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This is the first comprehensive teaching resource and textbook for the teaching of Matlab in the Neurosciences and in Psychology. Matlab is unique in that it can be used to learn the entire empirical and experimental process, including stimulus generation, experimental control, data collection, data analysis and modeling. Thus a wide variety of computational problems can be addressed in a single programming environment. The idea is to empower advanced undergraduates and beginning graduate students by allowing them to design and implement their own analytical tools. As students advance in their research careers, they will have achieved the fluency required to understand and adapt more specialized tools as opposed to treating them as \"black boxes\".

MATLAB for Neuroscientists serves as the only complete study manual and teaching resource for MATLAB, the globally accepted standard for scientific computing, in the neurosciences and psychology. This unique introduction can be used to learn the entire empirical and experimental process (including stimulus generation, experimental control, data collection, data analysis, modeling, and more), and the 2nd Edition continues to ensure that a wide variety of computational problems can be addressed in a single programming environment. This updated edition features additional material on the creation of visual stimuli, advanced psychophysics, analysis of LFP data, choice probabilities, synchrony, and advanced spectral analysis. Users at a variety of levels—advanced undergraduates, beginning graduate students, and researchers looking to modernize their skills—will learn to design and implement their own analytical tools, and gain the fluency required to meet the computational needs of neuroscience practitioners. The first complete volume on MATLAB focusing on neuroscience and psychology applicationsProblem-based approach with many examples from neuroscience and cognitive psychology using real dataIllustrated in full color throughout Careful tutorial approach, by authors who are award-winning educators with strong teaching experience

We hypothesize that neural activity recorded from the motor cortex, rather than encoding the directly observed motion itself, encodes a sequence of unobserved yet inferable motor primitives that are composed to build the observed motion. We make use of a double-step or \"target jump\" paradigm during 2D planar reaching to reliably induce corrections and decompose the motion during a jump into a primary primitive and a corrective, secondary primitive. We then show that a traditional encoding model, fit to unperturbed trials, does not adequately describe neural data during a jump. Instead, neural activity is better described by first applying the same encoding model to the primary primitive, and then implementing an instantaneous switch to the corrective, secondary primitive. While previous literature has argued for the existence of motor primitives on the basis of psychophysics, this is one of the first demonstrations of a signature of motor primitives in motor cortical activity. We also propose a primitive-based decoding algorithm for use in a brain-machine interface (BMI), whereby a collection of parameterized kinematic primitives are used to represent possible submovements. We define a likelihood ratio statistic, which is the ratio of the probability of movement to the probability of a hold condition. Using data recorded from primary motor cortex of a rhesus macaque, we show that the likelihood ratio can be used to decode the start time of a submovement and the number of submovements, which is difficult to do using existing methods. By linearly summing the most likely submovements, the likelihood ratio can be used to generate an estimate of hand position which is biologically realistic. We hypothesize that such a primitive-based decoding approach will also deliver superior performance during real-time, online neural control.