns of the single vector beams under different polarization projections. We show that one can achieve different capacities of encoding bits by changing the step of the polarization angle with the single vector beam. We also demonstrate the non-orthogonal polarization encoding scheme can be well decoded with the machine learning classification algorithm. A 64×64 gray image is successfully transmitted by using 4 bits/symbol encoding-decoding scheme with 99.94 % transmission accuracy. Besides, by extending the encoding-decoding scheme to 8 bits/symbol based on the same single vector beam, we achieve a higher transmission rate with 65.58% transmission accuracy. Our work holds promise for small-angle non-orthogonal polarization encoding for free-space optical communications.

Data-Driven Models of Efficient Chromatic Coding in the Outer Retina.

Recent experimental work on zebrafish has shown the in vivo activity of photoreceptors and horizontal cells (HCs) as a function of the stimulus spectrum, highlighting the appearance of chromatic-opponent signals at their first synaptic connection. Altogether with the observed lack of excitatory intercone connections, these findings suggest that the mechanism yielding early color opponency in zebrafish is dominated by inhibitory feedback. We propose a neuronal population model based on zebrafish retinal circuitry to investigate whether networks with predominantly inhibitory feedback are more advantageous in encoding chromatic information than networks with mixed excitatory and inhibitory mechanisms. We show that networks with dominant inhibitory feedback exhibit a unique and reliable encoding of chromatic information. In contrast, this property is not guaranteed in networks with strong excitatory intercone connections, exhibiting bistability. These findings provide a theoretical explanation for the absence of excitatory intercone couplings in zebrafish color circuits. In addition, our study shows that these networks, with only one type of horizontal cell, are suitable to encode most of the variance from the zebrafish environment. However, at least two successive layers of inhibitory neurons are needed to reach the optimum. Finally, we contrast the encoding performance of networks with different opsin sensitivities, showing an improvement of only 13% compared with zebrafish, suggesting that the zebrafish retina is adapted to encode color information from its habitat efficiently.

Suppressed prefrontal neuronal firing variability and impaired social representation in IRSp53-mutant mice

Social deficit is a major feature of neuropsychiatric disorders, including autism spectrum disorders, schizophrenia, and attention-deficit/hyperactivity disorder, but its neural mechanisms remain unclear. Here, we examined neuronal discharge characteristics in the medial prefrontal cortex (mPFC) of IRSp53/Baiap2-mutant mice, which show social deficits, during social approach. We found a decrease in the proportion of IRSp53-mutant excitatory mPFC neurons encoding social information, but not that encoding non-social information. In addition, the firing activity of IRSp53-mutant neurons was less differential between social and non-social targets. IRSp53-mutant excitatory mPFC neurons displayed an increase in baseline neuronal firing, but decreases in the variability and dynamic range of firing as well as burst firing during social and non-social target approaches compared to wild-type controls. Treatment of memantine, an NMDA receptor antagonist that rescues social deficit in IRSp53-mutant mice, alleviates the reduced burst firing of IRSp53-mutant pyramidal mPFC neurons. These results suggest that suppressed neuronal activity dynamics and burst firing may underlie impaired cortical encoding of social information and social behaviors in IRSp53-mutant mice.

Guest Editorial: IEEE TC Special Issue On Software, Hardware and Applications for Neuromorphic Computing

The papers in this special section focus on software, hardware, and computer applications for neuromorphic computing. Inspired by biological neural systems, neuromorphic computing has drawn much attention for its great potential of achieving machine intelligence at extremely low energy dissipation. Bio-inspired computing models have been investigated for information encoding, sparse representation, event driven communication/computation, and online learning. This new computing paradigm triggered a recent wave of innovations in software and hardware architecture and emerging device technology, which consequently enabled many novel applications. This is an exemplar of research area where the application, computing model, architecture and circuit level design are tightly coupled to deliver unprecedented functionality and energy efficiency.