In this project, we combine novel large-scale electrophysiological recordings and advanced behavioral methods to investigate neural information processing in higher visual cortical areas during object recognition.
In the first objective, we seek to investigate how cortical representations of visual stimuli progressively evolve across the cortical hierarchy to incorporate semantic information. For this, we have to record the neural activity of visual areas while mice are engaged in a visual discrimination task. Thus, we trained mice to discriminate between two different objects while they are freely-moving in their home cage. We are currently building a behavioral setup to enable us to record neural activity while mice are performing the task. In addition, we have a) established the recording technique using state-of-the-art high-density silicon probes that allow us to record the activity of hundreds of neurons simultaneously across many visual areas and b) optimized targeting and anatomical identification of the areas of interest.
Second objective is to characterize the selectivity of the neuronal populations in each cortical area. For this purpose, we utilize a closed loop approach that combines electrophysiological recordings with deep-learning modeling. Specifically, we simultaneously recorded the activity of hundreds of neurons in vivo in mouse visual areas in response to natural images. We then trained a convolutional neural network to predict the responses of each neuron recorded across the different areas and generated a set of stimuli that would optimally excite the recorded neurons. Subsequently, we showed the optimized stimuli back to the mice and recorded the activity of the same neurons. This enabled us to verify in vivo the results of the in silico model predictions. This approach allows us to identify what are the stimuli properties that the neural activity is invariant to across areas and thus to dissect the role of hierarchical processing in complex cortical computations such as object recognition.
Finally, the third objective focuses on resolving how dynamic interactions between areas shape the neural representations of visual stimuli. To identify the interareal interactions, we analyze the temporal dynamics of neural representations and compute the correlations of the neural activity between areas. We utilize advanced statistical modelling to uncover how the directionality of the neural information flow between the cortical areas contributes to shaping the object representations.
