Physiology seminars

Dendrites play a central role in neuronal computation, and many complex mechanisms shape their structure, function, and connectivity. Dendrites can undergo plastic changes during development and learning, as well as during neurodevelopmental and neurodegenerative disease. We will discuss how the molecular and electrophysiological properties of dendrites enable them to perform complex computations important for sensory-motor processing and higher cognitive function, and how these can go awry.

Dysregulation of emotional processing and its integration with cognitive functions are central features of many mental/emotional disorders associated both with externalizing problems (aggressive, antisocial behaviors) and internalizing problems (anxiety, depression). As Dr. Joseph LeDoux, our invited speaker of this program, wrote in his famous book “Synaptic self: How Our Brains Become Who We Are”—the brain’s synapses—are the channels through which we think, act, imagine, feel, and remember. Synapses encode the essence of personality, enabling each of us to function as a distinctive, integrated individual from moment to moment. Thus, exploring the functioning of synapses leads to the understanding of the mechanism of (patho)physiological function of our brain. In this context, we have investigated the pathophysiology of psychiatric disorders, with particular emphasis on the synaptic function of model mice of various psychiatric disorders such as schizophrenia, autism, depression, and PTSD. Our current interest is how synaptic inputs are integrated to generate the action potential. Because the spatiotemporal organization of neuronal firing is crucial for information processing, but how thousands of inputs to the dendritic spines drive the firing remains a central question in neuroscience. We identified a distinct pattern of synaptic integration in the disease-related models, in which extra-large (XL) spines generate NMDA spikes within these spines, which was sufficient to drive neuronal firing. We experimentally and theoretically observed that XL spines negatively correlated with working memory. Our work offers a whole new concept for dendritic computation and network dynamics, and the understanding of psychiatric research will be greatly reconsidered. The second half of my talk is the development of a novel synaptic tool. Because, no matter how beautifully we can illuminate the spine morphology and how accurately we can quantify the synaptic integration, the links between synapse and brain function remain correlational. In order to challenge the causal relationship between synapse and brain function, we established AS-PaRac1, which is unique not only because it can specifically label and manipulate the recently potentiated dendritic spine (Hayashi-Takagi et al, 2015, Nature). With use of AS-PaRac1, we developed an activity-dependent simultaneous labeling of the presynaptic bouton and the potentiated spines to establish “functional connectomics” in a synaptic resolution. When we apply this new imaging method for PTSD model mice, we identified a completely new functional neural circuit of brain region A→B→C with a very strong S/N in the PTSD model mice. This novel tool of “functional connectomics” and its photo-manipulation could open up new areas of emotional/psychiatric research, and by extension, shed light on the neural networks that determine who we are.

Arguably one of the biggest mysteries in neuroscience is how the brain stores long-term memories. The major challenge for investigating the neural circuit underlying memory formation in the neocortex is the distributed nature of the resulting memory trace throughout the cortex. Here, we used a new behavioral paradigm that enabled us to generate memory traces in a specific cortical location and to specifically examine the mechanisms of memory formation in that region. We found that medial-temporal inputs arrive in neocortical layer 1 where the apical dendrites of cortical pyramidal neurons predominate. These dendrites have active properties that make them sensitive to contextual inputs from other areas that also send axons to layer 1 around the cortex. Blocking the influence of these medial-temporal inputs prevented learning and suppressed resulting dendritic activity. We conclude that layer 1 is the locus for hippocampal-dependent memory formation in the neocortex and propose that this process enhances the sensitivity of the tuft dendrites to contextual inputs.

The long-term goal of my research is to study the mammalian retina as a model for the central nervous system (CNS) -- to understand how it functions in physiological conditions, how it is formed, how it breaks down in pathological conditions, and how it can be repaired. I have focused on two research themes: 1) Photoreceptor structure, synapse, circuits, and development, 2) Hibernation and metabolic adaptations in the retina and beyond. As the first neuron of the visual system, photoreceptors are vital for photoreception and transmission of visual signals. I am particularly interested in cone photoreceptors, as they mediate our daylight vision with high resolution color information. Diseases affecting cone photoreceptors compromise visual functions in the central macular area of the human retina and are thus most detrimental to our vision. However, because cones are much less abundant compared to rods in most mammals, they are less well studied. We have used the ground squirrel (GS) as a model system to study cone vision, taking advantage of their unique cone-dominant retina. In particular, we have focused on short-wavelength sensitive cones (S-cones), which are not only essential for color vision, but are also an important origin of signals for biological rhythm, mood and cognitive functions, and the growth of the eye during development. We are studying critical cone synaptic structures – synaptic ribbons, the synaptic connections of S-cones, and the development of S-cones with regard to their specific connections. These works will provide knowledge of normal retinal development and function, which can also be extended to the rest of CNS; for example, the mechanisms of synaptic targeting during development. In addition, such knowledge will benefit the development of optimal therapeutic strategies for regeneration and repair in cases of retinal degenerative disease. Many neurodegenerative diseases, including retinal diseases, are rooted in metabolic stress in neurons and/or glial cells. Using the same GS model, we aim to learn from this hibernating mammal, which possesses an amazing capability to adapt to the extreme metabolic conditions during hibernation. By exploring the mechanisms of such adaptation, we hope to discover novel therapeutic tactics for neurodegenerative diseases.

Synaptic scaling is a homeostatic normalization mechanism that preserves relative synaptic strengths by adjusting them with a common factor. This multiplicative change is believed to be critical, since synaptic strengths are involved in learning and memory retention. Further, this homeostatic process is thought to be crucial for neuronal stability, playing a stabilizing role in otherwise runaway Hebbian plasticity [1-3]. Synaptic scaling requires a mechanism to sense total neuron activity and globally adjust synapses to achieve some activity set-point [4]. This process is relatively slow, which places limits on its ability to stabilize network activity [5]. Here we show that this slow response is inevitable in realistic neuronal morphologies. Furthermore, we reveal that global scaling can in fact be a source of instability unless responsiveness or scaling accuracy are sacrificed." "A neuron with tens of thousands of synapses must regulate its own excitability to compensate for changes in input. The time requirement for global feedback can introduce critical phase lags in a neuron’s response to perturbation. The severity of phase lag increases with neuron size. Further, a more expansive morphology worsens cell responsiveness and scaling accuracy, especially in distal regions of the neuron. Local pools of reserve receptors improve efficiency, potentiation, and scaling, but this comes at a cost. Trafficking large quantities of receptors requires time, exacerbating the phase lag and instability. Local homeostatic feedback mitigates instability, but this too comes at the cost of reducing scaling accuracy." "Realization of the phase lag instability requires a unified model of synaptic scaling, regulation, and transport. We present such a model with global and local feedback in realistic neuron morphologies (Fig. 1). This combined model shows that neurons face a tradeoff between stability, accuracy, and efficiency. Global feedback is required for synaptic scaling but favors either system stability or efficiency. Large receptor pools improve scaling accuracy in large morphologies but worsen both stability and efficiency. Local feedback improves the stability-efficiency tradeoff at the cost of scaling accuracy. This project introduces unexplored constraints on neuron size, morphology, and synaptic scaling that are weakened by an interplay between global and local feedback.