Past events

What does it feel like to be a bat? Is conscious experience of echolocation closer to that of vision or audition? Or, echolocation is non-conscious processing and it doesn't feel anything? This famous question of bats' experience, posed by a philosopher Thomas Nagel in 1974, clarifies the difficult nature of the mind-body problem. Why a particular sense, such as vision, has to feel like vision, but not like audition, is puzzling. This is especially so given that any conscious experience is supported by neuronal activity. Activity of a single neuron appears fairly uniform across modalities, and even similar to those for non-conscious processing. Without any explanation on why a particular sense has to feel as the way it does, researchers even cannot approach the question of the bats' experience. Is there any theory that gives us a hope for such explanation? Currently, probably none, except for one. Integrated Information Theory (IIT), proposed by Tononi in 2004 has a potential to offer a plausible explanation. IIT essentially claims that any system that is composed of causally interacting mechanisms can have conscious experience. And precisely how the system feels like is determined by the way the mechanisms influence each other in a holistic way. In this talk, I will give a brief explanation of the essence of IIT and provide initial empirical partial tests of the theory, proposing a potential scientific pathway to approach bats' conscious experience. If IIT, or its improved or related versions, is validated enough, it will gain credibility to accept its prediction on rough nature of bats' experience. If we can gain a sophisticated insight as to whether bats' experience is closer to vision or audition, it is already a tremendously big step in consciousness science, which is just a first yet critical one, possibly a similar level of the breakthrough in cosmology in precisely estimating the age of the universe. References: 0) talk slide: https://www.slideshare.net/NaoNaotsuguTsuchiya/17-june-20-empirical-test-of-iit-dresden 1) Andrew M. Haun, Masafumi Oizumi, Christopher K. Kovach, Hiroto Kawasaki, Hiroyuki Oya, Matthew A. Howard, Ralph Adolphs, Naotsugu Tsuchiya, (2017, accepted) “Conscious perception as integrated information patterns in human electrocorticography” eNeuro link 2) Tsuchiya “"What is it like to be a bat?" - a pathway to the answer from the Integrated Information Theory ” Philosophy Compass (2017) link 3) Oizumi M, Tsuchiya N, Amari S, “Unified framework for quantifying causality and integrated information in a dynamical system” (2016) PNAS link

The brain represents and reasons probabilistically about complex stimuli and motor actions using a noisy, spike-based neural code. A key building block for such neural computations, as well as the basis for supervised and unsupervised learning, is the ability to estimate the surprise or likelihood of incoming high-dimensional neural activity patterns. Despite progress in statistical modeling of neural responses and deep learning, current approaches either do not scale to large neural populations or cannot be implemented using biologically realistic mechanisms. Inspired by the sparse and random connectivity of real neuronal circuits, we present a new model for neural codes that accurately estimates the likelihood of individual spiking patterns from the joint activities of actual populations of cortical neurons. The model has a straightforward, scalable, efficiently learnable, and realistic neural implementation as either a randomly connected neural circuit or as single neuron with a random dendritic tree. In the corresponding implementation, a neuron can take advantage of random connectivity leading to it in order to autonomously learn the respond with the surprise of its input patterns based on the previous observed patterns. Importantly, it can be achieved using a local learning rule that utilizes noise intrinsic to neural circuits. Slower, structural changes in random connectivity, consistent with rewiring and pruning processes occurring on developmental time scales, can further improve the efficiency and sparseness of the resulting neural representations. Our results merge insights from neuroanatomy, machine learning, and theoretical neuroscience to suggest random sparse connectivity as a key design principle for neuronal computation.

Movement shapes our interactions with the world, providing a means to translate intent into action. Among the wide repertoire of mammalian motor behaviors, the precise coordination of limb muscles to propel arms, hands and digits through space with speed and precision represents one of the more impressive achievements of the motor system. Skilled forelimb movements emerge from interactions between feedforward command pathways that induce muscle contraction and feedback systems that report and refine movement. Two broad classes of feedback modify motor output: one that originates in the periphery, and a second that is generated within the central nervous system itself. Yet the mechanisms by which these feedback pathways influence forelimb movement remain poorly understood. We take advantage of the genetic tractability of mice to examine the organization of motor circuits and define the ways in which these pathways enable dexterous behaviors. First, I will discuss recent studies that explore the transmission of proprioceptive and cutaneous signals from the forelimb into the spinal cord and brainstem, describing neural circuits that modulate the strength of this peripheral feedback and the implications of this sensory gain control for limb movement. Second, I will describe work exploring a diverse class of spinal interneurons that we hypothesize convey copies of forelimb motor commands as internal feedback to the cerebellum, enabling online predictions of motor outcome and reducing dependence on delayed sensory information. Through a complementary set of molecular, anatomical, electrophysiological and behavioral approaches, these findings are yielding insight into the organizational and functional logic of peripheral and internal feedback, and revealing how the circuits that convey feedback information help to orchestrate skilled behavior.

The brain represents and reasons probabilistically about complex stimuli and motor actions using a noisy, spike-based neural code. A key building block for such neural computations, as well as the basis for supervised and unsupervised learning, is the ability to estimate the surprise or likelihood of incoming high-dimensional neural activity patterns. Despite progress in statistical modeling of neural responses and deep learning, current approaches either do not scale to large neural populations or cannot be implemented using biologically realistic mechanisms. Inspired by the sparse and random connectivity of real neuronal circuits, we present a new model for neural codes that accurately estimates the likelihood of individual spiking patterns from the joint activities of actual populations of cortical neurons. The model has a straightforward, scalable, efficiently learnable, and realistic neural implementation as either a randomly connected neural circuit or as single neuron with a random dendritic tree. In the corresponding implementation, a neuron can take advantage of random connectivity leading to it in order to autonomously learn the respond with the surprise of its input patterns based on the previous observed patterns. Importantly, it can be achieved using a local learning rule that utilizes noise intrinsic to neural circuits. Slower, structural changes in random connectivity, consistent with rewiring and pruning processes occurring on developmental time scales, can further improve the efficiency and sparseness of the resulting neural representations. Our results merge insights from neuroanatomy, machine learning, and theoretical neuroscience to suggest random sparse connectivity as a key design principle for neuronal computation.

Movement shapes our interactions with the world, providing a means to translate intent into action. Among the wide repertoire of mammalian motor behaviors, the precise coordination of limb muscles to propel arms, hands and digits through space with speed and precision represents one of the more impressive achievements of the motor system. Skilled forelimb movements emerge from interactions between feedforward command pathways that induce muscle contraction and feedback systems that report and refine movement. Two broad classes of feedback modify motor output: one that originates in the periphery, and a second that is generated within the central nervous system itself. Yet the mechanisms by which these feedback pathways influence forelimb movement remain poorly understood. We take advantage of the genetic tractability of mice to examine the organization of motor circuits and define the ways in which these pathways enable dexterous behaviors. First, I will discuss recent studies that explore the transmission of proprioceptive and cutaneous signals from the forelimb into the spinal cord and brainstem, describing neural circuits that modulate the strength of this peripheral feedback and the implications of this sensory gain control for limb movement. Second, I will describe work exploring a diverse class of spinal interneurons that we hypothesize convey copies of forelimb motor commands as internal feedback to the cerebellum, enabling online predictions of motor outcome and reducing dependence on delayed sensory information. Through a complementary set of molecular, anatomical, electrophysiological and behavioral approaches, these findings are yielding insight into the organizational and functional logic of peripheral and internal feedback, and revealing how the circuits that convey feedback information help to orchestrate skilled behavior.

"Sensory disconnection" – conditions when the same sensory stimulus does not reliably affect behavior or subjective experience - is a defining feature of sleep, and similar processes may occur during light anesthesia or during cognitive lapses in wakefulness. What are the changes in brain activity that mediate sensory disconnection? In a series of studies in humans and rodents, we investigate how "disconnected" states affect sensory processing. The first set of studies reveals differences in neuronal responses to identical sensory stimuli across states. We find that in humans, cognitive lapses after sleep deprivation involve attenuated and delayed single-neuron responses in MTL co-occurring with local slow/theta waves. In the auditory domain, we show in both rodents and humans that responses in sleep and light anesthesia are preserved up to A1, challenging the classic "thalamic gating" notion, but robust attenuation occurs later in high-level cortical regions. In addition, sleep affects more strongly responses that require integration over long time intervals, and responses to high-frequency content. The second set of studies investigates the underlying mechanisms, testing the potential role of locus coeruleus-noradrenaline (LC-NE) neuromodulation. In rats, we test how NE signaling affects the probability to wake up from sleep in response to sounds. We establish a new approach for selective in-vivo LC optogenetics by showing effects on spiking activity, evoked sleep-wake transitions, and pupil dilation. Combined LC and auditory stimulation synergistically increases the probability of awakenings beyond independent effects of sound and laser alone, supporting a role for LC-NE activity in mediating sensory responses. We also tested the effects of NE levels on sensory perception and sensory-evoked activity (EEG, fMRI) in awake humans. Pharmacologically manipulating NE levels in double-blind placebo-controlled experiments, we found that NE modulates sensitivity and accuracy of visual perception without significant effects on decision bias (criterion). In addition, NE increased the fidelity of late EEG visual responses, and selectively modulated BOLD fMRI responses in high-order visual cortex, suggesting that NE plays an enabling causal role in visual awareness by affecting late visual processing.