The brain is affected strongly by experience, both during development and in adulthood. Understanding precisely how experience alters the brain and its processing is a central question in neuroscience, from studies of learning and memory to those of cortical reorganization following injury. In the realm of sensory processing, we know that both perception and cortical neurons are strongly affected by adaptation--the sensory input of the preceding tens of milliseconds to many minutes. Because of its rapid time scale, this form of plasticity is likely to be a critical component of ongoing sensory processing. Our long-term goal is to understand the effects of adaptation and how they contribute to vision. Previous work has established that adaptation alters neuronal response properties throughout the visual system, sometimes in different ways at different stages of processing. The goal of this project is to determine, for the early stages of the visual motion processing pathway, how neurons adapt to arbitrary spatial and temporal patterns of visual input. In the first series of experiments, we will determine how the responsiveness and tuning of neurons in primary visual cortex and in extrastriate area MT are affected by individual visual stimuli of different spatial form, size and duration. In these experiments, we will make use of electrode arrays that allow us to sample many neurons simultaneously and to study interactions among them. Based on preliminary and published work, we hypothesize that the plasticity triggered by individual stimuli serves to maintain the balance of activity in a local cortical network, not to optimize the sensory encoding of individual cells as previously suggested. As a result, we propose that not all stimuli that are effective at driving visual neurons will induce plasticity. In our second series of experiments, we will evaluate how cortical neurons adjust to the statistics of an ensemble of inputs, presented in a dynamic, continuous sequence. Our hypothesis in these experiments is that neurons adjust to the range of inputs in such ensembles and that this plasticity is a rapid gain control that is distinct from the effects triggered by persistent stimuli. The knowledge we gain in this study will be important for understanding how the locus and nature of plasticity depends on the properties of sensory input. This, in turn, is important for allowing us to integrate information gained in psychophysical, neuroimaging, and neurophysiological studies that use adaptation as a tool to study the visual system. In addition, many of the questions that we address are common to studies of other forms of plasticity, such as cortical reorganization after injury. By studying how cortical circuits are affected by recent stimulus history, we hope to learn more generally about the capacity of these circuits to learn and reorganize.
This project aims to determine how the visual system adapts to recent sensory input. Studying the rapid plasticity caused by sensory experience is likely to provide knowledge important for a number of clinical issues, including understanding how the brain reorganizes after central (e.g. stroke) or peripheral (e.g. limb amputation) injury and designing sensory devices (e.g. artificial cochlea or visual aids) that extract or insert signals into the brain.
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| Aschner, Amir; Solomon, Samuel G; Landy, Michael S et al. (2018) Temporal Contingencies Determine Whether Adaptation Strengthens or Weakens Normalization. J Neurosci 38:10129-10142 |
| Rosenbaum, Robert; Smith, Matthew A; Kohn, Adam et al. (2017) The spatial structure of correlated neuronal variability. Nat Neurosci 20:107-114 |
| O'Donnell, Cian; Gonçalves, J Tiago; Whiteley, Nick et al. (2017) The Population Tracking Model: A Simple, Scalable Statistical Model for Neural Population Data. Neural Comput 29:50-93 |
| Larsson, Jonas; Solomon, Samuel G; Kohn, Adam (2016) fMRI adaptation revisited. Cortex 80:154-60 |
| Cowley, Benjamin R; Smith, Matthew A; Kohn, Adam et al. (2016) Stimulus-Driven Population Activity Patterns in Macaque Primary Visual Cortex. PLoS Comput Biol 12:e1005185 |
| Kohn, Adam; Coen-Cagli, Ruben; Kanitscheider, Ingmar et al. (2016) Correlations and Neuronal Population Information. Annu Rev Neurosci 39:237-56 |
| Seely, Jeffrey S; Kaufman, Matthew T; Ryu, Stephen I et al. (2016) Tensor Analysis Reveals Distinct Population Structure that Parallels the Different Computational Roles of Areas M1 and V1. PLoS Comput Biol 12:e1005164 |
| Williamson, Ryan C; Cowley, Benjamin R; Litwin-Kumar, Ashok et al. (2016) Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models. PLoS Comput Biol 12:e1005141 |
| Kar, Kohitij; Krekelberg, Bart (2016) Testing the assumptions underlying fMRI adaptation using intracortical recordings in area MT. Cortex 80:21-34 |
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