Electrophysiology seminars

What are the things that draw us to a particular field of science and what is it that keeps us there? For Dr. Bahia, there was a particular attraction to sensory nerves; the monitors of the worlds inside and outside of our bodies. In this talk, Dr. Bahia will outline her career path as a neuroscientist resulting in the title of Research Associate. She will also talk about the longest project she has participated in, 'exploring the role of ion channels in sensory nerves' (rupress.org/jgp/article/147/6/451/43495/The-exceptionally-high-reactivity-of-Cys-621-is)

Despite the recent success of deep learning in artificial intelligence, the lack of biological plausibility and labeled data in natural learning still poses a challenge in understanding biological learning. At the other extreme lies Hebbian learning, the simplest local and unsupervised one, yet considered to be computationally less efficient. In this talk, I would introduce a novel method to infer the form of Hebbian learning from in vivo data. Applying the method to the data obtained from the monkey inferior temporal cortex for the recognition task indicates how Hebbian learning changes the dynamic properties of the circuits and may promote brain oscillation. Notably, recent electrophysiological data observed in rodent V1 showed that the effect of visual experience on direction selectivity was similar to that observed in monkey data and provided strong validation of asymmetric changes of feedforward and recurrent synaptic strengths inferred from monkey data. This may suggest a general learning principle underlying the same computation, such as familiarity detection across different features represented in different brain regions.

This talk will focus on different aspects of the Local Field Potential (LFP) signal. Classically, LFP fluctuations are related to changes in the functional state of the cortex. Yet, the mechanisms linking LFP changes with the state of the cortex are not well understood. The presentation will start with a brief explanation of the main oscillatory components of the LFP signal, how these different oscillatory components are generated at cortical microcircuits, and how their dynamics can be studied across multiple areas. Thereafter, a case study of a patient with akinetic mutism will be presented, linking cortical states with the behavior of the patient, as well as some preliminary results about how the LF cortical microcircuit dynamic changes modulate different cortical states and how these changes are reflected in the LFP signal

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.

Having conscious experience is arguably the most important reason why it matters to us whether we are alive or dead. A powerful paradigm to identify neural correlates of consciousness is binocular rivalry, wherein a constant visual stimulus evokes a varying conscious percept. It has recently been suggested that activity modulations observed during rivalry may represent the act of report rather than the conscious percept itself. Here, we performed single-unit recordings from face patches in macaque inferotemporal (IT) cortex using a novel no-report paradigm in which the animal’s conscious percept was inferred from eye movements. These experiments reveal two new results concerning the neural correlates of consciousness. First, we found that high proportions of IT neurons represented the conscious percept even without active report. Using high-channel recordings, including a new 128-channel Neuropixels-like probe, we were able to decode the conscious percept on single trials. Second, we found that even on single trials, modulation to rivalrous stimuli was weaker than that to unambiguous stimuli, suggesting that cells may encode not only the conscious percept but also the suppressed stimulus. To test this hypothesis, we varied the identity of the suppressed stimulus during binocular rivalry; we found that indeed, we could decode not only the conscious percept but also the suppressed stimulus from neural activity. Moreover, the same cells that were strongly modulated by the conscious percept also tended to be strongly modulated by the suppressed stimulus. Together, our findings indicate that (1) IT cortex possesses a true neural correlate of consciousness even in the absence of report, and (2) this correlate consists of a population code wherein single cells multiplex representation of the conscious percept and veridical physical stimulus, rather than a subset of cells perfectly reflecting consciousness.

One of the most important scientific and technological frontiers of our time is the interfacing of electronics with the human brain. This endeavour promises to help understand how the brain works and deliver new tools for diagnosis and treatment of pathologies including epilepsy and Parkinson’s disease. Current solutions, however, are limited by the materials that are brought in contact with the tissue and transduce signals across the biotic/abiotic interface. Recent advances in electronics have made available materials with a unique combination of attractive properties, including mechanical flexibility, mixed ionic/electronic conduction, enhanced biocompatibility, and capability for drug delivery. Professor Malliaras will present examples of novel devices for recording and stimulation of neurons and show that organic electronic materials offer tremendous opportunities to study the brain and treat its pathologies.

Medium-spiny neurons (MSNs) in the striatum are required for interval timing, or the estimation of the time over several seconds via a motor response. We and others have shown that striatal MSNs can encode the duration of temporal intervals via time-dependent ramping activity, progressive monotonic changes in firing rate preceding behaviorally salient points in time. Here, we investigated how timing-related activity within striatal ensembles changes with experience. We leveraged a rodent-optimized interval timing task in which mice ‘switch’ response ports after an amount of time has passed without reward. We report three main results. First, we found that the proportion of MSNs exhibiting time-dependent modulations of firing rate increased after 10 days of task overtraining. Second, temporal decoding by MSN ensembles increased with experience and was largely driven by time-related ramping activity. Finally, we found that time-related ramping activity generalized across both correct and error trials. These results enhance our understanding of striatal temporal processing by demonstrating that time-dependent activity within MSN ensembles evolves with experience and is dissociable from motor- and reward-related processes.

The eyes send a continuous stream of about two million nerve fibers to the brain, but only a fraction of this information is stored as visual memories. This talk will detail three neurocomputational models that attempt an understanding how the visual system makes on-the-fly decisions about how to encode that information. First, the STST family of models (Bowman & Wyble 2007; Wyble, Potter, Bowman & Nieuwenstein 2011) proposes mechanisms for temporal segmentation of continuous input. The conclusion of this work is that the visual system has mechanisms for rapidly creating brief episodes of attention that highlight important moments in time, and also separates each episode from temporally adjacent neighbors to benefit learning. Next, the RAGNAROC model (Wyble et al. 2019) describes a decision process for determining the spatial focus (or foci) of attention in a spatiotopic field and the neural mechanisms that provide enhancement of targets and suppression of highly distracting information. This work highlights the importance of integrating behavioral and electrophysiological data to provide empirical constraints on a neurally plausible model of spatial attention. The model also highlights how a neural circuit can make decisions in a continuous space, rather than among discrete alternatives. Finally, the binding pool (Swan & Wyble 2014; Hedayati, O’Donnell, Wyble in Prep) provides a mechanism for selectively encoding specific attributes (i.e. color, shape, category) of a visual object to be stored in a consolidated memory representation. The binding pool is akin to a holographic memory system that layers representations of select latent representations corresponding to different attributes of a given object. Moreover, it can bind features into distinct objects by linking them to token placeholders. Future work looks toward combining these models into a coherent framework for understanding the full measure of on-the-fly attentional mechanisms and how they improve learning.

Neural codes, such as temporal codes (precisely timed spikes) and rate codes (instantaneous spike firing rates), are believed to be used in encoding sensory information into spike trains of cortical neurons. Temporal and rate codes co-exist in the spike train and such multiplexed neural code-carrying spike trains have been shown to be spatially synchronized in multiple neurons across different cortical layers during sensory information processing. Inhibition is suggested to promote such synchronization, but it is unclear whether distinct subtypes of interneurons make different contributions in the synchronization of multiplexed neural codes. To test this, in vivo single-unit recordings from barrel cortex were combined with optogenetic manipulations to determine the contributions of parvalbumin (PV)- and somatostatin (SST)-positive interneurons to synchronization of precisely timed spike sequences. We found that PV interneurons preferentially promote the synchronization of spike times when instantaneous firing rates are low (<12 Hz), whereas SST interneurons preferentially promote the synchronization of spike times when instantaneous firing rates are high (>12 Hz). Furthermore, using a computational model, we demonstrate that these effects can be explained by PV and SST interneurons having preferential contribution to feedforward and feedback inhibition, respectively. Overall, these results show that PV and SST interneurons have distinct frequency (rate code)-selective roles in dynamically gating the synchronization of spike times (temporal code) through preferentially recruiting feedforward and feedback inhibitory circuit motifs. The inhibitory neural circuit mechanisms we uncovered here his may have critical roles in regulating neural code-based somatosensory information processing in the neocortex.

General anesthesia is a drug-induced, reversible condition comprised of five behavioral states: unconsciousness, amnesia (loss of memory), antinociception (loss of pain sensation), akinesia (immobility), and hemodynamic stability with control of the stress response. Our work shows that a primary mechanism through which anesthetics create these altered states of arousal is by initiating and maintaining highly structured oscillations. These oscillations impair communication among brain regions. We illustrate this effect by presenting findings from our human studies of general anesthesia using high-density EEG recordings and intracranial recordings. These studies have allowed us to give a detailed characterization of the neurophysiology of loss and recovery of consciousness due to propofol. We show how these dynamics change systematically with different anesthetic classes and with age. As a consequence, we have developed a principled, neuroscience-based paradigm for using the EEG to monitor the brain states of patients receiving general anesthesia. We demonstrate that the state of general anesthesia can be rapidly reversed by activating specific brain circuits. Finally, we demonstrate that the state of general anesthesia can be controlled using closed loop feedback control systems. The success of our research has depended critically on tight coupling of experiments, signal processing research and mathematical modeling.

A wealth of experimental studies show that the trial-to-trial variability of neuronal activity is quenched during stimulus evoked responses. This fact has helped ground a popular view that the variability of spiking activity can be decomposed into two components. The first is due to irregular spike timing conditioned on the firing rate of a neuron (i.e. a Poisson process), and the second is the trial-to-trial variability of the firing rate itself. Quenching of the variability of the overall response is assumed to be a reflection of a suppression of firing rate variability. Network models have explained this phenomenon through a variety of circuit mechanisms. However, in all cases, from the vantage of a neuron embedded within the network, quenching of its response variability is inherited from its synaptic input. We analyze in vivo whole cell recordings from principal cells in layer (L) 2/3 of mouse visual cortex. While the variability of the membrane potential is quenched upon stimulation, the variability of excitatory and inhibitory currents afferent to the neuron are amplified. This discord complicates the simple inheritance assumption that underpins network models of neuronal variability. We propose and validate an alternative (yet not mutually exclusive) mechanism for the quenching of neuronal variability. We show how an increase in synaptic conductance in the evoked state shunts the transfer of current to the membrane potential, formally decoupling changes in their trial-to-trial variability. The ubiquity of conductance based neuronal transfer combined with the simplicity of our model, provides an appealing framework. In particular, it shows how the dependence of cellular properties upon neuronal state is a critical, yet often ignored, factor. Further, our mechanism does not require a decomposition of variability into spiking and firing rate components, thereby challenging a long held view of neuronal activity.

Although substance use disorders (SUDs) occur commonly in patients with schizophrenia and significantly worsen their clinical course, the neurobiological basis of SUDs in schizophrenia is not well understood. Therefore, there is a critical need to understand the mechanisms underlying SUDs in schizophrenia in order to identify potential targets for therapeutic intervention. Since drug use usually begins in adolescence, it is also important to understand the long-term effects of adolescent drug exposure on schizophrenia- and reward- related behaviors and circuitry. This talk will combine pharmacological, behavioral, electrophysiologic (local field potential recordings) and pre-clinical magnetic resonance imaging (resting-state functional connectivity and magnetic resonance spectroscopy) approaches to study these topics with an eye toward developing better treatment approaches.

Spike trains recorded from the cortex of behaving animals can be complex, highly variable from trial to trial, and therefore challenging to interpret. A fraction of cells exhibit trial-averaged responses with obvious task-related features such as pure tone frequency tuning in auditory cortex. However, a substantial number of cells (including cells in primary sensory cortex) do not appear to fire in a task-related manner and are often neglected from analysis. We recently used a novel single-trial, spike-timing-based analysis to show that both classically responsive and non-classically responsive cortical neurons contain significant information about sensory stimuli and behavioral decisions suggesting that non-classically responsive cells may play an underappreciated role in perception and behavior. We now expand this investigation to explore the synaptic origins and potential contribution of these cells to network function. To do so, we trained a novel spiking recurrent neural network model that incorporates spike-timing-dependent plasticity (STDP) mechanisms to perform the same task as behaving animals. By leveraging excitatory and inhibitory plasticity rules this model reproduces neurons with response profiles that are consistent with previously published experimental data, including classically responsive and non-classically responsive neurons. We found that both classically responsive and non-classically responsive neurons encode behavioral variables in their spike times as seen in vivo. Interestingly, plasticity in excitatory-to-excitatory synapses increased the proportion of non-classically responsive neurons and may play a significant role in determining response profiles. Finally, our model also makes predictions about the synaptic origins of classically and non-classically responsive neurons which we can compare to in vivo whole-cell recordings taken from the auditory cortex of behaving animals. This approach successfully recapitulates heterogeneous response profiles measured from behaving animals and provides a powerful lens for exploring large-scale neuronal dynamics and the plasticity rules that shape them.

Individual neurons in visual cortex provide the brain with unreliable estimates of visual features. It is not known if the single-neuron variability is correlated across large neural populations, thus impairing the global encoding of stimuli. We recorded simultaneously from up to 50,000 neurons in mouse primary visual cortex (V1) and in higher-order visual areas and measured stimulus discrimination thresholds of 0.35 degrees and 0.37 degrees respectively in an orientation decoding task. These neural thresholds were almost 100 times smaller than the behavioral discrimination thresholds reported in mice. This discrepancy could not be explained by stimulus properties or arousal states. Furthermore, the behavioral variability during a sensory discrimination task could not be explained by neural variability in primary visual cortex. Instead behavior-related neural activity arose dynamically across a network of non-sensory brain areas. These results imply that sensory perception in mice is limited by downstream decoders, not by neural noise in sensory representations.

In this talk I will present recent developments in data sharing, organization, and analyses that allow to build fully reproducible workflows. First, I will present the Brain Imaging Data structure and discuss how this allows to build workflows, showing some new tools to read/import/create studies from EEG data structured that way. Second, I will present several newly developed tools for reproducible pre-processing and statistical analyses. Although it does take some extra effort, I will argue that it largely feasible to make most EEG data analysis fully reproducible.

To communicate effectively, two individuals must take turns to prevent overlap in their signals. How does the nervous system coordinate vocalizations between two individuals? Female and male plain-tailed wrens sing a duet in which they alternate syllable production so rapidly and precisely it sounds as if a single bird is singing. I will talk about experiments that examine the interaction between sensory cues and motor activity, using behavioral manipulations and neurophysiological recordings from pairs of awake, duetting wrens. I will show evidence that auditory cues link the brains of the wrens by modulating motor circuits.

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.

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.