Combining incoming sensory information with previously learned knowledge is one of the fundamental computations of sensory cortex, yet It remains poorly understood. In order to investigate the neural basis of this computation in visual cortex we combine three key techniques. First, we employ a rigorous mathematical framework to make predictions about how the activity of sensory neurons should change with learning and depend on the task. Second, we causally manipulate the neural circuitry by eliminating those inputs that have been hypothesized to carry previously learned information. Third, we record the spiking activity of many primary visual cortex (V1) neurons simultaneously, with and without those inputs. We will combine these three techniques in three important scenarios. In the first scenario, we will measure and analyze how V1 responses change over the course of learning two different versions of an orientation-discrimination task. We will use this new data to validate our theoretical framework and compare it to alternative theories. In the second scenario, we will analyze V1 responses while the subject is multitasking, switching between two different tasks. This will provide insights into the source of performance limitations due to multitasking and into the basis of hierarchical decision-making. In the third scenario, we '{ill c_on1pare V1 activity for sequential de~ision'.rnaking taskswh!Jn the brain over-weig_h_ts erly E)Vi_dencE) (displaying a 'confirmation bias'), and when it does not. This will allow us to test a new computational account of the confirmation bias in the visual domain. Our results will address several important debates in systems neuroscience: What is the function of feedback connections to sensory areas? What is the source and role of correlated variability of sensory responses? What mathematical framework best describes sensory computations?
This project will study how the brain combines the visual information on the retina with prior knowledge about the world, in order to support visual perception and decision-making. Insights into the neurological basis of those processes will help us understand the effect of diseases such as schizophrenia, autism, and ADHD on visual processing and function.
| Bondy, Adrian G; Haefner, Ralf M; Cumming, Bruce G (2018) Feedback determines the structure of correlated variability in primary visual cortex. Nat Neurosci 21:598-606 |
| Kawaguchi, Katsuhisa; Clery, Stephane; Pourriahi, Paria et al. (2018) Differentiating between Models of Perceptual Decision Making Using Pupil Size Inferred Confidence. J Neurosci 38:8874-8888 |
