Discovering plasticity rules that organize and maintain neural circuits.

Intrinsic dynamics within the brain can accelerate learning by providing a prior scaffolding for dynamics aligned with task objectives. Such intrinsic dynamics should self-organize and self-sustain in the face of fluctuating inputs and biological noise, including synaptic turnover and cell death. An example of such dynamics is the formation of sequences, a ubiquitous motif in neural activity.

Olfactory bulb tracks breathing rhythms and place in freely behaving mice.

Vertebrates sniff to control the odor samples that enter their nose. These samples can not only help identify odorous objects, but also locations and events. However, there is no receptor for place or time.

A Comparison of Rapid Rule-Learning Strategies in Humans and Monkeys.

Interspecies comparisons are key to deriving an understanding of the behavioral and neural correlates of human cognition from animal models. We perform a detailed comparison of the strategies of female macaque monkeys to male and female humans on a variant of the Wisconsin Card Sorting Test (WCST), a widely studied and applied task that provides a multiattribute measure of cognitive function and depends on the frontal lobe. WCST performance requires the inference of a rule change given ambiguous feedback.

Unsupervised restoration of a complex learned behavior after large-scale neuronal perturbation.

Reliable execution of precise behaviors requires that brain circuits are resilient to variations in neuronal dynamics. Genetic perturbation of the majority of excitatory neurons in HVC, a brain region involved in song production, in adult songbirds with stereotypical songs triggered severe degradation of the song. The song fully recovered within 2 weeks, and substantial improvement occurred even when animals were prevented from singing during the recovery period, indicating that offline mechanisms enable recovery in an unsupervised manner.

Emergence of co-tuning in inhibitory neurons as a network phenomenon mediated by randomness, correlations, and homeostatic plasticity.

Cortical excitatory neurons show clear tuning to stimulus features, but the tuning properties of inhibitory interneurons are ambiguous. While inhibitory neurons have been considered to be largely untuned, some studies show that some parvalbumin-expressing (PV) neurons do show feature selectivity and participate in co-tuned subnetworks with pyramidal neurons. In this study, we first use mean-field theory to demonstrate that a combination of homeostatic plasticity governing the synaptic dynamics of the connections from PV to excitatory neurons, heterogeneity in the excitatory postsynaptic potentials that impinge on PV neurons, and shared correlated input from layer 4 results in the functional and structural self-organization of PV subnetworks.

Non-Stationary Dynamic Mode Decomposition.

Many physical processes display complex high-dimensional time-varying behavior, from global weather patterns to brain activity. An outstanding challenge is to express high dimensional data in terms of a dynamical model that reveals their spatiotemporal structure. Dynamic Mode Decomposition is a means to achieve this goal, allowing the identification of key spatiotemporal modes through the diagonalization of a finite dimensional approximation of the Koopman operator.

Non-Stationary Dynamic Mode Decomposition.

Many physical processes display complex high-dimensional time-varying behavior, from global weather patterns to brain activity. An outstanding challenge is to express high dimensional data in terms of a dynamical model that reveals their spatiotemporal structure. Dynamic Mode Decomposition is a means to achieve this goal, allowing the identification of key spatiotemporal modes through the diagonalization of a finite dimensional approximation of the Koopman operator.

Resilience of A Learned Motor Behavior After Chronic Disruption of Inhibitory Circuits.

Maintaining motor behaviors throughout life is crucial for an individual's survival and reproductive success. The neuronal mechanisms that preserve behavior are poorly understood. To address this question, we focused on the zebra finch, a bird that produces a highly stereotypical song after learning it as a juvenile.

A complete biomechanical model of contractile behaviors, from neural drive to muscle to movement.

How does neural activity drive muscles to produce behavior? The recent development of genetic lines in that allow complete calcium imaging of both neuronal and muscle activity, as well as systematic machine learning quantification of behaviors, makes this small cnidarian an ideal model system to understand and model the complete transformation from neural firing to body movements. To achieve this, we have built a neuromechanical model of 's fluid-filled hydrostatic skeleton, showing how drive by neuronal activity activates distinct patterns of muscle activity and body column biomechanics. Our model is based on experimental measurements of neuronal and muscle activity and assumes gap junctional coupling among muscle cells and calcium-dependent force generation by muscles.

Comparing rapid rule-learning strategies in humans and monkeys.

Inter-species comparisons are key to deriving an understanding of the behavioral and neural correlates of human cognition from animal models. We perform a detailed comparison of macaque monkey and human strategies on an analogue of the Wisconsin Card Sort Test, a widely studied and applied multi-attribute measure of cognitive function, wherein performance requires the inference of a changing rule given ambiguous feedback. We found that well-trained monkeys rapidly infer rules but are three times slower than humans.

Next-generation brain observatories.

We propose centralized brain observatories for large-scale recordings of neural activity in mice and non-human primates coupled with cloud-based data analysis and sharing. Such observatories will advance reproducible systems neuroscience and democratize access to the most advanced tools and data.

Dopamine neurons evaluate natural fluctuations in performance quality.

Many motor skills are learned by comparing ongoing behavior to internal performance benchmarks. Dopamine neurons encode performance error in behavioral paradigms where error is externally induced, but it remains unknown whether dopamine also signals the quality of natural performance fluctuations. Here, we record dopamine neurons in singing birds and examine how spontaneous dopamine spiking activity correlates with natural fluctuations in ongoing song.

Adrienne Fairhall.

Interview with Adrienne Fairhall, who studies the relationship between neuronal circuitry and the functional algorithms of computation at the University of Washington.

Context-dependent representations of movement in Drosophila dopaminergic reinforcement pathways.

Dopamine plays a central role in motivating and modifying behavior, serving to invigorate current behavioral performance and guide future actions through learning. Here we examine how this single neuromodulator can contribute to such diverse forms of behavioral modulation. By recording from the dopaminergic reinforcement pathways of the Drosophila mushroom body during active odor navigation, we reveal how their ongoing motor-associated activity relates to goal-directed behavior.

Tracking calcium dynamics from individual neurons in behaving animals.

Measuring the activity of neuronal populations with calcium imaging can capture emergent functional properties of neuronal circuits with single cell resolution. However, the motion of freely behaving animals, together with the intermittent detectability of calcium sensors, can hinder automatic monitoring of neuronal activity and their subsequent functional characterization. We report the development and open-source implementation of a multi-step cellular tracking algorithm (Elastic Motion Correction and Concatenation or EMC2) that compensates for the intermittent disappearance of moving neurons by integrating local deformation information from detectable neurons.

Teaching Computation in Neuroscience: Notes on the 2019 Society for Neuroscience Professional Development Workshop on Teaching.

The 2019 Society for Neuroscience Professional Development Workshop on Teaching reviewed current tools, approaches, and examples for teaching computation in neuroscience. Robert Kass described the statistical foundations that students need to properly analyze data. Pascal Wallisch compared MATLAB and Python as programming languages for teaching students.

Gender bias in academia: A lifetime problem that needs solutions.

Despite increased awareness of the lack of gender equity in academia and a growing number of initiatives to address issues of diversity, change is slow, and inequalities remain. A major source of inequity is gender bias, which has a substantial negative impact on the careers, work-life balance, and mental health of underrepresented groups in science. Here, we argue that gender bias is not a single problem but manifests as a collection of distinct issues that impact researchers' lives.

Capturing Multiple Timescales of Adaptation to Second-Order Statistics With Generalized Linear Models: Gain Scaling and Fractional Differentiation.

Single neurons can dynamically change the gain of their spiking responses to take into account shifts in stimulus variance. Moreover, gain adaptation can occur across multiple timescales. Here, we examine the ability of a simple statistical model of spike trains, the generalized linear model (GLM), to account for these adaptive effects.

The Mind of a Mouse.

Large scientific projects in genomics and astronomy are influential not because they answer any single question but because they enable investigation of continuously arising new questions from the same data-rich sources. Advances in automated mapping of the brain's synaptic connections (connectomics) suggest that the complicated circuits underlying brain function are ripe for analysis. We discuss benefits of mapping a mouse brain at the level of synapses.

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.

Reconfiguring Motor Circuits for a Joint Manual and BCI Task.

Designing brain-computer interfaces (BCIs) that can be used in conjunction with ongoing motor behavior requires an understanding of how neural activity co-opted for brain control interacts with existing neural circuits. For example, BCIs may be used to regain lost motor function after stroke. This requires that neural activity controlling unaffected limbs is dissociated from activity controlling the BCI.

Visual-Olfactory Integration in the Human Disease Vector Mosquito Aedes aegypti.

Mosquitoes rely on the integration of multiple sensory cues, including olfactory, visual, and thermal stimuli, to detect, identify, and locate their hosts [1-4]. Although we increasingly know more about the role of chemosensory behaviors in mediating mosquito-host interactions [1], the role of visual cues is comparatively less studied [3], and how the combination of olfactory and visual information is integrated in the mosquito brain remains unknown. In the present study, we used a tethered-flight light-emitting diode (LED) arena, which allowed for quantitative control over the stimuli, and a control theoretic model to show that CO modulates mosquito steering responses toward vertical bars.