Abstract concept learning in a simple neural network inspired by the insect brain

The capacity to learn abstract concepts such as 'sameness' and 'difference' is considered a higher-order cognitive function, typically thought to be dependent on top-down neocortical processing. It is therefore surprising that honey bees apparantly have this capacity. Here we report a model of the structures of the honey bee brain that can learn sameness and difference, as well as a range of complex and simple associative learning tasks. Our model is constrained by the known connections and properties of the mushroom body, including the protocerebral tract, and provides a good fit to the learning rates and performances of real bees in all tasks, including learning sameness and difference. The model proposes a novel mechanism for learning the abstract concepts of 'sameness' and 'difference' that is compatible with the insect brain, and is not dependent on top-down or executive control processing

The nature of representation in cognitive control

Theoretical thesis.Bibliography: page 72.Introduction -- Chapter 1: Representation in the brain -- Chapter 2: Control in the absence of control representations -- Chapter 3: The anatomy of a control representation -- Conclusion.Cognitive control broadly refers to those processes which adaptively coordinate behaviour in service of a goal. To achieve control, the brain must resolve conflicting information and competing cognitive demands, even when doing so runs counter to more dominant, or prepotent impulses. Explaining this property in the context of the brain has long posed a general problem to researchers. Mechanisms of control have been typically posed as intentional processes and are thus subject to anthropomorphism-styled as a brain within the brain. It is difficult to imagine how neural circuits can achieve this. Classical cognitive science has often been criticised for invoking these 'homunculi' to account for control-related processing.Contemporary neuroscientific and computational literature provides an opportunity to resolve these homuncular accounts. Neural network function provides a plausible means of representing information in the brain. Viewed through the lens of network dynamics, certain structural and functional specialisations characterising control-related phenomena can be grounded in neurally plausible properties of the brain. I pay particular attention to how the circuit organisation of the neocortex may contribute to cognitive control mechanisms.I show that such a structurewould achieve a high level of cognitive control as an emergent property of network function, without the need to invoke homuncular mechanisms.1 online resource (72 pages