Year-end Sale % OFF 
Abstract
Modern recording methods enable sampling of thousands of neurons during the performance of behavioral tasks, raising the question of how recorded activity relates to theoretical models. In the context of decision making, functional connectivity between choice-selective cortical neurons was recently reported. The straightforward interpretation of these data suggests the existence of selective pools of inhibitory and excitatory neurons. Computationally investigating an alternative mechanism for these experimental observations, we find that a randomly connected network of excitatory and inhibitory neurons generates single-cell selectivity, patterns of pairwise correlations, and the same ability of excitatory and inhibitory populations to predict choice, as in experimental observations. Further, we predict that, for this task, there are no anatomically defined subpopulations of neurons representing choice, and that choice preference of a particular neuron changes with the details of the task. We suggest that distributed stimulus selectivity and functional organization in population codes could be emergent properties of randomly connected networks.
Highlights
Experimental and data-processing advances have opened the floodgates of neural data: recordings from hundreds to thousands of neurons, in animals performing well-defined tasks, are becoming widespread in the literature [1,2,3]
Theoretical neuroscience has many such high-level models for computational algorithms underlying important functions, including making predictions [4], or accumulating evidence in making a decision [5, 6]. How are these functional models instantiated by real populations of neurons? For instance, suppose that we have a functional model in which pools of excitatory or inhibitory neurons selectively connect to each other, resulting in mutual suppression and patterns of activity that are selective for specific classes of input
The straightforward interpretation of this functional model is that sub-populations of excitatory and inhibitory neurons in the brain reflect the organization of the functional model
Summary
Experimental and data-processing advances have opened the floodgates of neural data: recordings from hundreds to thousands of neurons, in animals performing well-defined tasks, are becoming widespread in the literature [1,2,3]. Theoretical neuroscience has many such high-level models for computational algorithms underlying important functions, including making predictions [4], or accumulating evidence in making a decision [5, 6]. How are these functional models instantiated by real populations of neurons? The straightforward interpretation of this functional model is that sub-populations of excitatory and inhibitory neurons in the brain reflect the organization of the functional model This interpretation raises the question of how such structural patterns of connectivity arose, perhaps through learning or development
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
