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 In this talk, I will discuss unpublished work where we combine graph-theoretical and experimental approaches to understand which anatomical features in network connectivity relate to these functional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.