Topic: pattern separation

How can the hippocampal system transition from episodic one-shot learning to a multi-shot learning regime and what is the utility of the resultant neural representations? This talk will explore the role of the dentate gyrus (DG) anatomy in this context. The canonical DG model suggests it performs pattern separation. More recent experimental results challenge this standard model, suggesting DG function is more complex and also supports the precise binding of objects and events to space and the integration of information across episodes. Very recent studies attribute pattern separation and patte

The ability to discriminate different sensory patterns by disentangling their neural representations is an important property of neural networks. While a variety of learning rules are known to be highly effective at fine-tuning synapses to achieve this, less is known about how different cell types in the brain can facilitate this process by providing architectural priors that bias the network towards sparse, selective, and discriminable representations. We studied this by simulating a neuronal network modelled on the dentate gyrus—an area characterised by sparse activity associated with patter

The cerebellum contains more than half of the brain’s neurons and it is essential for motor control. Its neural circuits have a distinctive architecture comprised of a large, sparse expansion from the input mossy fibres to the granule cell layer. For years, theories of how cerebellar architectural features relate to cerebellar function have been formulated. It has been shown that some of these features can facilitate pattern separation. However, these theories don’t consider the need for it to learn fast in order to control smooth and accurate movements. Here, we confront this gap. This talk w

Hippocampal granule cell (GC) models exhibit degeneracy with different ion channel parameters resulting in comparable functional behaviour [1,2]. However, it is unknown how the degenerate models are further constrained by energetic considerations and known computational functions of GCs. Here, we use Pareto optimality to investigate this question. The Pareto perspective is particularly useful for multi-objective optimisation with multiple trade-offs [3]. We assume that evolution constrains and simplifies the ion channel parameter space by favouring Pareto optimal neurons, which cannot improve

Network models of cortex typically focus on excitatory population dynamics, relegating inhibition to a support role. However, recent studies have shown that memories can be encoded by both excitatory and inhibitory neurons (EI engrams), adding to the evidence of the functional importance of inhibitory neurons beyond mere stabilization. The functional consequence of such EI engrams is unclear, and the synaptic plasticity rules that could sustain them are unknown. Here, we explore in silico a mechanism for EI engrams storage and recall to glean possible computational benefits of such engrams ove