Integrating data from multiple experiments is common practice in systems neuroscience but it requires inter-experimental variability to be negligible compared to the biological signal of interest. This requirement is rarely fulfilled; systematic changes between experiments can drastically affect the outcome of complex analysis pipelines. Modern machine learning approaches designed to adapt models across multiple data domains offer flexible ways of removing inter-experimental variability where classical statistical methods often fail. While applications of these methods have been mostly limited to single-cell genomics, in this work, we develop a theoretical framework for domain adaptation in systems neuroscience. We implement this in an adversarial optimization scheme that removes inter-experimental variability while preserving the biological signal.

Integrating data from multiple experiments is common practice in systems neuroscience but it requires inter-experimental variability to be negligible compared to the biological signal of interest. This requirement is rarely fulfilled; systematic changes between experiments can drastically affect the outcome of complex analysis pipelines. Modern machine learning approaches designed to adapt models across multiple data domains offer flexible ways of removing inter-experimental variability where classical statistical methods often fail. While applications of these methods have been mostly limited to single-cell genomics, in this work, we develop a theoretical framework for domain adaptation in systems neuroscience. We implement this in an adversarial optimization scheme that removes inter-experimental variability while preserving the biological signal.

Researchers have gained a first insight into how the brain structures higher-level information. By extracting and analysing data from a neural network of grid cells, they found that the collective neural activity is shaped like the surface of a doughnut. The study, from the Norwegian University of Science and Technology's (NTNU) Kavli Institute for Systems Neuroscience and collaborators, is published in Nature, High-level brain functions result from the orchestration of activity between many thousands of neurons in neural networks. For grid cells, these neural network conversations result in our understanding of location, our capacity to navigate, and our mental maps. "This discovery provides one of the first insights into how brain cells operate collectively, as a society. It provides an unprecedented glimpse into how large networks of neurons produce properties that cannot be inferred from the activities of single cells. These collective codes are the clue to all high-level cognitive functions of the brain," said Edvard Moser, a professor of neuroscience and co-director of the Norwegian University of Science and Technology's(NTNU)Kavli Institute for Systems Neuroscience.

Eye movements read by a new AI application can reveal thoughts, memories, goals -- and brain diseases. A new tool developed at the Kavli Institute for Systems Neuroscience in Norway and described in an article in Nature Neuroscience, predicts gaze direction and eye movement directly from magnetic resonance imaging (MRI) scans. The goal is to make eye tracking diagnostics a standard in brain imaging research and hospital clinics. Whenever you explore an environment or search for something, you scan the scene using continuous rapid eye movements. Your eyes also make short stops to fixate on certain elements of the scene that you want more detailed information about.

An artificial neural network (AI) designed by an international team involving UCL can translate raw data from brain activity, paving the way for new discoveries and a closer integration between technology and the brain. The new method could accelerate discoveries of how brain activities relate to behaviors. The study published today in eLife, co-led by the Kavli Institute for Systems Neuroscience in Trondheim and the Max Planck Institute for Human Cognitive and Brain Sciences Leipzig and funded by Wellcome and the European Research Council, shows that a convolutional neural network, a specific type of deep learning algorithm, is able to decode many different behaviors and stimuli from a wide variety of brain regions in different species, including humans. Lead researcher Markus Frey (Kavli Institute for Systems Neuroscience), said, "Neuroscientists have been able to record larger and larger datasets from the brain but understanding the information contained in that data--reading the neural code--is still a hard problem. In most cases we don't know what messages are being transmitted. "We wanted to develop an automatic method to analyze raw neural data of many different types, circumventing the need to manually decipher them." They tested the network, called DeepInsight, on neural signals from rats exploring an open arena and found it was able to precisely predict the position, head direction, and running speed of the animals. Even without manual processing, the results were more accurate than those obtained with conventional analyses. Senior author Professor Caswell Barry (UCL Cell & Developmental Biology), said, "Existing methods miss a lot of potential information in neural recordings because we can only decode the elements that we already understand.

Studies have shown that wealthy people live longer, but new research suggests it may be their novel experiences that makes them believe they do. A team at the Norwegian University of Science and Kavli Institute for Systems Neuroscience discovered a network of brain cells that expresses our sense of time within experiences and memories. The team found that enjoyable experiences, such as vacations and hobbies, create'time codes' in the brain that are more memorable and are easier to recall than events that are boring – making it seem we have been on the Earth longer. On the other hand, their work also shows that the brain typically does not stamp events that are mundane or constantly repeated, leaving us less to look back on. Researchers suggest that when you recall on a memory where you whisked away to a tropical island or spent an afternoon tinkering on a vintage car, life'feels longer in retrospect.'

Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to design computational systems based on the tasks they will have to solve. In artificial neural networks, the three components specified by design are the objective functions, the learning rules and the architectures. With the growing success of deep learning, which utilizes brain-inspired architectures, these three designed components have increasingly become central to how we model, engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience.

Researchers have discovered how our brain keeps track of time. They say a special network of brain cells expresses our sense of time within experiences and memories. It essentially provides timestamps for events, and keeps track of the order of them - rather like a filing system. The illustration shows the episodic time from the experience of a 4-hour-long ski trip up and down a steep mountain, including events that alter the skier's perception of time. The idea is that experienced time is event-dependent and may be perceived as faster or slower than clock time.The newly discovered neural record of experienced time is in the lateral entorhinal cortex (LEC) in green.