A universal interface for plug-and-play assembly of stretchable devices.

Stretchable hybrid devices have enabled high-fidelity implantable1-3 and on-skin4-6 monitoring of physiological signals. These devices typically contain soft modules that match the mechanical requirements in humans7,8 and soft robots9,10, rigid modules containing Si-based microelectronics11,12 and protective encapsulation modules13,14. To make such a system mechanically compliant, the interconnects between the modules need to tolerate stress concentration that may limit their stretching and ultimately cause debonding failure15-17. Here, we report a universal interface that can reliably connect soft, rigid and encapsulation modules together to form robust and highly stretchable devices in a plug-and-play manner. The interface, consisting of interpenetrating polymer and metal nanostructures, connects modules by simply pressing without using pastes. Its formation is depicted by a biphasic network growth model. Soft-soft modules joined by this interface achieved 600% and 180% mechanical and electrical stretchability, respectively. Soft and rigid modules can also be electrically connected using the above interface. Encapsulation on soft modules with this interface is strongly adhesive with an interfacial toughness of 0.24 N mm-1. As a proof of concept, we use this interface to assemble stretchable devices for in vivo neuromodulation and on-skin electromyography, with high signal quality and mechanical resistance. We expect such a plug-and-play interface to simplify and accelerate the development of on-skin and implantable stretchable devices.

A nonlinear hidden layer enables actor-critic agents to learn multiple paired association navigation.

Navigation to multiple cued reward locations has been increasingly used to study rodent learning. Though deep reinforcement learning agents have been shown to be able to learn the task, they are not biologically plausible. Biologically plausible classic actor-critic agents have been shown to learn to navigate to single reward locations, but which biologically plausible agents are able to learn multiple cue-reward location tasks has remained unclear. In this computational study, we show versions of classic agents that learn to navigate to a single reward location, and adapt to reward location displacement, but are not able to learn multiple paired association navigation. The limitation is overcome by an agent in which place cell and cue information are first processed by a feedforward nonlinear hidden layer with synapses to the actor and critic subject to temporal difference error-modulated plasticity. Faster learning is obtained when the feedforward layer is replaced by a recurrent reservoir network.

Assessment of Aphasia Across the International Classification of Functioning, Disability and Health Using an iPad-Based Application.

BACKGROUND: Access2Aphasia™ is an iPad™-based aphasia assessment application that enables real-time audiovisual communication between people with aphasia (PWA) and speech-language pathologists (SLPs), and the use of supported conversation techniques. This study aimed to establish the reliability of aphasia assessment across the International Classification of Functioning, Disability and Health (ICF) using Access2Aphasia, and compare it with face-to-face (FTF) assessment. Consumer perspectives of Access2Aphasia were also examined. MATERIALS AND METHODS: Thirty PWA were randomized into two conditions: online-led and FTF assessment. Participants in the online-led group were assessed remotely using Access2Aphasia™ in their own homes, while an FTF SLP scored silently simultaneously. Participants in the FTF group were assessed FTF using standard administration materials. Assessment included two subtests of the Psycholinguistic Assessment of Language Processing Activities (PALPA) and the Assessment of Living with Aphasia (ALA) to allow for outcomes to be captured across the ICF domains. Consumer perspectives on Access2Aphasia were obtained from both PWA and research SLPs in the online-led group. RESULTS: Kappa statistics indicated moderate to almost perfect agreement between online and FTF SLPs (k = 0.71-1.00). Intrarater and interrater reliability was excellent (ICC = 0.99-1.00) and equivalent for the online-led and FTF conditions. Both PWA and research SLPs in the online-led group reported being satisfied with the experience overall, with suggestions provided by research SLPs to improve Access2Aphasia. CONCLUSION: This study supports the provision of iPad-based aphasia assessments across the ICF in the online environment, with comparable reliability to FTF assessments. Future research is warranted to support the development of iPad-based aphasia assessment and treatment as an alternative mode of service delivery to PWA.

Self-organization of "fibro-axonal" composite tissue around unmodified metallic micro-electrodes can form a functioning interface with a peripheral nerve: A new direction for creating long-term neural interfaces.

INTRODUCTION: A long-term peripheral neural interface is an area of intense research. The use of electrode interfaces is limited by the biological response to the electrode material. METHODS: We created an electrode construct to harbor the rat sciatic nerve with interposition of autogenous adipose tissue between the nerve and the electrode. The construct was implanted for 10 weeks. RESULTS: Immunohistochemistry showed a unique laminar pattern of axonal growth layered between fibro-collagenous tissue, forming a physical interface with the tungsten micro-electrode. Action potentials transmitted across the intrerface showed mean conduction velocities varying between 6.99 ± 2.46 and 20.14 ± 4 m/s. CONCLUSIONS: We have demonstrated the feasibility of a novel peripheral nerve interface through modulation of normal biologic phenomena. It has potential applications as a chronic implantable neural interface.

Role of Muscle Synergies in Real-Time Classification of Upper Limb Motions using Extreme Learning Machines.

BACKGROUND: Myoelectric signals offer significant insights in interpreting the motion intention and extent of effort involved in performing a movement, with application in prostheses, orthosis and exoskeletons. Feature extraction plays a vital role, and follows two approaches: EMG and synergy features. More recently, muscle synergy based features are being increasingly explored, since it simplifies dimensionality of control, and are considered to be more robust to signal variations. Another important aspect in a myoelectrically controlled devices is the learning capability and speed of performance for online decoding. Extreme learning machine (ELM) is a relatively new neural-network based learning algorithm: its performance hasn't been explored in the context of online control, which is a more reliable measure compared to offline analysis. To this purpose we aim at focusing our investigation on a myoelectric-based interface which is able to identify and online classify, upper limb motions involving shoulder and elbow. The main objective is to compare the performance of the decoder trained using ELM, for two different features: EMG and synergy features. METHODS: The experiments are broadly divided in two phases training/calibration and testing respectively. ELM is used to train the decoder using data acquired during the calibration phase. The performance of the decoder is then tested in online motion control by using a simulated graphical user interface replicating the human limb: subjects are requested to control a virtual arm by using their muscular activity. The decoder performance is quantified using ad-hoc metrics based on the following indicators: motion selection time, motion completion time, and classification accuracy. RESULTS: Performance has been evaluated for both offline and online contexts. The offline classification results indicated better performance in the case of EMG features, whereas a better classification accuracy for synergy feature was observed for online decoding. Also the other indicators as motion selection time and motion completion time, showed better trend in the case of synergy than time-domain features. CONCLUSION: This work demonstrates better robustness of online decoding of upper-limb motions and motor intentions when using synergy feature. Furthermore, we have quantified the performance of the decoder trained using ELM for online control, providing a potential and viable option for real-time myoelectric control in assistive technology.

Singapore Tele-technology Aided Rehabilitation in Stroke (STARS) trial: protocol of a randomized clinical trial on tele-rehabilitation for stroke patients.

BACKGROUND: Most acute stroke patients with disabilities do not receive recommended rehabilitation following discharge to the community. Functional and social barriers are common reasons for non-adherence to post-discharge rehabilitation. Home rehabilitation is an alternative to centre-based rehabilitation but is costlier. Tele-rehabilitation is a possible solution, allowing for remote supervision of rehabilitation and eliminating access barriers. The objective of the Singapore Tele-technology Aided Rehabilitation in Stroke (STARS) trial is to determine if a novel tele-rehabilitation intervention for the first three months after stroke admission improves functional recovery compared to usual care. METHODS/DESIGN: This is a single blind (evaluator blinded), parallel, two-arm randomised controlled trial study design involving 100 recent stroke patients. The inclusion criteria are age ≥40 years, having caregiver support and recent stroke defined as stroke diagnosis within 4 weeks. Consenting participants will be randomized with varying block size of 4 or 6 assuming a 1:1 treatment allocation with the participating centre as the stratification factor. The baseline assessment will be done within 4 weeks of stroke onset, followed by follow-up assessments at 3 and 6 months. The tele-rehabilitation intervention lasts for 3 months and includes exercise 5-days-a-week using an iPad-based system that allows recording of daily exercise with video and sensor data and weekly video-conferencing with tele-therapists after data review. Those allocated to the control group will receive usual care. The primary outcome measure is improvement in life task's social activity participation at three months as measured by the disability component of the Jette Late Life Functional and Disability Instrument (LLFDI). Secondary outcome variables consist of gait speed (Timed 5-Meter Walk Test) and endurance (Two-Minute Walk test), performance of basic activities of daily living (Shah-modified Barthel Index), balance confidence (Activities-Specific Balance Confidence Scale), patient self-reported health-related quality-of-life [Euro-QOL (EQ-5D)], health service utilization (Singapore Stroke Study Health Service Utilization Form) and caregiver reported stress (Zarit Caregiver Burden Inventory). DISCUSSION: The goal of this trial is to provide evidence on the potential benefit and cost-effectiveness of this novel tele-rehabilitation programme which will guide health care decision-making and potentially improve performance of post-stroke community-based rehabilitation. TRIAL REGISTRATION: This trial protocol was registered under ClinicalTrials.gov on 18 July 2013 as study title "The Singapore Tele-technology Aided Rehabilitation in Stroke (STARS) Study" (ID: The STARS Study, ClinicalTrials.gov Identifier: NCT01905917 ).

Direct vagus nerve stimulation: A new tool to control allergic airway inflammation through α7 nicotinic acetylcholine receptor.

BACKGROUND AND PURPOSE: Asthma is characterized by airway inflammation, mucus hypersecretion, and airway hyperresponsiveness. The use of nicotinic agents to mimic the cholinergic anti-inflammatory pathway (CAP) controls experimental asthma. Yet, the effects of vagus nerve stimulation (VNS)-induced CAP on allergic inflammation remain unknown. EXPERIMENTAL APPROACH: BALB/c mice were sensitized and challenged with house dust mite (HDM) extract and treated with active VNS (5 Hz, 0.5 ms, 0.05-1 mA). Bronchoalveolar lavage (BAL) fluid was assessed for total and differential cell counts and cytokine levels. Lungs were examined by histopathology and electron microscopy. KEY RESULTS: In the HDM mouse asthma model, VNS at intensities equal to or above 0.1 mA (VNS 0.1) but not sham VNS reduced BAL fluid differential cell counts and alveolar macrophages expressing α7 nicotinic receptors (α7nAChR), goblet cell hyperplasia, and collagen deposition. Besides, VNS 0.1 also abated HDM-induced elevation of type 2 cytokines IL-4 and IL-5 and was found to block the phosphorylation of transcription factor STAT6 and expression level of IRF4 in total lung lysates. Finally, VNS 0.1 abrogated methacholine-induced hyperresponsiveness in asthma mice. Prior administration of α-bungarotoxin, a specific inhibitor of α7nAChR, but not propranolol, a specific inhibitor of ß2-adrenoceptors, abolished the therapeutic effects of VNS 0.1. CONCLUSION AND IMPLICATIONS: Our data revealed the protective effects of VNS on various clinical features in allergic airway inflammation model. VNS, a clinically approved therapy for depression and epilepsy, appears to be a promising new strategy for controlling allergic asthma.

Parallel programming of saccades during natural scene viewing: evidence from eye movement positions.

Previous studies have shown that saccade plans during natural scene viewing can be programmed in parallel. This evidence comes mainly from temporal indicators, i.e., fixation durations and latencies. In the current study, we asked whether eye movement positions recorded during scene viewing also reflect parallel programming of saccades. As participants viewed scenes in preparation for a memory task, their inspection of the scene was suddenly disrupted by a transition to another scene. We examined whether saccades after the transition were invariably directed immediately toward the center or were contingent on saccade onset times relative to the transition. The results, which showed a dissociation in eye movement behavior between two groups of saccades after the scene transition, supported the parallel programming account. Saccades with relatively long onset times (>100 ms) after the transition were directed immediately toward the center of the scene, probably to restart scene exploration. Saccades with short onset times (<100 ms) moved to the center only one saccade later. Our data on eye movement positions provide novel evidence of parallel programming of saccades during scene viewing. Additionally, results from the analyses of intersaccadic intervals were also consistent with the parallel programming hypothesis.

Assessing Elevated Blood Glucose Levels Through Blood Glucose Evaluation and Monitoring Using Machine Learning and Wearable Photoplethysmography Sensors: Algorithm Development and Validation.

BACKGROUND: Diabetes mellitus is the most challenging and fastest-growing global public health concern. Approximately 10.5% of the global adult population is affected by diabetes, and almost half of them are undiagnosed. The growing at-risk population exacerbates the shortage of health resources, with an estimated 10.6% and 6.2% of adults worldwide having impaired glucose tolerance and impaired fasting glycemia, respectively. All current diabetes screening methods are invasive and opportunistic and must be conducted in a hospital or laboratory by trained professionals. At-risk participants might remain undetected for years and miss the precious time window for early intervention to prevent or delay the onset of diabetes and its complications. OBJECTIVE: We aimed to develop an artificial intelligence solution to recognize elevated blood glucose levels (≥7.8 mmol/L) noninvasively and evaluate diabetic risk based on repeated measurements. METHODS: This study was conducted at KK Women's and Children's Hospital in Singapore, and 500 participants were recruited (mean age 38.73, SD 10.61 years; mean BMI 24.4, SD 5.1 kg/m2). The blood glucose levels for most participants were measured before and after consuming 75 g of sugary drinks using both a conventional glucometer (Accu-Chek Performa) and a wrist-worn wearable. The results obtained from the glucometer were used as ground-truth measurements. We performed extensive feature engineering on photoplethysmography (PPG) sensor data and identified features that were sensitive to glucose changes. These selected features were further analyzed using an explainable artificial intelligence approach to understand their contribution to our predictions. RESULTS: Multiple machine learning models were trained and assessed with 10-fold cross-validation, using participant demographic data and critical features extracted from PPG measurements as predictors. A support vector machine with a radial basis function kernel had the best detection performance, with an average accuracy of 84.7%, a sensitivity of 81.05%, a specificity of 88.3%, a precision of 87.51%, a geometric mean of 84.54%, and F score of 84.03%. CONCLUSIONS: Our findings suggest that PPG measurements can be used to identify participants with elevated blood glucose measurements and assist in the screening of participants for diabetes risk.

Natural movies evoke spike trains with low spike time variability in cat primary visual cortex.

Neuronal responses in primary visual cortex have been found to be highly variable. This has led to the widespread notion that neuronal responses have to be averaged over large numbers of neurons to obtain suitably invariant responses that can be used to reliably encode or represent external stimuli. However, it is possible that the high variability of neuronal responses may result from the use of simple, artificial stimuli and that the visual cortex may respond differently to dynamic, naturalistic images. To investigate this question, we recorded the responses of primary visual cortical neurons in the anesthetized cat under stimulation with time-varying natural movies. We found that cortical neurons on the whole exhibited a high degree of spike count variability, but a surprisingly low degree of spike time variability. The spike count variability was further reduced when all but the first spike in a burst were removed. We also found that responses exhibiting low spike time variability exhibited low spike count variability, suggesting that rate coding and temporal coding might be more compatible than previously thought. In addition, we found the spike time variability to be significantly lower when stimulated by natural movies as compared with stimulation using drifting gratings. Our results indicate that response variability in primary visual cortex is stimulus dependent and significantly lower than previous measurements have indicated.

Tutorial: a guide to techniques for analysing recordings from the peripheral nervous system.

The nervous system, through a combination of conscious and automatic processes, enables the regulation of the body and its interactions with the environment. The peripheral nervous system is an excellent target for technologies that seek to modulate, restore or enhance these abilities as it carries sensory and motor information that most directly relates to a target organ or function. However, many applications require a combination of both an effective peripheral nerve interface (PNI) and effective signal processing techniques to provide selective and stable recordings. While there are many reviews on the design of PNIs, reviews of data analysis techniques and translational considerations are limited. Thus, this tutorial aims to support new and existing researchers in the understanding of the general guiding principles, and introduces a taxonomy for electrode configurations, techniques and translational models to consider.

Detection of Freezing of Gait Using Convolutional Neural Networks and Data From Lower Limb Motion Sensors.

Parkinson's disease (PD) is a chronic, non-reversible neurodegenerative disorder, and freezing of gait (FOG) is one of the most disabling symptoms in PD as it is often the leading cause of falls and injuries that drastically reduces patients' quality of life. In order to monitor continuously and objectively PD patients who suffer from FOG and enable the possibility of on-demand cueing assistance, a sensor-based FOG detection solution can help clinicians manage the disease and help patients overcome freezing episodes. Many recent studies have leveraged deep learning models to detect FOG using signals extracted from inertial measurement unit (IMU) devices. Usually, the latent features and patterns of FOG are discovered from either the time or frequency domain. In this study, we investigated the use of the time-frequency domain by applying the Continuous Wavelet Transform to signals from IMUs placed on the lower limbs of 63 PD patients who suffered from FOG. We built convolutional neural networks to detect the FOG occurrences, and employed the Bayesian Optimisation approach to obtain the hyper-parameters. The results showed that the proposed subject-independent model was able to achieve a geometric mean of 90.7% and a F1 score of 91.5%.

STEER: 3D Printed Guide for Nerve Regrowth Control and Neural Interface in Non-Human Primate Model.

OBJECTIVE: Peripheral neural interface (PNI) with a stable integration of synthetic elements with neural tissue is key for successfulneuro-prosthetic applications. An inevitable phenomenon of reactive fibrosis is a primary hurdle for long term functionality of PNIs. This proof-of-concept study aimed to fabricate and test a novel, stable PNI that harnesses fibro-axonal outgrowth at the nerve end and includes fibrosis in the design. METHODS: Two non-human primates were implanted with Substrate-guided, Tissue-Electrode Encapsulation and Integration (STEER) PNIs. The implant included a 3D printed guide that strove to steer the regrowing nerve towards encapsulation of the electrodes into a fibro-axonal tissue. After four months from implantation, we performed electrophysiological measurements to test STEER's functionality and examined the macro and micro- morphology of the outgrowth tissue. RESULTS: We observed a highly structured fibro-axonal composite within the STEER PNI. A conduction of intracranially generated action potentials was successfully recorded across the neural interface. Immunohistology demonstrated uniquely configured laminae of myelinated axons encasing the implant. CONCLUSION: STEER PNI reconfigured the structure of the fibro-axonal tissue and facilitated long-term functionality and stability of the neural interface. SIGNIFICANCE: The results point to the feasibility of our concept for creating a stable PNI with long-term electrophysiologic functionality by using simple design and materials.

Artemisia argyi exhibits anti-aging effects through decreasing the senescence in aging stem cells.

Aging is accompanied by functional loss of many cellular pathways, creating an increased risk of many age-related complications (ARC). Aging causes stem cell exhaustion with a concomitant increase in cellular dysfunction. Recently, interest in senotherapeutics has been growing rapidly to promote healthy aging and as an intervention for ARCs. This research focused on screening the senomorphic properties of Artemisia argyi, as an emerging strategy for longevity, and prevention or treatment of ARCs. In this study, we aimed to find the clinical efficacy of daily consumption of Artemisia argyi water extract (AAW) on aging. In vitro 0.1µM Doxorubicin induced senescent human adipose derived mesenchymal stem cells was treated with different concentrations of AAW to show its anti-aging effect. 15 months old SHR rats (n=6) were treated with 7.9 mg/ml AAW for 4 weeks and anti-aging effect was evaluated. In vitro study showed the protective effect of AAW in telomere shortening and helps in maintaining a balance in the expression of anti-aging protein Klotho and TERT. AAW effectively reduced mitochondrial superoxide and also provided a protective shield against senescence markers like over-expression of p21 and formation of double strand breaks, which is known to cause premature aging. Moreover, animal studies indicated that AAW promoted the expression of Klotho in naturally aging rats. In addition, AAW successfully restored the decline cardiac function and improved the grip strength and memory of aging rat. These findings showed that therapeutic targeting of senescent stem cells by AAW restored stem cell homeostasis and improves overall health.

Minimally dependent activity subspaces for working memory and motor preparation in the lateral prefrontal cortex.

The lateral prefrontal cortex is involved in the integration of multiple types of information, including working memory and motor preparation. However, it is not known how downstream regions can extract one type of information without interference from the others present in the network. Here, we show that the lateral prefrontal cortex of non-human primates contains two minimally dependent low-dimensional subspaces: one that encodes working memory information, and another that encodes motor preparation information. These subspaces capture all the information about the target in the delay periods, and the information in both subspaces is reduced in error trials. A single population of neurons with mixed selectivity forms both subspaces, but the information is kept largely independent from each other. A bump attractor model with divisive normalization replicates the properties of the neural data. These results provide new insights into neural processing in prefrontal regions.

Convolutional Neural Network for Freezing of Gait Detection Leveraging the Continuous Wavelet Transform on Lower Extremities Wearable Sensors Data.

Freezing of Gait is the most disabling gait disturbance in Parkinson's disease. For the past decade, there has been a growing interest in applying machine learning and deep learning models to wearable sensor data to detect Freezing of Gait episodes. In our study, we recruited sixty-seven Parkinson's disease patients who have been suffering from Freezing of Gait, and conducted two clinical assessments while the patients wore two wireless Inertial Measurement Units on their ankles. We converted the recorded time-series sensor data into continuous wavelet transform scalograms and trained a Convolutional Neural Network to detect the freezing episodes. The proposed model achieved a generalisation accuracy of 89.2% and a geometric mean of 88.8%.

A 3-Mbps, 802.11g-Based EMG Recording System With Fully Implantable 5-Electrode EMG Acquisition Device.

We have developed a 5-electrode recording system that combines an implantable electromyography (EMG) device package with transcutaneous inductive power transmission, near-infrared (NIR) transcutaneous data telemetry and 3 Mbps Wi-Fi data acquisition for chronic EMG recording in vivo. This system comprises a hermetically-sealed single-chip, 5-electrode Implantable EMG Acquisition Device (IEAD), a custom external powering and Implant Telemetry Module (ITM), and a custom Wi-Fi-based Raspberry Pi-based Data Acquisition (RaspDAQ) and relay device. The external unit (ITM and RaspDAQ) is powered entirely by a single battery to achieve the objective of untethered EMG recording, for the convenience of clinicians and animal researchers. The IEAD acquires intramuscular EMG signals at 17.85 ksps/electrode while being powered transcutaneously by the ITM using 22 MHz near-field inductive coupling. The acquired EMG data is transmitted transcutaneously via NIR telemetry to the ITM, which in turn, transfers the data to the RaspDAQ for relaying to a laptop computer for display and storage. We have also validated the complete system by acquiring EMG signals from rodents for up to two months. Following the explantation of the devices, we have also conducted failure and histological analysis on the devices and the surrounding tissue, respectively.

Nanotunnels within Poly(3,4-ethylenedioxythiophene)-Carbon Nanotube Composite for Highly Sensitive Neural Interfacing.

Neural electrodes are developed for direct communication with neural tissues for theranostics. Although various strategies have been employed to improve performance, creating an intimate electrode-tissue interface with high electrical fidelity remains a great challenge. Here, we report the rational design of a tunnel-like electrode coating comprising poly(3,4-ethylenedioxythiophene) (PEDOT) and carbon nanotubes (CNTs) for highly sensitive neural recording. The coated electrode shows a 50-fold reduction in electrochemical impedance at the biologically relevant frequency of 1 kHz, compared to the bare gold electrode. The incorporation of CNT significantly reinforces the nanotunnel structure and improves coating adhesion by ∼1.5 fold. In vitro primary neuron culture confirms an intimate contact between neurons and the PEDOT-CNT nanotunnel. During acute in vivo nerve recording, the coated electrode enables the capture of high-fidelity neural signals with low susceptibility to electrical noise and reveals the potential for precisely decoding sensory information through mechanical and thermal stimulation. These findings indicate that the PEDOT-CNT nanotunnel composite serves as an active interfacing material for neural electrodes, contributing to neural prosthesis and brain-machine interface.

Spatial and temporal jitter distort estimated functional properties of visual sensory neurons.

The functional properties of neural sensory cells or small neural ensembles are often characterized by analyzing response-conditioned stimulus ensembles. Many widely used analytical methods, like receptive fields (RF), Wiener kernels or spatio-temporal receptive fields (STRF), rely on simple statistics of those ensembles. They also tend to rely on simple noise models for the residuals of the conditional ensembles. However, in many cases the response-conditioned stimulus set has more complex structure. If not taken explicitly into account, it can bias the estimates of many simple statistics, and lead to erroneous conclusions about the functionality of a neural sensory system. In this article, we consider sensory noise in the visual system generated by small stimulus shifts in two dimensions (2 spatial or 1-space 1-time jitter). We model this noise as the action of a set of translations onto the stimulus that leave the response invariant. The analysis demonstrates that the spike-triggered average is a biased estimator of the model mean, and provides a de-biasing method. We apply this approach to observations from the stimulus/response characteristics of cells in the cat visual cortex and provide improved estimates of the structure of visual receptive fields. In several cases the new estimates differ substantially from the classic receptive fields, to a degree that may require re-evaluation of the functional description of the associated cells.

Time-invariant working memory representations in the presence of code-morphing in the lateral prefrontal cortex.

Maintenance of working memory is thought to involve the activity of prefrontal neuronal populations with strong recurrent connections. However, it was recently shown that distractors evoke a morphing of the prefrontal population code, even when memories are maintained throughout the delay. How can a morphing code maintain time-invariant memory information? We hypothesized that dynamic prefrontal activity contains time-invariant memory information within a subspace of neural activity. Using an optimization algorithm, we found a low-dimensional subspace that contains time-invariant memory information. This information was reduced in trials where the animals made errors in the task, and was also found in periods of the trial not used to find the subspace. A bump attractor model replicated these properties, and provided predictions that were confirmed in the neural data. Our results suggest that the high-dimensional responses of prefrontal cortex contain subspaces where different types of information can be simultaneously encoded with minimal interference.