Sensory input can evoke very different percepts depending on how information is processed by the nervous system. Fundamental aspects of that neural processing remain poorly understood. Evidence points to correlation of spiking across neurons as a possible neural coding mechanism, especially in sensory systems. For example, sensory information is most effectively transmitted to the cortex when spiking is synchronized across thalamocortical neurons, and available evidence suggests that synchronous activity continues to be propagated to downstream cortical layers. This transfer of synchrony between pre- and postsynaptic neurons (i.e. synchrony transfer) is crucial, lest the information carried by synchronous spiking be lost. An important yet unresolved issue is how well synchrony is transferred between layers of cortex and, in general, how synchrony transfer is regulated. One thing is clear: sets of neurons transfer synchronous input to their postsynaptic targets only if they themselves respond to synchronous inputs with synchronous spiking. What, then, are the biophysical mechanisms that control spike synchrony across a set of neurons receiving synchronous input? Deciphering the cellular and synaptic bases for synchrony transfer has proven extremely challenging because synchrony is a multi-neuron, network-level phenomenon that is difficult to measure or control using standard experimental techniques. Consequently, the task has fallen to computer modeling. But although modeling has provided valuable insights, the need for experimentation persists. Our solution to this challenge is to embed real neurons in virtual networks by integrating electrophysiology with mathematical modeling. This will enable us to experimentally investigate the biophysical mechanisms regulating synchrony transfer in a slice preparation of rat somatosensory cortex. In brief, we will simulate synaptic connectivity patterns by combining dynamic clamp and mathematical modeling such that individually recorded neurons operate (and will be analyzed) as if they are part of a network propagating synchronous activity. Synchrony transfer will be quantified by comparing output synchrony, calculated by cross-correlation of recorded output spike trains, with input synchrony, specified when constructing our simulated synaptic input. We will use this innovative approach to test our central hypothesis that biophysical mechanisms at the level of single neurons, microcircuits, and synaptic plasticity can enable good synchrony transfer between cortical layers. We have identified spike generation, feedforward inhibition, and spike time dependent plasticity as candidate mechanisms based on theoretical insights derived from our previous work. Relating network-level phenomena like synchrony with their underlying biophysical mechanisms is essential for understanding the neurobiological basis of sensory processing. By combining mathematical modeling with electrophysiology to study real neurons embedded in virtual networks, our proposed study will establish direct links between network-level synchrony and the cellular and synaptic mechanisms regulating synchrony transfer.
Sensory input can evoke very different percepts depending on how the nervous system processes information. Derangements in that processing can lead to chronic perceptual abnormalities such as neuropathic pain, which is characterized by spontaneous painful sensations and hypersensitivity to normally innocuous stimuli. Deciphering the biophysical basis for network-level processing will facilitate translation of molecular breakthroughs into clinically effective treatments for complex, hard-to-treat conditions like neuropathic pain.
|Prescott, Steven A; Ma, Qiufu; De Koninck, Yves (2014) Normal and abnormal coding of somatosensory stimuli causing pain. Nat Neurosci 17:183-91|
|Ratte, Stephanie; Hong, Sungho; De Schutter, Erik et al. (2013) Impact of neuronal properties on network coding: roles of spike initiation dynamics and robust synchrony transfer. Neuron 78:758-72|
|Hong, Sungho; Ratte, Stephanie; Prescott, Steven A et al. (2012) Single neuron firing properties impact correlation-based population coding. J Neurosci 32:1413-28|