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.

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%.

Neural modulation technology and the capability to affect organ function have spawned the new field of bioelectronic medicine. Therapeutic interventions depend on wireless bioelectronic neural interfaces that can conformally and easily attach to small (few hundred micrometers) nerves located deep in the body without neural damage. Besides size, factors like flexibility and compliance to attach and adapt to visceral nerves associated moving organs are of paramount importance and have not been previously addressed. This study proposes a novel flexible neural clip (FNC) that can be used to interface with a variety of different peripheral nerves. To illustrate the flexibility of the design, this study stimulates the pelvic nerve, the vagus nerve, and branches of the sciatic nerve and evaluates the feasibility of the design in modulating the function of each of these nerves. It is found that this FNC allows fine‐tuning of physiological processes such as micturition, heart rate, and muscle contractions. Furthermore, this study also tests the ability of wirelessly powered FNC to enable remote modulation of visceral pelvic nerves located deep in the body. These results show that the FNC can be used with a range of different nerves, providing one of the critical pieces in the field of bioelectronics medicines.