Juan M. Galeazzi

University of Oxford | OX · Department of Experimental Psychology

Frontopolar cortex (FPC) is a large, anterior sub-region of prefrontal cortex found in both humans and non-human primates (NHPs) and is thought to support monitoring the value of switching between alternative goals. However, the neuronal mechanisms underlying this function are unclear. Here we used multielectrode arrays to record the local field po...

Understanding the unique functions of different subregions of primate prefrontal cortex has been a longstanding goal in cog-nitive neuroscience. Yet, the anatomy and function of one of its largest subregions (the frontopolar cortex) remain enigmatic and underspecified. Our Society for Neuroscience minisymposium Primate Frontopolar Cortex: From Circ...

According to dual‐process signal detection (DPSD) theories, short and long‐term recognition memory draw upon both familiarity and recollection. It remains unclear how primate prefrontal cortex (PFC) contributes to these processes but frequency‐specific neuronal activities are considered to play a key role. In Experiment 1, non‐human primate (NHP) l...

In this paper, we simulate the effects of hippocampal lesions on Pavlovian conditioning with an existing neural network model. According to the model, the hippocampus sends a diffuse discrepancy signal that modulates efficacies of synapses from primary sensory to polysensory areas. We hypothesize that this signal lessens the detrimental effects of...

According to dual-process theories, recognition memory draws upon both familiarity and recollection. It remains unclear how primate prefrontal cortex (PFC) contributes to familiarity and recollection processes but frequency-specific neuronal activities are considered to play a key role. Here, non-human primate (NHP) electrophysiological local field...

We discuss a recently proposed approach to solve the classic feature-binding problem in primate vision that uses neural dynamics known to be present within the visual cortex. Broadly, the feature-binding problem in the visual context concerns not only how a hierarchy of features such as edges and objects within a scene are represented, but also the...

This is a condensed version of the paper "Hebbian Learning of Hand-Centred Representations in a Hierarchical Neural Network Model of the Primate Visual System" (DOI: https://doi.org/10.1371/journal. pone.0178304)

A subset of neurons in the posterior parietal and premotor areas of the primate brain respond to the locations of visual targets in a hand-centred frame of reference. Such hand-centred visual representations are thought to play an important role in visually-guided reaching to target locations in space. In this paper we show how a biologically plaus...

Neurons have been found in the primate brain that respond to objects in specific locations in hand-centered coordinates. A key theoretical challenge is to explain how such hand-centered neuronal responses may develop through visual experience. In this paper we show how hand-centered visual receptive fields can develop using an artificial neural net...

Neurons that respond to visual targets in a hand-centered frame of reference have been found within various areas of the primate brain. We investigate how hand-centered visual representations may develop in a neural network model of the primate visual system called VisNet, when the model is trained on images of the hand seen against natural visual...

This chapter discusses computer modelling of hierarchical motor function in the brain. The focus is on dynamicalmodels that utilise biologically plausible neural network architectures with local associative synaptic learning rules. The chapter begins with a review of our own laboratory's work in this area. We present a series of hierarchical motor...

We show how hand-centred visual representations could develop in the primate posterior parietal and premotor cortices during visually guided learning in a self-organizing neural network model. The model incorporates trace learning in the feed-forward synaptic connections between successive neuronal layers. Trace learning encourages neurons to learn...

This paper investigates the possible role of neuroanatomical features in Pavlovian conditioning, via computer simulations with layered, feedforward artificial neural networks. The networks' structure and functioning are described by a strongly bottom-up model that takes into account the roles of hippocampal and dopaminergic systems in conditioning....

Second-order conditioning depends on the intensities of the conditioned (CS1, CS2) and unconditioned stimuli (US), and the number of CS1-US and CS2-CS1 pairings. This paper describes simulations of these dependencies in feedforward partially-connected artificial neural networks. In Simulation 1, second-order conditioning was successfully simulated...