Top-down control of neocortical threat memory
Accurate perception of the environment is a constructive process that requires integration of external bottom-up sensory signals with internally-generated top-down information reflecting past experiences and current aims. Decades of work have elucidated how sensory neocortex processes physical stimulus features. In contrast, examining how memory-related-top-down information is encoded and integrated with bottom-up signals has long been challenging. Here, I will discuss our recent work pinpointing the outermost layer 1 of neocortex as a central hotspot for processing of experience-dependent top-down information threat during perception, one of the most fundamentally important forms of sensation.
Neural architectures: what are they good for anyway?
The brain has a highly complex structure in terms of cell types and wiring between different regions. What is it for, if anything? I'll start this talk by asking what might an answer to this question even look like given that we can't run an alternative universe where our brains are structured differently. (Preview: we can do this with models!) I'll then talk about some of our work in two areas: (1) does the modular structure of the brain contribute to specialisation of function? (2) how do different cell types and architectures contribute to multimodal sensory processing?
Rethinking Attention: Dynamic Prioritization
Decades of research on understanding the mechanisms of attentional selection have focused on identifying the units (representations) on which attention operates in order to guide prioritized sensory processing. These attentional units fit neatly to accommodate our understanding of how attention is allocated in a top-down, bottom-up, or historical fashion. In this talk, I will focus on attentional phenomena that are not easily accommodated within current theories of attentional selection – the “attentional platypuses,” as they allude to an observation that within biological taxonomies the platypus does not fit into either mammal or bird categories. Similarly, attentional phenomena that do not fit neatly within current attentional models suggest that current models need to be revised. I list a few instances of the ‘attentional platypuses” and then offer a new approach, the Dynamically Weighted Prioritization, stipulating that multiple factors impinge onto the attentional priority map, each with a corresponding weight. The interaction between factors and their corresponding weights determines the current state of the priority map which subsequently constrains/guides attention allocation. I propose that this new approach should be considered as a supplement to existing models of attention, especially those that emphasize categorical organizations.
Measures and models of multisensory integration in reaction times
First, a new measure of MI for reaction times is proposed that takes the entire RT distribution into account. Second, we present some recent developments in TWIN modeling, including a new proposal for the sound-induced flash illusion (SIFI).
Trial by trial predictions of subjective time from human brain activity
Our perception of time isn’t like a clock; it varies depending on other aspects of experience, such as what we see and hear in that moment. However, in everyday life, the properties of these simple features can change frequently, presenting a challenge to understanding real-world time perception based on simple lab experiments. We developed a computational model of human time perception based on tracking changes in neural activity across brain regions involved in sensory processing, using fMRI. By measuring changes in brain activity patterns across these regions, our approach accommodates the different and changing feature combinations present in natural scenarios, such as walking on a busy street. Our model reproduces people’s duration reports for natural videos (up to almost half a minute long) and, most importantly, predicts whether a person reports a scene as relatively shorter or longer–the biases in time perception that reflect how natural experience of time deviates from clock time
Hunger state-dependent modulation of decision-making in larval Drosophila
It is critical for all animals to make appropriate, but also flexible, foraging decisions, especially when facing starvation. Sensing olfactory information is essential to evaluate food quality before ingestion. Previously, we found that <i>Drosophila</i> larvae switch their response to certain odors from aversion to attraction when food deprived. The neural mechanism underlying this switch in behavior involves serotonergic modulation and reconfiguration of odor processing in the early olfactory sensory system. We now investigate if a change in hunger state also influences other behavioral decisions. Since it had been shown that fly larvae can perform cannibalism, we investigate the effect of food deprivation on feeding on dead conspecifics. We find that fed fly larvae rarely use dead conspecifics as a food source. However, food deprivation largely enhances this behavior. We will now also investigate the underlying neural mechanisms that mediate this enhancement and compare it to the already described mechanism for a switch in olfactory choice behavior. Generally, this flexibility in foraging behavior enables the larva to explore a broader range of stimuli and to expand their feeding choices to overcome starvation.
Disentangling neural correlates of consciousness and task relevance using EEG and fMRI
How does our brain generate consciousness, that is, the subjective experience of what it is like to see face or hear a sound? Do we become aware of a stimulus during early sensory processing or only later when information is shared in a wide-spread fronto-parietal network? Neural correlates of consciousness are typically identified by comparing brain activity when a constant stimulus (e.g., a face) is perceived versus not perceived. However, in most previous experiments, conscious perception was systematically confounded with post-perceptual processes such as decision-making and report. In this talk, I will present recent EEG and fMRI studies dissociating neural correlates of consciousness and task-related processing in visual and auditory perception. Our results suggest that consciousness emerges during early sensory processing, while late, fronto-parietal activity is associated with post-perceptual processes rather than awareness. These findings challenge predominant theories of consciousness and highlight the importance of considering task relevance as a confound across different neuroscientific methods, experimental paradigms and sensory modalities.
Hierarchical transformation of visual event timing representations in the human brain: response dynamics in early visual cortex and timing-tuned responses in association cortices
Quantifying the timing (duration and frequency) of brief visual events is vital to human perception, multisensory integration and action planning. For example, this allows us to follow and interact with the precise timing of speech and sports. Here we investigate how visual event timing is represented and transformed across the brain’s hierarchy: from sensory processing areas, through multisensory integration areas, to frontal action planning areas. We hypothesized that the dynamics of neural responses to sensory events in sensory processing areas allows derivation of event timing representations. This would allow higher-level processes such as multisensory integration and action planning to use sensory timing information, without the need for specialized central pacemakers or processes. Using 7T fMRI and neural model-based analyses, we found responses that monotonically increase in amplitude with visual event duration and frequency, becoming increasingly clear from primary visual cortex to lateral occipital visual field maps. Beginning in area MT/V5, we found a gradual transition from monotonic to tuned responses, with response amplitudes peaking at different event timings in different recording sites. While monotonic response components were limited to the retinotopic location of the visual stimulus, timing-tuned response components were independent of the recording sites' preferred visual field positions. These tuned responses formed a network of topographically organized timing maps in superior parietal, postcentral and frontal areas. From anterior to posterior timing maps, multiple events were increasingly integrated, response selectivity narrowed, and responses focused increasingly on the middle of the presented timing range. These results suggest that responses to event timing are transformed from the human brain’s sensory areas to the association cortices, with the event’s temporal properties being increasingly abstracted from the response dynamics and locations of early sensory processing. The resulting abstracted representation of event timing is then propagated through areas implicated in multisensory integration and action planning.
Feedback controls what we see
We hardly notice when there is a speck on our glasses, the obstructed visual information seems to be magically filled in. The visual system uses visual context to predict the content of the stimulus. What enables neurons in the visual system to respond to context when the stimulus is not available? In cortex, sensory processing is based on a combination of feedforward information arriving from sensory organs, and feedback information that originates in higher-order areas. Whereas feedforward information drives the activity in cortex, feedback information is thought to provide contextual signals that are merely modulatory. We have made the exciting discovery that mouse primary visual cortical neurons are strongly driven by feedback projections from higher visual areas, in particular when their feedforward sensory input from the retina is missing. This drive is so strong that it makes visual cortical neurons fire as much as if they were receiving a direct sensory input.
Connecting structure and function in early visual circuits
How does the brain interpret signals from the outside world? Walking through a park, you might take for granted the ease with which you can understand what you see. Rather than seeing a series of still snapshots, you are able to see simple, fluid movement — of dogs running, squirrels foraging, or kids playing basketball. You can track their paths and know where they are headed without much thought. “How does this process take place?” asks Rudy Behnia, PhD, a principal investigator at Columbia’s Mortimer B. Zuckerman Mind Brain Behavior Institute. “For most of us, it’s hard to imagine a world where we can’t see motion, shapes, and color; where we can’t have a representation of the physical world in our head.” And yet this representation does not happen automatically — our brain has no direct connection with the outside world. Instead, it interprets information taken in by our senses. Dr. Behnia is studying how the brain builds these representations. As a starting point, she focuses on how we see motion
Attention to visual motion: shaping sensation into perception
Evolution has endowed primates, including humans, with a powerful visual system, seemingly providing us with a detailed perception of our surroundings. But in reality the underlying process is one of active filtering, enhancement and reshaping. For visual motion perception, the dorsal pathway in primate visual cortex and in particular area MT/V5 is considered to be of critical importance. Combining physiological and psychophysical approaches we have used the processing and perception of visual motion and area MT/V5 as a model for the interaction of sensory (bottom-up) signals with cognitive (top-down) modulatory influences that characterizes visual perception. Our findings document how this interaction enables visual cortex to actively generate a neural representation of the environment that combines the high-performance sensory periphery with selective modulatory influences for producing an “integrated saliency map’ of the environment.
Invariant neural subspaces maintained by feedback modulation
Sensory systems reliably process incoming stimuli in spite of changes in context. Most recent models accredit this context invariance to an extraction of increasingly complex sensory features in hierarchical feedforward networks. Here, we study how context-invariant representations can be established by feedback rather than feedforward processing. We show that feedforward neural networks modulated by feedback can dynamically generate invariant sensory representations. The required feedback can be implemented as a slow and spatially diffuse gain modulation. The invariance is not present on the level of individual neurons, but emerges only on the population level. Mechanistically, the feedback modulation dynamically reorients the manifold of neural activity and thereby maintains an invariant neural subspace in spite of contextual variations. Our results highlight the importance of population-level analyses for understanding the role of feedback in flexible sensory processing.
Synergy of color and motion vision for detecting approaching objects in Drosophila
I am working on color vision in Drosophila, identifying behaviors that involve color vision and understanding the neural circuits supporting them (Longden 2016). I have a long-term interest in understanding how neural computations operate reliably under changing circumstances, be they external changes in the sensory context, or internal changes of state such as hunger and locomotion. On internal state-modulation of sensory processing, I have shown how hunger alters visual motion processing in blowflies (Longden et al. 2014), and identified a role for octopamine in modulating motion vision during locomotion (Longden and Krapp 2009, 2010). On responses to external cues, I have shown how one kind of uncertainty in the motion of the visual scene is resolved by the fly (Saleem, Longden et al. 2012), and I have identified novel cells for processing translation-induced optic flow (Longden et al. 2017). I like working with colleagues who use different model systems, to get at principles of neural operation that might apply in many species (Ding et al. 2016, Dyakova et al. 2015). I like work motivated by computational principles - my background is computational neuroscience, with a PhD on models of memory formation in the hippocampus (Longden and Willshaw, 2007).
Being awake while sleeping, being asleep while awake: consequences on cognition and consciousness
Sleep is classically presented as an all-or-nothing phenomenon. Yet, there is increasing evidence showing that sleep and wakefulness can actually intermingle and that wake-like and sleep-like activity can be observed concomitantly in different brain regions. I will here explore the implications of this conception of sleep as a local phenomenon for cognition and consciousness. In the first part of my presentation, I will show how local modulations of sleep depth during sleep could support the processing of sensory information by sleepers. I will also how, under certain circumstances, sleepers can learn while sleeping but also how they can forget. In the second part, I will show how the reverse phenomenon, sleep intrusions during waking, can explain modulations of attention. I will focus in particular on modulations of subjective experience and how the local sleep framework can inform our understanding of everyday phenomena such as mind wandering and mind blanking. Through this presentation and the exploration of both sleep and wakefulness, I will seek to connect changes in neurophysiology with changes in behaviour and subjective experience.
Migraine: a disorder of excitatory-inhibitory balance in multiple brain networks? Insights from genetic mouse models of the disease
Migraine is much more than an episodic headache. It is a complex brain disorder, characterized by a global dysfunction in multisensory information processing and integration. In a third of patients, the headache is preceded by transient sensory disturbances (aura), whose neurophysiological correlate is cortical spreading depression (CSD). The molecular, cellular and circuit mechanisms of the primary brain dysfunctions that underlie migraine onset, susceptibility to CSD and altered sensory processing remain largely unknown and are major open issues in the neurobiology of migraine. Genetic mouse models of a rare monogenic form of migraine with aura provide a unique experimental system to tackle these key unanswered questions. I will describe the functional alterations we have uncovered in the cerebral cortex of genetic mouse models and discuss the insights into the cellular and circuit mechanisms of migraine obtained from these findings.
Understanding the role of prediction in sensory encoding
At any given moment the brain receives more sensory information than it can use to guide adaptive behaviour, creating the need for mechanisms that promote efficient processing of incoming sensory signals. One way in which the brain might reduce its sensory processing load is to encode successive presentations of the same stimulus in a more efficient form, a process known as neural adaptation. Conversely, when a stimulus violates an expected pattern, it should evoke an enhanced neural response. Such a scheme for sensory encoding has been formalised in predictive coding theories, which propose that recent experience establishes expectations in the brain that generate prediction errors when violated. In this webinar, Professor Jason Mattingley will discuss whether the encoding of elementary visual features is modulated when otherwise identical stimuli are expected or unexpected based upon the history of stimulus presentation. In humans, EEG was employed to measure neural activity evoked by gratings of different orientations, and multivariate forward modelling was used to determine how orientation selectivity is affected for expected versus unexpected stimuli. In mice, two-photon calcium imaging was used to quantify orientation tuning of individual neurons in the primary visual cortex to expected and unexpected gratings. Results revealed enhanced orientation tuning to unexpected visual stimuli, both at the level of whole-brain responses and for individual visual cortex neurons. Professor Mattingley will discuss the implications of these findings for predictive coding theories of sensory encoding. Professor Jason Mattingley is a Laureate Fellow and Foundation Chair in Cognitive Neuroscience at The University of Queensland. His research is directed toward understanding the brain processes that support perception, selective attention and decision-making, in health and disease.
Perceptual Inference, Uncertainty and Representation
To act effectively and flexibly in an imperfectly predictable environment with only incomplete and unreliable sensory information, animals must learn to form and compute with internal representations that reflect their necessarily uncertain beliefs about the state of the world. The optimal approach to handling uncertainty is rooted in Bayesian probability, and indeed humans and other animals often approach Bayes optimality with a degree of robustness and flexibility that continues to evade artificial systems. However, the question of how neural circuits organise to achieve this performance remains one of the fundamental mysteries of neuroscience. I will discuss a series of models built around the idea that distributional information is naturally encoded in a distributed fashion by neural population firing rates that converge on the mean values of non-linear functions of state. We will see that such representations emerge naturally in task-optimised systems, and also provide a simple and effective substrate for unsupervised learning. Finally, I will sketch ongoing work that links the emergence of such representations to the architecture of recurrent neural circuits. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2021-07-07. Recording duration: 00:52:28.
Workshop: Spatial Brain Dynamics
Traditionally, the term dynamics means changes in a system evolving over time. However, in the brain action potentials propagate along axons to induce postsynaptic currents with different delays at many sites simultaneously. This fundamental computational mechanism evolves spatially to engage the neuron populations involved in brain functions. To identify and understand the spatial processing in brains, this workshop will focus on the spatial principles of brain dynamics that determine how action potentials and membrane currents propagate in the networks of neurons that brains are made of. We will focus on non-artificial dynamics, which excludes in vitro dynamics, interference, electrical and optogenetic stimulations of brains in vivo. Recent non-artificial studies of spatial brain dynamics can actually explain how sensory, motor and internal brain functions evolve. The purpose of this workshop is to discuss these recent results and identify common principles of spatial brain dynamics.
Neural codes in early sensory areas maximize fitness
It has generally been presumed that sensory information encoded by a nervous system should be as accurate as its biological limitations allow. However, perhaps counter intuitively, accurate representations of sensory signals do not necessarily maximize the organism’s chances of survival. We show that neural codes that maximize reward expectation—and not accurate sensory representations—account for retinal responses in insects, and retinotopically-specific adaptive codes in humans. Thus, our results provide evidence that fitness-maximizing rules imposed by the environment are applied at the earliest stages of sensory processing.
Workshop: Spatial Brain Dynamics
Traditionally, the term dynamics means changes in a system evolving over time. However, in the brain action potentials propagate along axons to induce postsynaptic currents with different delays at many sites simultaneously. This fundamental computational mechanism evolves spatially to engage the neuron populations involved in brain functions. To identify and understand the spatial processing in brains, this workshop will focus on the spatial principles of brain dynamics that determine how action potentials and membrane currents propagate in the networks of neurons that brains are made of. We will focus on non-artificial dynamics, which excludes in vitro dynamics, interference, electrical and optogenetic stimulations of brains in vivo. Recent non-artificial studies of spatial brain dynamics can actually explain how sensory, motor and internal brain functions evolve. The purpose of this workshop is to discuss these recent results and identify common principles of spatial brain dynamics.
Workshop: Spatial Brain Dynamics
Traditionally, the term dynamics means changes in a system evolving over time. However, in the brain action potentials propagate along axons to induce postsynaptic currents with different delays at many sites simultaneously. This fundamental computational mechanism evolves spatially to engage the neuron populations involved in brain functions. To identify and understand the spatial processing in brains, this workshop will focus on the spatial principles of brain dynamics that determine how action potentials and membrane currents propagate in the networks of neurons that brains are made of. We will focus on non-artificial dynamics, which excludes in vitro dynamics, interference, electrical and optogenetic stimulations of brains in vivo. Recent non-artificial studies of spatial brain dynamics can actually explain how sensory, motor and internal brain functions evolve. The purpose of this workshop is to discuss these recent results and identify common principles of spatial brain dynamics.
Contextual modulation of cortical processing by a higher-order thalamic input
Higher-order thalamic nuclei have extensive connections with various cortical areas. Yet their functionals roles remain not well understood. In our recent studies, using optogenetic and chemogenetic tools we manipulated the activity of a higher-order thalamic nucleus, the lateral posterior nucleus (LP, analogous to the primate pulvinar nucleus) and its projections and examined the effects on sensory discrimination and information processing functions in the cortex. We found an overall suppressive effect on layer 2/3 pyramidal neurons in the cortex, resulting in enhancements of sensory feature selectivities. These mechanisms are in place in contextual modulation of cortical processing, as well as in cross-modality modulation of sensory processing.
Cholinergic regulation of learning in the olfactory system
In the olfactory system, cholinergic modulation has been associated with contrast modulation and changes in receptive fields in the olfactory bulb, as well the learning of odor associations in the olfactory cortex. Computational modeling and behavioral studies suggest that cholinergic modulation could improve sensory processing and learning while preventing pro-active interference when task demands are high. However, how sensory inputs and/or learning regulate incoming modulation has not yet been elucidated. We here use a computational model of the olfactory bulb, piriform cortex (PC) and horizontal limb of the diagonal band of Broca (HDB) to explore how olfactory learning could regulate cholinergic inputs to the system in a closed feedback loop. In our model, the novelty of an odor is reflected in firing rates and sparseness of cortical neurons in response to that odor and these firing rates can directly regulate learning in the system by modifying cholinergic inputs to the system.
Circuit dysfunction and sensory processing in Fragile X Syndrome
To uncover the circuit-level alterations that underlie atypical sensory processing associated with autism, we have adopted a symptom-to-circuit approach in theFmr1-/- mouse model of Fragile X syndrome (FXS). Using a go/no-go task and in vivo 2-photon calcium imaging, we find that impaired visual discrimination in Fmr1-/- mice correlates with marked deficits in orientation tuning of principal neurons in primary visual cortex, and a decrease in the activity of parvalbumin (PV) interneurons. Restoring visually evoked activity in PV cells in Fmr1-/- mice with a chemogenetic (DREADD) strategy was sufficient to rescue their behavioural performance. Strikingly, human subjects with FXS exhibit similar impairments in visual discrimination as Fmr1-/- mice. These results suggest that manipulating inhibition may help sensory processing in FXS. More recently, we find that the ability of Fmr1-/- mice to perform the visual discrimination task is also drastically impaired in the presence of visual or auditory distractors, suggesting that sensory hypersensitivity may affect perceptual learning in autism.
Multi-layer network learning in an electric fish
The electrosensory lobe (ELL) in mormyrid electric fish is a cerebellar-like structure that cancels the sensory effects of self-generated electric fields, allowing prey to be detected. Like the cerebellum, the ELL involves two stages of processing, analogous to the Purkinje cells and cells of the deep cerebellar nuclei. Through the work of Curtis Bell and others, a model was previously developed to describe the output stage of the ELL, but the role of the Purkinje-cell analogs, the medium ganglion (MG) cells, in the circuit had remained mysterious. I will present a complete, multi-layer circuit description of the ELL, developed in collaboration with Nate Sawtell and Salomon Muller, that reveals a novel role for the MG cells. The resulting model provides an example of how a biological system solves well-known problems associated with learning in multi-layer networks, and it reveals that ELL circuitry is organization on the basis of learning rather than by the response properties of neurons.
Algorithms and circuits for olfactory navigation in walking Drosophila
Olfactory navigation provides a tractable model for studying the circuit basis of sensori-motor transformations and goal-directed behaviour. Macroscopic organisms typically navigate in odor plumes that provide a noisy and uncertain signal about the location of an odor source. Work in many species has suggested that animals accomplish this task by combining temporal processing of dynamic odor information with an estimate of wind direction. Our lab has been using adult walking Drosophila to understand both the computational algorithms and the neural circuits that support navigation in a plume of attractive food odor. We developed a high-throughput paradigm to study behavioural responses to temporally-controlled odor and wind stimuli. Using this paradigm we found that flies respond to a food odor (apple cider vinegar) with two behaviours: during the odor they run upwind, while after odor loss they perform a local search. A simple computational model based one these two responses is sufficient to replicate many aspects of fly behaviour in a natural turbulent plume. In on-going work, we are seeking to identify the neural circuits and biophysical mechanisms that perform the computations delineated by our model. Using electrophysiology, we have identified mechanosensory neurons that compute wind direction from movements of the two antennae and central mechanosensory neurons that encode wind direction are are involved in generating a stable downwind orientation. Using optogenetic activation, we have traced olfactory circuits capable of evoking upwind orientation and offset search from the periphery, through the mushroom body and lateral horn, to the central complex. Finally, we have used optogenetic activation, in combination with molecular manipulation of specific synapses, to localize temporal computations performed on the odor signal to olfactory transduction and transmission at specific synapses. Our work illustrates how the tools available in fruit fly can be applied to dissect the mechanisms underlying a complex goal-directed behaviour.