Use case determines the validity of neural systems comparisons

Deep learning provides new data-driven tools to relate neural activity to perception and cognition, aiding scientists in developing theories of neural computation that increasingly resemble biological systems both at the level of behavior and of neural activity. But what in a deep neural network should correspond to what in a biological system? This question is addressed implicitly in the use of comparison measures that relate specific neural or behavioral dimensions via a particular functional form. However, distinct comparison methodologies can give conflicting results in recovering even a known ground-truth model in an idealized setting, leaving open the question of what to conclude from the outcome of a systems comparison using any given methodology. Here, we develop a framework to make explicit and quantitative the effect of both hypothesis-driven aspects—such as details of the architecture of a deep neural network—as well as methodological choices in a systems comparison setting. We demonstrate via the learning dynamics of deep neural networks that, while the role of the comparison methodology is often de-emphasized relative to hypothesis-driven aspects, this choice can impact and even invert the conclusions to be drawn from a comparison between neural systems. We provide evidence that the right way to adjudicate a comparison depends on the use case—the scientific hypothesis under investigation—which could range from identifying single-neuron or circuit-level correspondences to capturing generalizability to new stimulus properties

How does the brain analyse sensory information and learns from it?

Introducing exciting methods that enable neuroscientists to look deep into the living brain, allowing us to study how the brain's neural networks learn and process sensory information.

Mice identify subgoals locations through an action-driven mapping process

Mammals instinctively explore and form mental maps of their spatial environments. Models of cognitive mapping in neuroscience mostly depict map-learning as a process of random or biased diffusion. In practice, however, animals explore spaces using structured, purposeful, sensory-guided actions. We have used threat-evoked escape behavior in mice to probe the relationship between ethological exploratory behavior and abstract spatial cognition. First, we show that in arenas with obstacles and a shelter, mice spontaneously learn efficient multi-step escape routes by memorizing allocentric subgoal locations. Using closed-loop neural manipulations to interrupt running movements during exploration, we next found that blocking runs targeting an obstacle edge abolished subgoal learning. We conclude that mice use an action-driven learning process to identify subgoals, and these subgoals are then integrated into an allocentric map-like representation. We suggest a conceptual framework for spatial learning that is compatible with the successor representation from reinforcement learning and sensorimotor enactivism from cognitive science.

NMC4 Short Talk: Sensory intermixing of mental imagery and perception

Several lines of research have demonstrated that internally generated sensory experience - such as during memory, dreaming and mental imagery - activates similar neural representations as externally triggered perception. This overlap raises a fundamental challenge: how is the brain able to keep apart signals reflecting imagination and reality? In a series of online psychophysics experiments combined with computational modelling, we investigated to what extent imagination and perception are confused when the same content is simultaneously imagined and perceived. We found that simultaneous congruent mental imagery consistently led to an increase in perceptual presence responses, and that congruent perceptual presence responses were in turn associated with a more vivid imagery experience. Our findings can be best explained by a simple signal detection model in which imagined and perceived signals are added together. Perceptual reality monitoring can then easily be implemented by evaluating whether this intermixed signal is strong or vivid enough to pass a ‘reality threshold’. Our model suggests that, in contrast to self-generated sensory changes during movement, our brain does not discount self-generated sensory signals during mental imagery. This has profound implications for our understanding of reality monitoring and perception in general.

Inhibitory circuits in sensory processing and behaviour

Multisensory encoding of self-motion in the retrosplenial cortex and beyond

In order to successfully navigate through the environment, animals must accurately estimate the status of their motion with respect to the surrounding scene and objects. In this talk, I will present our recent work on how retrosplenial cortical (RSC) neurons combine vestibular and visual signals to reliably encode the direction and speed of head turns during passive motion and active navigation. I will discuss these data in the context of RSC long-range connectivity and further show our ongoing work on building population-level models of motion representation across cortical and subcortical networks.

Neural Circuit Mechanisms for Navigating to Shelter During Instinctive Escape

Connectivity and computation in the cerebral cortex

Neuroscientists believe that perception, action and cognition arise from brain’s activity. A major challenge in neuroscience is to understand how brain’s complex circuits give rise to activity patterns that support these different functions. I will discuss different ways of mapping neural circuits in the brain, and how we can relate the structure of neural circuits to the computations that take place within them, with an emphasis on the visual system.

Understanding sensorimotor control at global and local scales

The brain is remarkably flexible, and appears to instantly reconfigure its processing depending on what’s needed to solve a task at hand: fMRI studies indicate that distal brain areas appear to fluidly couple and decouple with one another depending on behavioral context. But the structural architecture of the brain is comprised of long-range axonal projections that are relatively fixed by adulthood. How does the global dynamism evident in fMRI recordings manifest at a cellular level? To bridge the gap between the activity of single neurons and cortex-wide networks, we correlated electrophysiological recordings of individual neurons in primary visual (V1) and retrosplenial (RSP) associational cortex with activity across dorsal cortex, recorded simultaneously using widefield calcium imaging. We found that individual neurons in both cortical areas independently engaged in different distributed cortical networks depending on the animal’s behavioral state, suggesting that locomotion puts cortex into a more sensory driven mode relevant for navigation.

Study of sensory "prior distributions" in rodent models of working memory and perceptual decision making

From oscillations to laminar responses - characterising the neural circuitry of autobiographical memories

Autobiographical memories are the ghosts of our past. Through them we visit places long departed, see faces once familiar, and hear voices now silent. These, often decades-old, personal experiences can be recalled on a whim or come unbidden into our everyday consciousness. Autobiographical memories are crucial to cognition because they facilitate almost everything we do, endow us with a sense of self and underwrite our capacity for autonomy. They are often compromised by common neurological and psychiatric pathologies with devastating effects. Despite autobiographical memories being central to everyday mental life, there is no agreed model of autobiographical memory retrieval, and we lack an understanding of the neural mechanisms involved. This precludes principled interventions to manage or alleviate memory deficits, and to test the efficacy of treatment regimens. This knowledge gap exists because autobiographical memories are challenging to study – they are immersive, multi-faceted, multi-modal, can stretch over long timescales and are grounded in the real world. One missing piece of the puzzle concerns the millisecond neural dynamics of autobiographical memory retrieval. Surprisingly, there are very few magnetoencephalography (MEG) studies examining such recall, despite the important insights this could offer into the activity and interactions of key brain regions such as the hippocampus and ventromedial prefrontal cortex. In this talk I will describe a series of MEG studies aimed at uncovering the neural circuitry underpinning the recollection of autobiographical memories, and how this changes as memories age. I will end by describing our progress on leveraging an exciting new technology – optically pumped MEG (OP-MEG) which, when combined with virtual reality, offers the opportunity to examine millisecond neural responses from the whole brain, including deep structures, while participants move within a virtual environment, with the attendant head motion and vestibular inputs.

Higher-order thalamocortical interactions during visual processing