Past events

A fundamental problem in neuroscience is to elucidate the relationship between neuronal network anatomy and its functional dynamics. The nematode worm C. elegans is an ideal model to study these problems. Its nervous system has just 302 neurons and all synaptic connections between them have been fully mapped. Using a large-scale Ca2+-imaging approach, we previously discovered nervous system wide neuronal population dynamics in the worm that encode action commands. These dynamics feature various network attractor states during which neurons coordinate and synchronize their activities, thereby providing functional interaction maps. In this talk, I will discuss unpublished work where we combine graph-theoretical and experimental approaches to understand which anatomical features in network connectivity relate to these functional dynamics and interactions between neurons.

Denise Cai: Linking memories across time The compilation of memories, collected and aggregated across a lifetime defines our human experience. My lab is interested in dissecting how memories are stored, updated, integrated and retrieved across a lifetime. Recent studies suggest that a shared neural ensemble may link distinct memories encoded close in time. Using in vivo calcium imaging (with open-source Miniscopes in freely behaving mice), TetTag transgenic system, chemogenetics, electrophysiology and novel behavioral designs, we tested how hippocampal networks temporally link memories. Multiple convergent findings suggest that contextual memories encoded close in time are linked by directing storage into overlapping hippocampal ensembles, such that the recall of one memory can trigger the recall of another temporally-related memory. Alteration of this process (e.g. during aging, PTSD, etc) affect the temporal structure of memories, thus impairing efficient recall of related information. Tristan Shuman: Breakdown of spatial coding and interneuron synchronization in epileptic mice Temporal lobe epilepsy causes severe cognitive deficits yet the circuit mechanisms that alter cognition remain unknown. We hypothesized that the death and reorganization of inhibitory connections during epileptogenesis may disrupt synchrony of hippocampal inhibition. To test this, we simultaneously recorded from CA1 and dentate gyrus (DG) in pilocarpine-treated epileptic mice with silicon probes during head-fixed virtual navigation. We found desynchronized interneuron firing between CA1 and DG in epileptic mice. Since hippocampal interneurons control information processing, we tested whether CA1 spatial coding was altered in this desynchronized circuit using a novel wire-free Miniscope. We found that CA1 place cells in epileptic mice were unstable and completely remapped across a week. This place cell instability emerged ~6 weeks after status epilepticus, well after the onset of chronic spontaneous seizures and interneuron death. Finally, our CA1 network model showed that desynchronized inputs can impair information content and stability of CA1 place cells. Together, these results demonstrate that temporally precise intra-hippocampal communication is critical for spatial processing and hippocampal desynchronization contributes to spatial coding deficits in epileptic mice.

In recent years, a growing body of scientific and clinical evidence points to the effectiveness of mental imagery (MI) in enhancing motor and non-motor aspects of performance in a variety of populations, including athletes, dancers, and people with neurodegenerative conditions, such as Parkinsonʼs disease (PD). However, MI’s mechanisms of effect are not fully understood to date. Further, MI’s best practices and potential benefits from implementing such approaches in sports and neurorehabilitation are in its infancy. This talk will focus on three MI-based approaches to movement retraining I study―motor imagery practice, dynamic neurocognitive imagery, and Gaga movement language―that use a variety of neuro-cognitive elements, including problem-solving, proprioception, and internally guided movement, along with anatomical and metaphorical imagery, self-touch, and self-talk. I will discuss a brief background of MI and its beneficial effects on human motor and cognitive performance, followed by a review of my research into MI for PD rehabilitation and dance training. I will specifically discuss our work on MI and its association with body schema in people with PD. Lastly, future directions into basic, transitional, and clinical research will be discussed.

Face recognition is a computationally challenging classification task that requires generalization across different views of the same identity as well as discrimination across different identities of a relatively homogenous set of visual stimuli. How does the brain resolve this taxing classification task? It is well-established that faces are processed by specialized neural mechanisms in high-level visual cortex. Nevertheless, it is not clear how this divergence to a face-specific and an object-general system contributes to face recognition. Recent advances in machine face recognition together with our understanding of how humans recognize faces enable us to address this question. In particular, I will show that a deep convolutional neural network (DCNN) that is trained on face recognition, but not a DCNN that is trained on object recognition, is sensitive to the same view-invariant facial features that humans use for face recognition. Similar to the hierarchical architecture of the visual system that diverges to a face and an object system at high-level visual cortex, a human-like, view-invariant face representation emerges only at higher layers of the face-trained but not the object-trained neural network. This view-invariant face representation is specific to the category of faces that the system was trained with both in humans and machines. I will therefore further emphasize the important role of experience and suggest that human face recognition depends on our social experience with familiar faces (“supervised learning”) rather than passive perceptual exposure to unfamiliar faces (“unsupervised learning”), highlighting the important role of social cognition in face recognition.

Integrating information from different sensory systems is essential for faithful representation of the external world. Different senses represent different aspects of the surrounding world. They operate at different time scales, and integrate information presented at different distances from the body. For example, tactile sensation enables sensing very proximal objects, whereas the auditory system allows us to sense very distant objects as well. In many instances, however, we simultaneously sense the same object using two or more modalities. This occurs, for example, when we use a hammer. In this case, we watch our movements and get tactile and auditory feedbacks for fine-tuning and precision. In this work, we are interested in revealing how the primary sensory cortices of both the auditory and somatosensory vibrissa systems integrate information they receive from a given source, in this case when the animal touches an object by moving its whiskers. We ask if such touch signals can produce audible signals that can be perceived by the auditory system. Towards this aim, we severed the infraorbital nerve (ION) of mice to eliminate somatosensory sensation going from the whiskers and the pad to the cortex. We then head fixed the mice and presented a piece of aluminum foil to the whiskers. Our preliminary results show that when the mouse whisked against the object it produced audible sound as we examined by using highly sensitive ultrasonic microphones. This sound differs from the environment both in amplitude and frequency range. Many neurons in the primary auditory cortex responded to the sound generated by the contact of the whiskers with the object. We propose that mice can use the two sensory systems in a complimentary manner in order to produce comprehensive representation of their proximal environment, perhaps similar to the way cane is used by visually impaired people. To demonstrate if the animal can use this auditory sensation, we will train head fixed and ION severed mice to respond to objects that produce sound by the whiskers’ touch. Showing that the auditory sensation can be relevant for the animal in identifying objects.

Internal states, including affective or homeostatic states, are important behavioral motivators. The amygdala is a key regulator of motivated behaviors, yet how distinct internal states are represented in amygdala circuits is unknown. Here, by longitudinally imaging neural calcium dynamics across different environments in freely moving mice, we identify changes in the activity levels of two major, non-overlapping populations of principal neurons in the basal amygdala (BA) that predict switches between exploratory and non-exploratory (defensive, anxiety-like) states. Moreover, the amygdala broadcasts state information via several output pathways to larger brain networks, and sensory responses in BA occur independently of behavioral state encoding. Thus, the brain processes external stimuli and internal states orthogonally, which may facilitate rapid and flexible selection of appropriate, state-dependent behavioral responses.

Electroencephalography and surface electromyography are notoriously cumbersome technologies. A typical setup may involve bulky electrodes, dandling wires, and a large amplifier unit. The wide adaptation of these technologies in numerous applications has been accordingly fairly limited. Thanks to the availability of printed electronics technologies, it is now possible to dramatically simplify these techniques. Elegant electrode arrays with unprecedented performances can be readily produced, eliminating the need to handle multiple electrodes and wires. Specifically, in this presentation I will discuss how printed electronics can improve signal transmission at the electrode-skin interface, facilitate electrode-skin stability, and enhance user convenience during electrode placement while achieving prolonged use. Customizing electrode array designs and implementing blind source separation methods, can also improve recording resolution, reduce variability between individuals and minimizing signal cross-talk between nearby electrodes. Finally, I will outline several important applications in the field of neuroscience and how each can benefit from the convergence of electrophysiology and printed electronics.

Learning to detect content-independent transformations from data is one of the central problems in biological and artificial intelligence. An example of such problem is unsupervised learning of a visual motion detector from pairs of consecutive video frames. Here, by optimizing a principled objective funciton, we derive an unsupervised algorithm that maps onto a biological plausible neural network. When trained on video frames, the neural network recapitulates the reconstructed connectome of the fly motion detector. In particular, local motion detectors combine information from at least three adjacent pixels, something that contradicts the celebrated Hassenstein-Reichardt model.