Neuroscience Driven Visual Representation
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Neuroscience-driven Visual Representation
Visual representation learning enables computers or systems to simulate the function of retinas, optic nerves, and visual cortex in the human brain, and derive meaningful information from digital images, videos, and other visual inputs. To learn the effective presentation of visual data is essential for many computer vision and artificial intelligence applications ranging from energy and utilities to manufacturing and automotive. Current popular deep learning-based visual representation learning methods do not fully consider the nature of the biological visual nervous system and are lack in interpretability. To solve visual representation well, the integration of psychological or neuroscientific approaches is required to enhance the cognition of visual data.
The Cognitive Neuroscience of Visual Working Memory
Visual working memory allows us to temporarily maintain and manipulate visual information in order to solve a task. The study of the brain mechanisms underlying this function began more than a half century ago, with Scoville and Milner’s (1957) seminal discoveries with amnesic patients. This timely collection of papers brings together diverse perspectives on the cognitive neuroscience of visual working memory from multiple fields that have traditionally been fairly disjointed: human neuroimaging, electrophysiological, behavioural and animal lesion studies, investigating both the developing and the adult brain.
Dialogues in Music Therapy and Music Neuroscience: Collaborative Understanding Driving Clinical Advances
Music is a complex, dynamic stimulus with an un-paralleled ability to stimulate a global network of neural activity involved in attention, emotion, memory, communication, motor co-ordination and cognition. As such, it provides neuroscience with a highly effective tool to develop our understanding of brain function, connectivity and plasticity. Increasingly sophisticated neuroimaging technologies have enabled the expanding field of music neuroscience to reveal how musical experience, perception and cognition may support neuroplasticity, with important implications for the rehabilitation and assessment of those with acquired brain injuries and neurodegenerative conditions. Other studies have indicated the potential for music to support arousal, attention and emotional regulation, suggesting therapeutic applications for conditions including ADHD, PTSD, autism, learning disorders and mood disorders. In common with neuroscience, the music therapy profession has advanced significantly in the past 20 years. Various interventions designed to address functional deficits and health care needs have been developed, alongside standardised behavioural assessments. Historically, music therapy has drawn its evidence base from a number of contrasting theoretical frameworks. Clinicians are now turning to neuroscience, which offers a unifying knowledge base and frame of reference to understand and measure therapeutic interventions from a biomedical perspective. Conversely, neuroscience is becoming more enriched by learning about the neural effects of ‘real world’ clinical applications in music therapy. While neuroscientific imaging methods may provide biomarking evidence for the efficacy of music therapy interventions it also offers important tools to describe time-locked interactive therapy processes and feeds into the emerging field of social neuroscience. Music therapy is bound to the process of creating and experiencing music together in improvisation, listening and reflection. Thus the situated cognition and experience of music developing over time and in differing contexts is of interest in time series data. We encouraged researchers to submit papers illustrating the mutual benefits of dialogue between music therapy and other disciplines important to this field, particularly neuroscience, neurophysiology, and neuropsychology. The current eBook consists of the peer reviewed responses to our call for papers.