Large-scale waves of activity in the neonatal mouse brain in vivo occur almost exclusively during sleep cycles.

Spontaneous electrical activity plays major roles in the development of cortical circuitry. This activity can occur highly localized regions or can propagate over the entire cortex. Both types of activity coexist during early development.

The unbiased estimation of the fraction of variance explained by a model.

The correlation coefficient squared, r2, is commonly used to validate quantitative models on neural data, yet it is biased by trial-to-trial variability: as trial-to-trial variability increases, measured correlation to a model's predictions decreases. As a result, models that perfectly explain neural tuning can appear to perform poorly. Many solutions to this problem have been proposed, but no consensus has been reached on which is the least biased estimator.

Accounting for Biases in the Estimation of Neuronal Signal Correlation.

Signal correlation () is commonly defined as the correlation between the tuning curves of two neurons and is widely used as a metric of tuning similarity. It is fundamental to how populations of neurons represent stimuli and has been central to many studies of neural coding. Yet the classic estimate, Pearson's correlation coefficient, [Formula: see text], between the average responses of two neurons to a set of stimuli suffers from confounding biases.

Mining naturalistic human behaviors in long-term video and neural recordings.

Background: Recent technological advances in brain recording and machine learning algorithms are enabling the study of neural activity underlying spontaneous human behaviors, beyond the confines of cued, repeated trials. However, analyzing such unstructured data lacking a priori experimental design remains a significant challenge, especially when the data is multi-modal and long-term.

New Method: Here we describe an automated, behavior-first approach for analyzing simultaneously recorded long-term, naturalistic electrocorticography (ECoG) and behavior video data.

Using Head-Mounted Ethanol Sensors to Monitor Olfactory Information and Determine Behavioral Changes Associated with Ethanol-Plume Contact during Mouse Odor-Guided Navigation.

Olfaction guides navigation and decision-making in organisms from multiple animal phyla. Understanding how animals use olfactory cues to guide navigation is a complicated problem for two main reasons. First, the sensory cues used to guide animals to the source of an odor consist of volatile molecules, which form plumes.

A micro-LED implant and technique for optogenetic stimulation of the rat spinal cord.

To date, relatively few studies have used optogenetic stimulation to address basic science and therapeutic questions within the spinal cord. Even less have reported optogenetic stimulation in the rat spinal cord. This is likely due to a lack of accessible optogenetic implants.

Muscle weakness has a limited effect on motor control of gait in Duchenne muscular dystrophy.

Aim: Our aim was to determine if synergy weights and activations are altered in Duchenne muscular dystrophy (DMD) and if these alterations could be linked to muscle weakness.

Methods: In 22 children with DMD and 22 typical developing (TD) children of a similar age, surface electromyography (sEMG) of the gluteus medius, rectus femoris (REF), medial hamstrings, tibialis anterior, and medial gastrocnemius (GAS) were recorded during gait. Muscle weakness was assessed with maximal voluntary isometric contractions (MVIC).

Relating Natural Language Aptitude to Individual Differences in Learning Programming Languages.

This experiment employed an individual differences approach to test the hypothesis that learning modern programming languages resembles second "natural" language learning in adulthood. Behavioral and neural (resting-state EEG) indices of language aptitude were used along with numeracy and fluid cognitive measures (e.g.

Signal recovery from stimulation artifacts in intracranial recordings with dictionary learning.

Objective: Electrical stimulation of the human brain is commonly used for eliciting and inhibiting neural activity for clinical diagnostics, modifying abnormal neural circuit function for therapeutics, and interrogating cortical connectivity. However, recording electrical signals with concurrent stimulation results in dominant electrical artifacts that mask the neural signals of interest. Here we develop a method to reproducibly and robustly recover neural activity during concurrent stimulation.

Respiratory resetting elicited by single pulse spinal stimulation.

Intraspinal microstimulation (ISMS) can effectively activate spinal motor circuits, but the impact on the endogenous respiratory pattern has not been systematically evaluated. Here we delivered ISMS in spontaneously breathing adult rats while simultaneously recording diaphragm and external intercostal electromyography activity. ISMS pulses were delivered from C2-T1 along two rostrocaudal tracts located 0.

Model-Based Recommendations for Optimal Surgical Placement of Epiretinal Implants.

A major limitation of current electronic retinal implants is that in addition to stimulating the intended retinal ganglion cells, they also stimulate passing axon fibers, producing perceptual 'streaks' that limit the quality of the generated visual experience. Recent evidence suggests a dependence between the shape of the elicited visual percept and the retinal location of the stimulating electrode. However, this knowledge has yet to be incorporated into the surgical placement of retinal implants.

The role of adaptation in neural coding.

The concept of 'neural coding' supposes that neural firing patterns in some sense represent some external correlate, whether sensory, motor, or structural knowledge about the world. While the implied existence of a one-to-one mapping between external referents and neural firing has been useful, the prevalence of adaptation challenges this. Adaptation provides neural responses with dynamics on timescales that range from milliseconds up to many seconds.

Data-driven models in human neuroscience and neuroengineering.

Discoveries in modern human neuroscience are increasingly driven by quantitative understanding of complex data. Data-intensive approaches to modeling have promise to dramatically advance our understanding of the brain and critically enable neuroengineering capabilities. In this review, we provide an accessible primer to modern modeling approaches and highlight recent data-driven discoveries in the domains of neuroimaging, single-neuron and neuronal population responses, and device neuroengineering.

Coding Principles in Adaptation.

Adaptation is a common principle that recurs throughout the nervous system at all stages of processing. This principle manifests in a variety of phenomena, from spike frequency adaptation, to apparent changes in receptive fields with changes in stimulus statistics, to enhanced responses to unexpected stimuli. The ubiquity of adaptation leads naturally to the question: What purpose do these different types of adaptation serve? A diverse set of theories, often highly overlapping, has been proposed to explain the functional role of adaptive phenomena.

Neural correlates of sparse coding and dimensionality reduction.

Supported by recent computational studies, there is increasing evidence that a wide range of neuronal responses can be understood as an emergent property of nonnegative sparse coding (NSC), an efficient population coding scheme based on dimensionality reduction and sparsity constraints. We review evidence that NSC might be employed by sensory areas to efficiently encode external stimulus spaces, by some associative areas to conjunctively represent multiple behaviorally relevant variables, and possibly by the basal ganglia to coordinate movement. In addition, NSC might provide a useful theoretical framework under which to understand the often complex and nonintuitive response properties of neurons in other brain areas.

A model of ganglion axon pathways accounts for percepts elicited by retinal implants.

Degenerative retinal diseases such as retinitis pigmentosa and macular degeneration cause irreversible vision loss in more than 10 million people worldwide. Retinal prostheses, now implanted in over 250 patients worldwide, electrically stimulate surviving cells in order to evoke neuronal responses that are interpreted by the brain as visual percepts ('phosphenes'). However, instead of seeing focal spots of light, current implant users perceive highly distorted phosphenes that vary in shape both across subjects and electrodes.

Combining Citizen Science and Deep Learning to Amplify Expertise in Neuroimaging.

Big Data promises to advance science through data-driven discovery. However, many standard lab protocols rely on manual examination, which is not feasible for large-scale datasets. Meanwhile, automated approaches lack the accuracy of expert examination.

Variation in sequence dynamics improves maintenance of stereotyped behavior in an example from bird song.

Performing a stereotyped behavior successfully over time requires both maintaining performance quality and adapting efficiently to environmental or physical changes affecting performance. The bird song system is a paradigmatic example of learning a stereotyped behavior and therefore is a good place to study the interaction of these two goals. Through a model of bird song learning, we show how instability in neural representation of stable behavior confers advantages for adaptation and maintenance with minimal cost to performance quality.

BrainNet: A Multi-Person Brain-to-Brain Interface for Direct Collaboration Between Brains.

We present BrainNet which, to our knowledge, is the first multi-person non-invasive direct brain-to-brain interface for collaborative problem solving. The interface combines electroencephalography (EEG) to record brain signals and transcranial magnetic stimulation (TMS) to deliver information noninvasively to the brain. The interface allows three human subjects to collaborate and solve a task using direct brain-to-brain communication.

From the Wet Lab to the Web Lab: A Paradigm Shift in Brain Imaging Research.

Web technology has transformed our lives, and has led to a paradigm shift in the computational sciences. As the neuroimaging informatics research community amasses large datasets to answer complex neuroscience questions, we find that the web is the best medium to facilitate novel insights by way of improved collaboration and communication. Here, we review the landscape of web technologies used in neuroimaging research, and discuss future applications, areas for improvement, and the limitations of using web technology in research.

The Role of Dorsal Premotor Cortex in Resolving Abstract Motor Rules: Converging Evidence From Transcranial Magnetic Stimulation and Cognitive Modeling.

In this study, repetitive transcranial magnetic stimulation (rTMS) was applied over left dorsal premotor cortex (PMd) while participants performed a novel task paradigm that required planning of responses in accordance with both instructed rules and present stimuli. rTMS is a noninvasive form of neurostimulation that can interfere with ongoing processing of a targeted cortical region, resulting in a transient "virtual lesion" that can reveal the contribution of the region to ongoing behavior. Increased response times (RTs) were observed specifically when rTMS was applied over PMd while participants were preparing to execute a complex response to an uninstructed stimulus.

'Artiphysiology' reveals V4-like shape tuning in a deep network trained for image classification.

Deep networks provide a potentially rich interconnection between neuroscientific and artificial approaches to understanding visual intelligence, but the relationship between artificial and neural representations of complex visual form has not been elucidated at the level of single-unit selectivity. Taking the approach of an electrophysiologist to characterizing single CNN units, we found many units exhibit translation-invariant boundary curvature selectivity approaching that of exemplar neurons in the primate mid-level visual area V4. For some V4-like units, particularly in middle layers, the natural images that drove them best were qualitatively consistent with selectivity for object boundaries.