Past events

Background: The aim of anaesthesia is to eliminate awareness and prevent memory of the various aversive stimuli of medical procedures. Yet in a portion of cases, patients can recall events that occurred during surgery resulting in risks of adverse psychological outcomes. Fear conditioning offers a robust behavioral model to study this phenomenon, while the abundant evidence implicating the amygdala-medial prefrontal cortex (mPFC) circuit in acquisition, consolidation and retrieval of these memories offers a natural hypothesis for the neural mechanisms. Objective: We aimed to study the effect of anaesthesia on stimulus valence, acquisition and memory and to identify the correlates in the mPFC-amygdala circuit using a primate model and clinically relevant doses of anesthesia. Materials and methods: Two non-human primates acquired aversive memories by tone-odor classical conditioning under anesthesia with different doses of ketamine, a non-competitive antagonist of NMDA and midazolam, a GABA agonist. Both agents are in wide clinical use. We simultaneously recorded single neurons in the BLA and mPFC. Analyses focused on behavioral and neural evidence suggesting maintained valence, acquisition and retention of memory. Results: Seventy-six full sessions from two non-human primates entered analysis. We recorded 172 amygdala and 189 dACC neurons respectively. We found evidence of successful aversive conditioning under both anesthetics and in all doses. Under anesthesia, we found behavioral evidence of retention in 46% of sessions matched by a complementary response of 16.2% and 18.7% of amygdala and mPFC neurons respectively. An increased and escalating amygdala and mPFC response during acquisition predicted later retention and correlated the behavioral result. The behavioral and neural representation of aversive valence was sufficient to drive learning and affected conditioning outcome. Conclusion: Our results suggest that under anesthesia, the perception of stimuli and implicit aversive memory formation may be maintained. We show patterns in the amygdala-mPFC circuit that precede and predict this phenomenon and that may serve future monitoring strategies of anesthetized patients. The use of a primate model and therapeutic doses of common anesthetics affecting both GABA and NMDA transmission improves the possible translation of our findings.

Synapses change their strength in response to specific activity patterns. This functional plasticity is assumed to be the brain’s primary mechanism for information storage. We combine optogenetic and chemogenetic control of synapses in rat hippocampal slice cultures with calcium and glutamate imaging of their spines and boutons. This approach enables us to perform all-optical quantal analysis of synaptic transmission, to induce long-term potentiation (LTP), long-term depression (LTD), or both forms of plasticity in sequence, to chronically manipulate activity and to follow the fate of individual synapses for 7 days. We ask how plasticity and activity are integrated at Schaffer collateral synapses over time. Our findings suggest that activity-dependent changes in the transmission strength of individual synapses are transient, but have long-lasting consequences for synaptic lifetime.

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.