There are many types of learning realized in different brain areas but the basic mechanisms underlying different forms of learning may be quite similar. One of the most widely studied forms of learning is perceptual learning in the visual system, defined as the permanent improvement in performance as a result of training on a sensory task. Training improves the ability to perceive simple as well as complex visual attributes such as depth defined with a random-dot stereogram, pop-out of a stimulus, and bomb outlines visualized though x-ray machines. Studying perceptual learning in the visual system is advantageous for understanding mechanisms of learning since it is thought to involve early stages of visual processing where the most is known about the properties of single units and cortical architecture. In addition, perceptual learning paradigms are used in the treatment of visual disorders (such as amblyopia) and in the enhancement of reorganization and behavioral recovery after brain injury, underscoring the importance of understanding the underlying mechanisms. A prerequisite for understanding learning mechanisms is to characterize how the response properties of individual neurons as well as their interactions change during the course of learning. A major impediment for realizing this has been the inability to record from the same individual neurons chronically, in vivo, during the course of learning. We now, for the first time, bypassed this limitation by developing a method using chronically implanted tetrode arrays that allows us to record from the same neurons across multiple consecutive days and weeks in awake, behaving macaques. We plan to study perceptual learning in an orientation discrimination task associated with improved performance in the discrimination of the orientation of a stimulus as a result of practice. Learning in this paradigm is specific to the trained retinal position and is therefore thought to reflect plasticity in early visual areas like the primary visual cortex (V1). We will study this process in area V1 of the macaque. First, we will measure how variable orientation tuning functions are across days and weeks during periods of no training (Specific Aim 1). Determining the stability of baseline orientation maps in V1 is an essential first step for studying how learning can modify them. Moreover, the information contained in the activity of a population of neurons also depends on the pair wise interactions between these neurons in addition to their individual properties. A good analogy is the performance of a team which does not only depend on the capabilities of its individual members but also in the way players interact with each other. Therefore, in order to quantify the information content of neural circuits before and after learning it is essential to also measure the strength of correlations between the neurons (Specific Aim 2).
In Specific Aim 3, we will record chronically from the same neurons in V1 while animals are being trained in an orientation discrimination task. This is very exciting since for the first time we are now able to record from individual neurons during the course of learning and characterize the associated changes in neural circuits.
One of the most widely studied forms of learning is perceptual learning in the visual system, defined as the permanent improvement in performance as a result of extensive training on numerous sensory tasks. Perceptual learning paradigms are used in the treatment of visual disorders such as amblyopia and using learning paradigms has great potential in the enhancement of reorganization and behavioral recovery after brain injury, underscoring the importance of understanding learning mechanisms. We will study perceptual learning while recording from the same neurons chronically during the course of learning.
| Denfield, George H; Ecker, Alexander S; Shinn, Tori J et al. (2018) Attentional fluctuations induce shared variability in macaque primary visual cortex. Nat Commun 9:2654 |
| Ecker, Alexander S; Denfield, George H; Bethge, Matthias et al. (2016) On the Structure of Neuronal Population Activity under Fluctuations in Attentional State. J Neurosci 36:1775-89 |
| Yatsenko, Dimitri; Josi?, Krešimir; Ecker, Alexander S et al. (2015) Improved estimation and interpretation of correlations in neural circuits. PLoS Comput Biol 11:e1004083 |
| Froudarakis, Emmanouil; Berens, Philipp; Ecker, Alexander S et al. (2014) Population code in mouse V1 facilitates readout of natural scenes through increased sparseness. Nat Neurosci 17:851-7 |
| Ecker, Alexander S; Berens, Philipp; Cotton, R James et al. (2014) State dependence of noise correlations in macaque primary visual cortex. Neuron 82:235-48 |
| Subramaniyan, Manivannan; Ecker, Alexander S; Berens, Philipp et al. (2013) Macaque monkeys perceive the flash lag illusion. PLoS One 8:e58788 |
| Berens, Philipp; Ecker, Alexander S; Cotton, R James et al. (2012) A fast and simple population code for orientation in primate V1. J Neurosci 32:10618-26 |
| Berens, Philipp; Ecker, Alexander S; Gerwinn, Sebastian et al. (2011) Reassessing optimal neural population codes with neurometric functions. Proc Natl Acad Sci U S A 108:4423-8 |
| Ecker, Alexander S; Berens, Philipp; Tolias, Andreas S et al. (2011) The effect of noise correlations in populations of diversely tuned neurons. J Neurosci 31:14272-83 |
| Ecker, Alexander S; Berens, Philipp; Keliris, Georgios A et al. (2010) Decorrelated neuronal firing in cortical microcircuits. Science 327:584-7 |
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