Neuroscience seminars

Cognitive bias is a "subjective reality" that is uniquely created in the brain and affects our various behaviors. It may lead to what is widely called irrationality in behavioral economics, such as inaccurate judgment and illogical interpretation, but it also has an adaptive aspect in terms of mental hygiene. When such cognitive bias is regarded as a product of information processing in the brain, the approach to clarify the mechanism in the brain will play a part in finding the direct relations between the brain and the mind. In my talk, I will introduce our studies investigating the neural and molecular bases of cognitive biases, especially focusing on the role of dopamine.

Spontaneous behavior in animals and humans shows a striking amount of variability both in the spatial domain (which actions to choose) and temporal domain (when to act). Concatenating actions into sequences and behavioral plans reveals the existence of a hierarchy of timescales ranging from hundreds of milliseconds to minutes. How do multiple timescales emerge from neural circuit dynamics? How do circuits modulate temporal responses to flexibly adapt to changing demands? In this talk, we will present recent results from experiments and theory suggesting a new computational mechanism generating the temporal variability underlying naturalistic behavior. We will show how neural activity from premotor areas unfolds through temporal sequences of attractors, which predict the intention to act. These sequences naturally emerge from recurrent cortical networks, where correlated neural variability plays a crucial role in explaining the observed variability in action timing. We will then discuss how reaction times in these recurrent circuits can be accelerated or slowed down via gain modulation, induced by neuromodulation or perturbations. Finally, we will present a general mechanism producing a reservoir of multiple timescales in recurrent networks.

Las enfermedades neurodegenerativas como la Enfermedad de Alzheimer, Enfermedad de Parkinson y la Esclerosis Lateral Amiotrófica tienen una prevalencia creciente en nuestra sociedad, de acuerdo con el aumento de la expectativa de vida. Durante el envejecimiento, las células gliales sufren cambios funcionales favoreciendo la “neuroinflamación”, que tiene un reconocido papel patogénico en la progresión de la enfermedad neurodegenerativa. Estudios recientes demuestran que durante el envejecimiento del sistema nervioso se acumulan notablemente células senescentes, tanto de estirpe neuronal como glial. Las células senescentes no proliferan, muchas de ellas exhiben un fenotipo secretor (SASP) con capacidad de inducir inflamación. La eliminación de células senescentes por ablación genética inducida farmacológicamente o por bloqueos de fármacos senolíticos mejoran la neuroinflamación y disminuyen la neurotoxicidad. En la presentación, se realizará una revisión de la bibliografía sobre este tema y se realizará un análisis del potencial terapéutico de fármacos senolíticos como una aproximación terapéutica novedosa de las enfermedades neurodegenerativas.

Many networks in the nervous system possess an abundance of inhibition, which serves to shape and stabilize neural dynamics. The neurons in such networks exhibit intricate patterns of connectivity whose structure controls the allowed patterns of neural activity. In this work, we examine inhibitory threshold-linear networks whose dynamics are constrained by an underlying directed graph. We develop a set of parameter-independent graph rules that enable us to predict features of the dynamics, such as emergent sequences and dynamic attractors, from properties of the graph. These rules provide a direct link between the structure and function of these networks, and may provide new insights into how connectivity shapes dynamics in real neural circuits.

In this talk series, addiction neuroscientists from across the world share their personal stories/experiences on the beauty of addiction neuroscience and how/why they have decided to invest their scientific life in this field. We hope that this talk series would encourage and support a new generation of young and passionate addiction neuroscientists in different countries to revolutionize the field of addiction medicine.

Rajanlab designs neural network models constrained by experimental data, and reverse engineers them to figure out how brain circuits function in health and disease. Recently, we have been developing a powerful new theory-based framework for “in-vivo tract tracing” from multi-regional neural activity collected experimentally. We call this framework CURrent-Based Decomposition (CURBD). CURBD employs recurrent neural networks (RNNs) directly constrained, from the outset, by time series measurements acquired experimentally, such as Ca2+ imaging or electrophysiological data. Once trained, these data-constrained RNNs let us infer matrices quantifying the interactions between all pairs of modeled units. Such model-derived “directed interaction matrices” can then be used to separately compute excitatory and inhibitory input currents that drive a given neuron from all other neurons. Therefore different current sources can be de-mixed – either within the same region or from other regions, potentially brain-wide – which collectively give rise to the population dynamics observed experimentally. Source de-mixed currents obtained through CURBD allow an unprecedented view into multi-region mechanisms inaccessible from measurements alone. We have applied this method successfully to several types of neural data from our experimental collaborators, e.g., zebrafish (Deisseroth lab, Stanford), mice (Harvey lab, Harvard), monkeys (Rudebeck lab, Sinai), and humans (Rutishauser lab, Cedars Sinai), where we have discovered both directed interactions brain wide and inter-area currents during different types of behaviors. With this framework based on data-constrained multi-region RNNs and CURrent Based Decomposition (CURBD), we can ask if there are conserved multi-region mechanisms across different species, as well as identify key divergences.

The neuronal gene Arc is essential for long-lasting information storage in the mammalian brain and mediates various forms of synaptic plasticity. We recently discovered that Arc self-assembles into virus-like capsids that encapsulate RNA. Endogenous Arc protein is released from neurons in extracellular vesicles that mediate the transfer of Arc mRNA into new target cells. Evolutionary analysis indicates that Arc is derived from a vertebrate lineage of Ty3/gypsy retrotransposons, which are also ancestral to retroviruses such as HIV. These findings suggest that Gag retroelements have been repurposed during evolution to mediate intercellular communication in the nervous system that may underlie cognition and memory.

Cortical circuits receive multiple inputs from upstream populations with non-overlapping stimulus tuning preferences. Both the feedforward and recurrent architectures of the receiving cortical layer will reflect this diverse input tuning. We study how population-wide neuronal variability propagates through a hierarchical cortical network receiving multiple, independent, tuned inputs. We present new analysis of in vivo neural data from the primate visual system showing that the number of latent variables (dimension) needed to describe population shared variability is smaller in V4 populations compared to those of its downstream visual area PFC. We successfully reproduce this dimensionality expansion from our V4 to PFC neural data using a multi-layer spiking network with structured, feedforward projections and recurrent assemblies of multiple, tuned neuron populations. We show that tuning-structured connectivity generates attractor dynamics within the recurrent PFC current, where attractor competition is reflected in the high dimensional shared variability across the population. Indeed, restricting the dimensionality analysis to activity from one attractor state recovers the low-dimensional structure inherited from each of our tuned inputs. Our model thus introduces a framework where high-dimensional cortical variability is understood as ``time-sharing’’ between distinct low-dimensional, tuning-specific circuit dynamics.

The ability to encode the direction of image motion is fundamental to our sense of vision. Direction selectivity along the four cardinal directions is thought to originate in direction-selective ganglion cells (DSGCs), due to directionally-tuned GABAergic suppression by starburst cells. Here, by utilizing two-photon glutamate imaging to measure synaptic release, we reveal that direction selectivity along all four directions arises earlier than expected, at bipolar cell outputs. Thus, DSGCs receive directionally-aligned glutamatergic inputs from bipolar cell boutons. We further show that this bouton-specific tuning relies on cholinergic excitation and GABAergic inhibition from starburst cells. In this way, starburst cells are able to refine directional tuning in the excitatory visual pathway by modulating the activity of DSGC dendrites and their axonal inputs using two different neurotransmitters.

Electric Gymnotiform fish live in muddy, shallow waters near the shore – hiding in the dense filamentous roots of floating plants such as Eichornia crassipes (“camalote”). They explore their surroundings by using a series of electric pulses that serve as self emitted carrier of electrosensory signals. This propagates at the speed of light through this spongiform habitat and is barely sensed by the lateral line of predators and prey. The emitted field polarizes the surroundings according to the difference in impedance with water which in turn modifies the profile of transcutaneous currents considered as an electrosensory image. Using this system, pulse Gymnotiformes create an electrosensory bubble where an object’s location, impedance, size and other characteristics are discriminated and probably recognized. Although consciousness is still not well-proven, cognitive functions as volition, attention, and path integration have been shown. Here I will summarize different aspects of the electromotor electrosensory loop of pulse Gymnotiforms. First, I will address how objects are polarized with a stereotyped but temporospatially complex electric field, consisting of brief pulses emitted at regular intervals. This relies on complex electric organs quasi periodically activated through an electromotor coordination system by a pacemaker in the medulla. Second, I will deal with the imaging mechanisms of pulse gymnotiform fish and the presence of two regions in the electrosensory field, a rostral region where the field time course is coherent and field vector direction is constant all along the electric organ discharge and a lateral region where the field time course is site specific and field vector direction describes a stereotyped 3D trajectory. Third, I will describe the electrosensory mosaic and their characteristics. Receptor and primary afferents correspond one to one showing subtypes optimally responding to the time course of the self generated pulse with a characteristic train of spikes. While polarized objects at the rostral region project their electric images on the perioral region where electrosensory receptor density, subtypes and central projection are maximal, the image of objects on the side recruit a single type of scattered receptors. Therefore, the rostral mosaic has been likened to an electrosensory fovea and its receptive field referred to as foveal field. The rest of the mosaic and field are referred to as peripheral. Finally, I will describe ongoing work on early processing structures. I will try to generate an integrated view, including anatomical and functional data obtained in vitro, acute experiments, and unitary recordings in freely moving fish. We have recently shown have shown that these fish tract allo-generated fields and the virtual fields generated by nearby objects in the presence of self-generated fields to explore the nearby environment. These data together with the presence of a multimodal receptor mosaic at the cutaneous surface particularly surrounding the mouth and an important role of proprioception in early sensory processing suggests the hypothesis that the active electrosensory system is part of a multimodal haptic sense.

Neurons in the primary visual cortex (V1) encode the orientation and contrast of visual stimuli through changes in firing rate (Hubel and Wiesel, 1962). Their activity typically peaks at a preferred orientation and decays to zero at the orientations that are orthogonal to the preferred. This activity pattern is re-scaled by contrast but its shape is preserved, a phenomenon known as contrast invariance. Contrast-invariant selectivity is also observed at the population level in V1 (Carandini and Sengpiel, 2004). The mechanisms supporting the emergence of contrast-invariance at the population level remain unclear. How does the activity of different neurons with diverse orientation selectivity and non-linear contrast sensitivity combine to give rise to contrast-invariant population selectivity? Theoretical studies have shown that in the balance limit, the properties of single-neurons do not determine the population activity (van Vreeswijk and Sompolinsky, 1996). Instead, the synaptic dynamics (Mongillo et al., 2012) as well as the intracortical connectivity (Rosenbaum and Doiron, 2014) shape the population activity in balanced networks. We report that short-term plasticity can change the synaptic strength between neurons as a function of the presynaptic activity, which in turns modifies the population response to a stimulus. Thus, the same circuit can process a stimulus in different ways –linearly, sublinearly, supralinearly – depending on the properties of the synapses. We found that balanced networks with excitatory to excitatory short-term synaptic plasticity cannot be contrast-invariant. Instead, short-term plasticity modifies the network selectivity such that the tuning curves are narrower (broader) for increasing contrast if synapses are facilitating (depressing). Based on these results, we wondered whether balanced networks with plastic synapses (other than short-term) can support the emergence of contrast-invariant selectivity. Mathematically, we found that the only synaptic transformation that supports perfect contrast invariance in balanced networks is a power-law release of neurotransmitter as a function of the presynaptic firing rate (in the excitatory to excitatory and in the excitatory to inhibitory neurons). We validate this finding using spiking network simulations, where we report contrast-invariant tuning curves when synapses release the neurotransmitter following a power- law function of the presynaptic firing rate. In summary, we show that synaptic plasticity controls the type of non-linear network response to stimulus contrast and that it can be a potential mechanism mediating the emergence of contrast invariance in balanced networks with orientation-dependent connectivity. Our results therefore connect the physiology of individual synapses to the network level and may help understand the establishment of contrast-invariant selectivity.

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.

Every day when we fall asleep we lose consciousness, we are not there. And then, every morning, when we wake up, we regain it. What mechanisms give rise to consciousness, and how can we explain consciousness in the realm of the physical world of atoms and matter? For centuries, philosophers and scientists have aimed to crack this mystery. Much progress has been made in the past decades to understand how consciousness is instantiated in the brain, yet critical questions remain: can we develop a consciousness meter? Are computers conscious? What about other animals and babies? We have embarked in a large-scale, multicenter project to test, in the context of an open science, adversarial collaboration, two of the most prominent theories: Integrated information theory (IIT) and Global Neuronal Workspace (GNW) theory. We are collecting over 500 datasets including invasive and non-invasive recordings of the human brain, i.e.. fMRI, MEG and ECoG. We hope this project will enable theory-driven discoveries and further explorations that will help us better understand how consciousness fits inside the human brain.

Spontaneous and sensory-evoked cortical activity is highly state-dependent, promoting the functional flexibility of cortical circuits underlying perception and cognition. Using neural recordings in combination with behavioral state monitoring, we find that arousal and motor activity have complementary roles in regulating local cortical operations, providing dynamic control of sensory encoding. These changes in encoding are linked to altered performance on perceptual tasks. Neuromodulators, such as acetylcholine, may regulate this state-dependent flexibility of cortical network function. We therefore recently developed an approach for dual mesoscopic imaging of acetylcholine release and neural activity across the entire cortical mantle in behaving mice. We find spatiotemporally heterogeneous patterns of cholinergic signaling across the cortex. Transitions between distinct behavioral states reorganize the structure of large-scale cortico-cortical networks and differentially regulate the relationship between cholinergic signals and neural activity. Together, our findings suggest dynamic state-dependent regulation of cortical network operations at the levels of both local and large-scale circuits.