Visual neurons are strongly influenced by sensory history or adaptation. Our long-term goal is to understand how such experience influences cortical processing and contributes to vision. Adaptation effects have been described throughout the visual system, from the retina to higher visual cortex. Almost without exception, these effects have been measured in individual neurons. This is problematic, as it is widely believed that sensory information is encoded and processed by neuronal populations distributed across multiple cortical areas. Population coding is not adequately described by the mean tuning functions of individual neurons; it can be strongly influence by the coordination of activity among cells. How adaptation affects population coding is poorly understood, hampering our understanding of how experience driven changes in cortical circuits contribute to visual experience. In this proposal, we make use of recent theoretical work in understanding population coding, to test how adaptation influences the encoding of stimulus orientation by populations of neurons distributed across multiple stages of the macaque visual system.
In specific aim 1, we will determine how brief periods of adaptation affect neuronal tuning, response variability and response correlations, in the primary visual cortex (V1) and area V4 of awake monkeys performing a fixation task.
In specific aim 2, we will relate changes in population responses in V1 and V4 to the animal's performance on a fine orientation discrimination task. We will test algorithms for estimating stimulus orientation from the measured population responses and we will compare these with algorithms that best predict the animals' perceptual decisions, in both control and adapted states. This will reveal how animals weight the sensory responses to make perceptual decisions, how this weighting corresponds to the theoretically-optimal for estimating orientation, and how that weighting is altered by experience-driven plasticity.
In specific aim 3, we will conduct complementary experiments, with a more mechanistic focus. We will use a novel recording arrangement to measure simultaneously the activity of an output population in V1 and its downstream targets in V2 in anesthetized animals. We will determine how adaptation alters the functional coupling between these networks, and how coupling depends on the experience-driven changes in single neuronal properties and ensemble responses. Our project will provide the first comprehensive view of how population responses in visual cortex are affected by recent history, and how population responses are interpreted by downstream networks, to give rise to perceptual experience. The combination of recently-developed tools will allow us to bring unprecedented power to addressing these central and basic issues. We expect this project will contribute broadly to our understanding of population coding, corticocortical communication, and plasticity. The knowledge gained should be invaluable for developing more effective devices for interpreting brain activity for prosthetic devices, and for understanding the coordination of activity within and between cortical areas, in health and disease.
This project aims to determine how the visual system adapts to recent sensory input. The investigation of rapid, experience-dependent plasticity 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 ito the brain. This project will also further our understanding of the coordination of ensembles of neurons distributed across stages of the visual system, whose disruption is associated with a number of psychiatric disorders, including schizophrenia, autism spectrum disorders, and epilepsy.
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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 |
| 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 |
| Rosenbaum, Robert; Smith, Matthew A; Kohn, Adam et al. (2017) The spatial structure of correlated neuronal variability. Nat Neurosci 20:107-114 |
| 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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