In a first part of the talk, I will present Predictive Coding Light (PCL), a novel unsupervised learning architecture for spiking neural networks. In contrast to conventional predictive coding approaches, which only transmit prediction errors to higher processing stages, PCL learns inhibitory lateral and top-down connectivity to suppress the most predictable spikes and passes a compressed representation of the input to higher processing stages. We show that PCL reproduces a range of biological findings and exhibits a favorable tradeoff between energy consumption and downstream classification performance on challenging benchmarks. A second part of the talk will feature our lab’s efforts to explain how infants and toddlers might learn abstract object representations without supervision. I will present deep learning models that exploit the temporal and multimodal structure of their sensory inputs to learn representations of individual objects, object categories, or abstract super-categories such as „kitchen object“ in a fully unsupervised fashion. These models offer a parsimonious account of how abstract semantic knowledge may be rooted in children's embodied first-person experiences.

The ability to build, store, and manipulate semantic representations lies at the core of all our (inter)actions. Combining evidence from cognitive neuroimaging and experimental neuropsychology, I study the neurocognitive correlates of semantic knowledge in relation to other cognitive functions, chiefly language. In this talk, I will start by reviewing neuroimaging findings supporting the idea that semantic representations are encoded in distributed yet specialized cortical areas (1), and rapidly recovered (2) according to the requirement of the task at hand (3). I will then focus on studies conducted in neurodegenerative patients, offering a unique window on the key role played by a structurally and functionally heterogeneous piece of cortex: the anterior temporal lobe (4,5). I will present pathological, neuroimaging, cognitive, and behavioral data illustrating how damages to language-related networks can affect or spare semantic knowledge as well as possible paths to functional compensation (6,7). Time permitting, we will discuss the neurocognitive dissociation between nouns and verbs (8) and how verb production is differentially impacted by specific language impairments (9).

We will explore how neuropsychology can be leveraged to directly test cognitive neuroscience theories using the case of frontotemporal dementias affecting the language network. Specifically, we will focus on pathological, neuroimaging, and cognitive data from primary progressive aphasia. We will see how they can help us investigate the reading network, semantic knowledge organisation, and grammatical categories processing. Time permitting, the end of the talk will cover the temporal dynamics of semantic dimensions recovery and the role played by the task.