An object’s colour, brightness and pattern are all influenced by its surroundings, and a number of visual phenomena and “illusions” have been discovered that highlight these often dramatic effects. Explanations for these phenomena range from low-level neural mechanisms to high-level processes that incorporate contextual information or prior knowledge. Importantly, few of these phenomena can currently be accounted for when measuring an object’s perceived colour. Here we ask to what extent colour appearance is predicted by a model based on the principle of coding efficiency. The model assumes that the image is encoded by noisy spatio-chromatic filters at one octave separations, which are either circularly symmetrical or oriented. Each spatial band’s lower threshold is set by the contrast sensitivity function, and the dynamic range of the band is a fixed multiple of this threshold, above which the response saturates. Filter outputs are then reweighted to give equal power in each channel for natural images. We demonstrate that the model fits human behavioural performance in psychophysics experiments, and also primate retinal ganglion responses. Next we systematically test the model’s ability to qualitatively predict over 35 brightness and colour phenomena, with almost complete success. This implies that contrary to high-level processing explanations, much of colour appearance is potentially attributable to simple mechanisms evolved for efficient coding of natural images, and is a basis for modelling the vision of humans and other animals.

During perception the visual system integrates current sensory evidence with previously acquired knowledge of the visual world. Presumably this computation relies on internal recurrent interactions. We record populations of neurons from the primary visual cortex of cats and macaque monkeys and find evidence for adaptive internal responses to structured stimulation that change on both slow and fast timescales. In the first experiment, we present abstract images, only briefly, a protocol known to produce strong and persistent recurrent responses in the primary visual cortex. We show that repetitive presentations of a large randomized set of images leads to enhanced stimulus encoding on a timescale of minutes to hours. The enhanced encoding preserves the representational details required for image reconstruction and can be detected in post-exposure spontaneous activity. In a second experiment, we show that the encoding of natural scenes across populations of V1 neurons is improved, over a timescale of hundreds of milliseconds, with the allocation of spatial attention. Given the hierarchical organization of the visual cortex, contextual information from the higher levels of the processing hierarchy, reflecting high-level image regularities, can inform the activity in V1 through feedback. We hypothesize that these fast attentional boosts in stimulus encoding rely on recurrent computations that capitalize on the presence of high-level visual features in natural scenes. We design control images dominated by low-level features and show that, in agreement with our hypothesis, the attentional benefits in stimulus encoding vanish. We conclude that, in the visual system, powerful recurrent processes optimize neuronal responses, already at the earliest stages of cortical processing.

Low-dimensional population dynamics can be observed in neural activity recorded from the prefrontal cortex (PFC) of subjects performing simple cognitive tasks. Many studies have shown that recurrent neural networks (RNNs) trained on the same tasks can reproduce qualitatively these state space trajectories, and have used them as models of how neuronal dynamics implement task computations. The PFC is also viewed as a conductor that organizes the communication between cortical areas and provides contextual information. It is then not clear what is its role in solving simple cognitive tasks. Do the low-dimensional trajectories observed in the PFC really correspond to the computations that it performs? Or do they indirectly reflect the computations occurring within the cortical areas projecting to the PFC? To address these questions, we modelled cortical areas with a modular RNN and equipped it with a PFC-like cognitive system. When trained on cognitive tasks, this multi-system brain model can reproduce the low-dimensional population responses observed in neuronal activity as well as classical RNNs. Qualitatively different mechanisms can emerge from the training process when varying some details of the architecture such as the time constants. In particular, there is one class of models where it is the dynamics of the cognitive system that is implementing the task computations, and another where the cognitive system is only necessary to provide contextual information about the task rule as task performance is not impaired when preventing the system from accessing the task inputs. These constitute two different hypotheses about the causal role of the PFC in solving simple cognitive tasks, which could motivate further experiments on the brain.

Our work concerns the general problem of adaptive behaviour in response to predatory threats, and of the neural mechanisms underlying a choice between strategies. When faced with a threat, an animal must decide whether to freeze, reducing its chances of being noticed, or to flee to the safety of a refuge. Animals from fish to primates choose between these two alternatives when confronted by an attacking predator, a choice that largely depends on the context in which the threat occurs. Recent work has made strides identifying the pre-motor circuits, and their inputs, which control freezing behaviour in rodents, but how contextual information is integrated to guide this choice is still far from understood. The social environment is a potent contextual modulator of defensive behaviours of animals in a group. Indeed, anti-predation strategies are believed to be a major driving force for the evolution of sociality. We recently found that fruit flies in response to visual looming stimuli, simulating a large object on collision course, make rapid freeze/flee choices accompanied by lasting changes in the fly’s internal state, reflected in altered cardiac activity. In this talk, I will discuss our work on how flies process contextual cues, focusing on the social environment, to guide their behavioural response to a threat. We have identified a social safety cue, resumption of activity, and visual projection neurons involved in processing this cue. Given the knowledge regarding sensory detection of looming threats and descending neuron involved in the expression of freezing, we are now in a unique position to understand how information about a threat is integrated with cues from the social environment to guide the choice of whether to freeze.

This seminar is part of our “Emergent Scientists” series, an initiative that provides a platform for scientists at the critical PhD/postdoc transition period to share their work with a broad audience and network. Summary: These talks cover Alzheimer’s disease (AD) research in both mice and humans. Christiana will discuss in particular the translational aspects of applying mouse work to humans and the importance of timing in disease pathology and intervention (e.g. timing between AD biomarkers vs. symptom onset, timing of therapy, etc.). Siddharth will discuss a rare variant of Alzheimer’s disease called “Logopenic Progressive Aphasia”, which presents with temporo-parietal atrophy yet relative sparing of hippocampal circuitry. Siddharth will discuss how, despite the unusual anatomical basis underlying this AD variant, degeneration of the angular gyrus in the left inferior parietal lobule contributes to memory deficits similar to those of typical amnesic Alzheimer’s disease. Christiana’s abstract: Alzheimer’s disease (AD) is a debilitating neurodegenerative disorder that causes severe deterioration of memory, cognition, behavior, and the ability to perform daily activities. The disease is characterized by the accumulation of two proteins in fibrillar form; Amyloid-β forms fibrils that accumulate as extracellular plaques while tau fibrils form intracellular tangles. Here we aim to translate findings from a commonly used AD mouse model to AD patients. Here we initiate and chronically inhibit neuropathology in lateral entorhinal cortex (LEC) layer two neurons in an AD mouse model. This is achieved by over-expressing P301L tau virally and chronically activating hM4Di DREADDs intracranially using the ligand dechloroclozapine. Biomarkers in cerebrospinal fluid (CSF) is measured longitudinally in the model using microdialysis, and we use this same system to intracranially administer drugs aimed at halting AD-related neuropathology. The models are additionally tested in a novel contextual memory task. Preliminary findings indicate that viral injections of P301L tau into LEC layer two reveal direct projections between this region and the outer molecular layer of dentate gyrus and the rest of hippocampus. Additionally, phosphorylated tau co-localize with ‘starter cells’ and appear to spread from the injection site. Preliminary microdialysis results suggest that the concentrations of CSF amyloid-β and tau proteins mirror changes observed along the disease cascade in patients. The disease-modifying drugs appear to halt neuropathological development in this preclincial model. These findings will lead to a novel platform for translational AD research, linking the extensive research done in rodents to clinical applications. Siddharth’s abstract: A distributed brain network supports our ability to remember past events. The parietal cortex is a critical member of this network, yet, its exact contributions to episodic remembering remain unclear. Neurodegenerative syndromes affecting the posterior neocortex offer a unique opportunity to understand the importance and role of parietal regions to episodic memory. In this talk, I introduce and explore the rare neurodegenerative syndrome of Logopenic Progressive Aphasia (LPA), an aphasic variant of Alzheimer’s disease presenting with early, left-lateralized temporo-parietal atrophy, amidst relatively spared hippocampal integrity. I then discuss two key studies from my recent Ph.D. work showcasing pervasive episodic and autobiographical memory dysfunction in LPA, to a level comparable to typical, amnesic Alzheimer’s disease. Using multimodal neuroimaging, I demonstrate how degeneration of the angular gyrus in the left inferior parietal lobule, and its structural connections to the hippocampus, contribute to amnesic profiles in this syndrome. I finally evaluate these findings in the context of memory profiles in other posterior cortical neurodegenerative syndromes as well as recent theoretical models underscoring the importance of the parietal cortex in the integration and representation of episodic contextual information.