Turtle Dorsal Cortex Pyramidal Neurons Comprise Two Distinct Cell Types with Indistinguishable Visual Responses

Transcriptomic cell type structures in vivo neuronal activity across multiple timescales

Cell type is hypothesized to be a key determinant of a neuron's role within a circuit. Here, we examine whether a neuron's transcriptomic type influences the timing of its activity. We develop a deep-learning architecture that learns features of interevent intervals across timescales (ms to >30 min). We show that transcriptomic cell-class information is embedded in the timing of single neuron activity in the intact brain of behaving animals (calcium imaging and extracellular electrophysiology) as well as in a bio-realistic model of the visual cortex. Further, a subset of excitatory cell types are distinguishable but can be classified with higher accuracy when considering cortical layer and projection class. Finally, we show that computational fingerprints of cell types may be universalizable across structured stimuli and naturalistic movies. Our results indicate that transcriptomic class and type may be imprinted in the timing of single neuron activity across diverse stimuli.

Glutamatergic pathways in the brains of turtles: A comparative perspective among reptiles, birds, and mammals

Glutamate acts as the main excitatory neurotransmitter in the brain and plays a vital role in physiological and pathological neuronal functions. In mammals, glutamate can cause detrimental excitotoxic effects under anoxic conditions. In contrast, Trachemys scripta, a freshwater turtle, is one of the most anoxia-tolerant animals, being able to survive up to months without oxygen. Therefore, turtles have been investigated to assess the molecular mechanisms of neuroprotective strategies used by them in anoxic conditions, such as maintaining low levels of glutamate, increasing adenosine and GABA, upregulating heat shock proteins, and downregulating KATP channels. These mechanisms of anoxia tolerance of the turtle brain may be applied to finding therapeutics for human glutamatergic neurological disorders such as brain injury or cerebral stroke due to ischemia. Despite the importance of glutamate as a neurotransmitter and of the turtle as an ideal research model, the glutamatergic circuits in the turtle brain remain less described whereas they have been well studied in mammalian and avian brains. In reptiles, particularly in the turtle brain, glutamatergic neurons have been identified by examining the expression of vesicular glutamate transporters (VGLUTs). In certain areas of the brain, some ionotropic glutamate receptors (GluRs) have been immunohistochemically studied, implying that there are glutamatergic target areas. Based on the expression patterns of these glutamate-related molecules and fiber connection data of the turtle brain that is available in the literature, many candidate glutamatergic circuits could be clarified, such as the olfactory circuit, hippocampal–septal pathway, corticostriatal pathway, visual pathway, auditory pathway, and granule cell–Purkinje cell pathway. This review summarizes the probable glutamatergic pathways and the distribution of glutamatergic neurons in the pallium of the turtle brain and compares them with those of avian and mammalian brains. The integrated knowledge of glutamatergic pathways serves as the fundamental basis for further functional studies in the turtle brain, which would provide insights on physiological and pathological mechanisms of glutamate regulation as well as neural circuits in different species.

From Cell Types to an Integrated Understanding of Brain Evolution: The Case of the Cerebral Cortex

With the discovery of the incredible diversity of neurons, Cajal and coworkers laid the foundation of modern neuroscience. Neuron types are not only structural units of nervous systems but also evolutionary units, because their identities are encoded in the genome. With the advent of high-throughput cellular transcriptomics, neuronal identities can be characterized and compared systematically across species. The comparison of neurons in mammals, reptiles, and birds indicates that the mammalian cerebral cortex is a mosaic of deeply conserved and recently evolved neuron types. Using the cerebral cortex as a case study, this review illustrates how comparing neuron types across species is key to reconciling observations on neural development, neuroanatomy, circuit wiring, and physiology for an integrated understanding of brain evolution.

Comparative Analysis of Brain Stiffness Among Amniotes Using Glyoxal Fixation and Atomic Force Microscopy

Brain structures are diverse among species despite the essential molecular machinery of neurogenesis being common. Recent studies have indicated that differences in the mechanical properties of tissue may result in the dynamic deformation of brain structure, such as folding. However, little is known about the correlation between mechanical properties and species-specific brain structures. To address this point, a comparative analysis of mechanical properties using several animals is required. For a systematic measurement of the brain stiffness of remotely maintained animals, we developed a novel strategy of tissue-stiffness measurement using glyoxal as a fixative combined with atomic force microscopy. A comparison of embryonic and juvenile mouse and songbird brain tissue revealed that glyoxal fixation can maintain brain structure as well as paraformaldehyde (PFA) fixation. Notably, brain tissue fixed by glyoxal remained much softer than PFA-fixed brains, and it can maintain the relative stiffness profiles of various brain regions. Based on this method, we found that the homologous brain regions between mice and songbirds exhibited different stiffness patterns. We also measured brain stiffness in other amniotes (chick, turtle, and ferret) following glyoxal fixation. We found stage-dependent and species-specific stiffness in pallia among amniotes. The embryonic chick and matured turtle pallia showed gradually increasing stiffness along the apico-basal tissue axis, the lowest region at the most apical region, while the ferret pallium exhibited a catenary pattern, that is, higher in the ventricular zone, the inner subventricular zone, and the cortical plate and the lowest in the outer subventricular zone. These results indicate that species-specific microenvironments with distinct mechanical properties emerging during development might contribute to the formation of brain structures with unique morphology.

Alternative classifications of neurons based on physiological properties and synaptic responses, a computational study

One of the central goals of today's neuroscience is to achieve the conceivably most accurate classification of neuron types in the mammalian brain. As part of this research effort, electrophysiologists commonly utilize current clamp techniques to gain a detailed characterization of the neurons' physiological properties. While this approach has been useful, it is not well understood whether neurons that share physiological properties of a particular phenotype would also operate consistently under the action of natural synaptic inputs. We approached this problem by simulating a biophysically diverse population of model neurons based on 3 generic phenotypes. We exposed the model neurons to two types of stimulation to investigate their voltage responses under conventional current step protocols and under simulated synaptic bombardment. We extracted standard physiological parameters from the voltage responses elicited by current step stimulation and spike arrival times descriptive of the model's firing behavior under synaptic inputs. The biophysical phenotypes could be reliably identified using classification based on the 'static' physiological properties, but not the interspike interval-based parameters. However, the model neurons associated with the biophysically different phenotypes retained cell type specific features in the fine structure of their spike responses that allowed their accurate classification.

Single-Cell Membrane Potential Fluctuations Evince Network Scale-Freeness and Quasicriticality

What information single neurons receive about general neural circuit activity is a fundamental question for neuroscience. Somatic membrane potential (V _m) fluctuations are driven by the convergence of synaptic inputs from a diverse cross-section of upstream neurons. Furthermore, neural activity is often scale-free, implying that some measurements should be the same, whether taken at large or small scales. Together, convergence and scale-freeness support the hypothesis that single V _m recordings carry useful information about high-dimensional cortical activity. Conveniently, the theory of "critical branching networks" (one purported explanation for scale-freeness) provides testable predictions about scale-free measurements that are readily applied to V _m fluctuations. To investigate, we obtained whole-cell current-clamp recordings of pyramidal neurons in visual cortex of turtles with unknown genders. We isolated fluctuations in V _m below the firing threshold and analyzed them by adapting the definition of "neuronal avalanches" (i.e., spurts of population spiking). The V _m fluctuations which we analyzed were scale-free and consistent with critical branching. These findings recapitulated results from large-scale cortical population data obtained separately in complementary experiments using microelectrode arrays described previously (Shew et al., 2015). Simultaneously recorded single-unit local field potential did not provide a good match, demonstrating the specific utility of V _m Modeling shows that estimation of dynamical network properties from neuronal inputs is most accurate when networks are structured as critical branching networks. In conclusion, these findings extend evidence of critical phenomena while also establishing subthreshold pyramidal neuron V _m fluctuations as an informative gauge of high-dimensional cortical population activity.SIGNIFICANCE STATEMENT The relationship between membrane potential (V _m) dynamics of single neurons and population dynamics is indispensable to understanding cortical circuits. Just as important to the biophysics of computation are emergent properties such as scale-freeness, where critical branching networks offer insight. This report makes progress on both fronts by comparing statistics from single-neuron whole-cell recordings with population statistics obtained with microelectrode arrays. Not only are fluctuations of somatic V _m scale-free, they match fluctuations of population activity. Thus, our results demonstrate appropriation of the brain's own subsampling method (convergence of synaptic inputs) while extending the range of fundamental evidence for critical phenomena in neural systems from the previously observed mesoscale (fMRI, LFP, population spiking) to the microscale, namely, V _m fluctuations.

Coupling of synaptic inputs to local cortical activity differs among neurons and adapts after stimulus onset

Cortical activity contributes significantly to the high variability of sensory responses of interconnected pyramidal neurons, which has crucial implications for sensory coding. Yet, largely because of technical limitations of in vivo intracellular recordings, the coupling of a pyramidal neuron's synaptic inputs to the local cortical activity has evaded full understanding. Here we obtained excitatory synaptic conductance ( g) measurements from putative pyramidal neurons and local field potential (LFP) recordings from adjacent cortical circuits during visual processing in the turtle whole brain ex vivo preparation. We found a range of g-LFP coupling across neurons. Importantly, for a given neuron, g-LFP coupling increased at stimulus onset and then relaxed toward intermediate values during continued visual stimulation. A model network with clustered connectivity and synaptic depression reproduced both the diversity and the dynamics of g-LFP coupling. In conclusion, these results establish a rich dependence of single-neuron responses on anatomical, synaptic, and emergent network properties. NEW & NOTEWORTHY Cortical neurons are strongly influenced by the networks in which they are embedded. To understand sensory processing, we must identify the nature of this influence and its underlying mechanisms. Here we investigate synaptic inputs to cortical neurons, and the nearby local field potential, during visual processing. We find a range of neuron-to-network coupling across cortical neurons. This coupling is dynamically modulated during visual processing via biophysical and emergent network properties.

The turtle visual system mediates a complex spatiotemporal transformation of visual stimuli into cortical activity

The three-layered visual cortex of turtle is characterized by extensive intracortical axonal projections and receives non-retinotopic axonal projections from lateral geniculate nucleus. What spatiotemporal transformation of visual stimuli into cortical activity arises from such tangle of malleable cortical inputs and intracortical connections? To address this question, we obtained band-pass filtered extracellular recordings of neural activity in turtle dorsal cortex during visual stimulation of the retina. We discovered important spatial and temporal features of stimulus-modulated cortical local field potential (LFP) recordings. Spatial receptive fields span large areas of the visual field, have an intricate internal structure, and lack directional tuning. The receptive field structure varies across recording sites in a distant-dependent manner. Such composite spatial organization of stimulus-modulated cortical activity is accompanied by an equally multifaceted temporal organization. Cortical visual responses are delayed, persistent, and oscillatory. Further, prior cortical activity contributes globally to adaptation in turtle visual cortex. In conclusion, these results demonstrate convoluted spatiotemporal transformations of visual stimuli into stimulus-modulated cortical activity that, at present, largely evade computational frameworks.

Induced cortical oscillations in turtle cortex are coherent at the mesoscale of population activity, but not at the microscale of the membrane potential of neurons

Bursts of oscillatory neural activity have been hypothesized to be a core mechanism by which remote brain regions can communicate. Such a hypothesis raises the question to what extent oscillations are coherent across spatially distant neural populations. To address this question, we obtained local field potential (LFP) and membrane potential recordings from the visual cortex of turtle in response to visual stimulation of the retina. The time-frequency analysis of these recordings revealed pronounced bursts of oscillatory neural activity and a large trial-to-trial variability in the spectral and temporal properties of the observed oscillations. First, local bursts of oscillations varied from trial to trial in both burst duration and peak frequency. Second, oscillations of a given recording site were not autocoherent; i.e., the phase did not progress linearly in time. Third, LFP oscillations at spatially separate locations within the visual cortex were more phase coherent in the presence of visual stimulation than during ongoing activity. In contrast, the membrane potential oscillations from pairs of simultaneously recorded pyramidal neurons showed smaller phase coherence, which did not change when switching from black screen to visual stimulation. In conclusion, neuronal oscillations at distant locations in visual cortex are coherent at the mesoscale of population activity, but coherence is largely absent at the microscale of the membrane potential of neurons.NEW & NOTEWORTHY Coherent oscillatory neural activity has long been hypothesized as a potential mechanism for communication across locations in the brain. In this study we confirm the existence of coherent oscillations at the mesoscale of integrated cortical population activity. However, at the microscopic level of neurons, we find no evidence for coherence among oscillatory membrane potential fluctuations. These results raise questions about the applicability of the communication through coherence hypothesis to the level of the membrane potential.

Network activity influences the subthreshold and spiking visual responses of pyramidal neurons in the three-layer turtle cortex

A primary goal of systems neuroscience is to understand cortical function, typically by studying spontaneous and stimulus-modulated cortical activity. Mounting evidence suggests a strong and complex relationship exists between the ongoing and stimulus-modulated cortical state. To date, most work in this area has been based on spiking in populations of neurons. While advantageous in many respects, this approach is limited in scope: it records the activity of a minority of neurons and gives no direct indication of the underlying subthreshold dynamics. Membrane potential recordings can fill these gaps in our understanding, but stable recordings are difficult to obtain in vivo. Here, we recorded subthreshold cortical visual responses in the ex vivo turtle eye-attached whole brain preparation, which is ideally suited for such a study. We found that, in the absence of visual stimulation, the network was "synchronous"; neurons displayed network-mediated transitions between hyperpolarized (Down) and depolarized (Up) membrane potential states. The prevalence of these slow-wave transitions varied across turtles and recording sessions. Visual stimulation evoked similar Up states, which were on average larger and less reliable when the ongoing state was more synchronous. Responses were muted when immediately preceded by large, spontaneous Up states. Evoked spiking was sparse, highly variable across trials, and mediated by concerted synaptic inputs that were, in general, only very weakly correlated with inputs to nearby neurons. Together, these results highlight the multiplexed influence of the cortical network on the spontaneous and sensory-evoked activity of individual cortical neurons.NEW & NOTEWORTHY Most studies of cortical activity focus on spikes. Subthreshold membrane potential recordings can provide complementary insight, but stable recordings are difficult to obtain in vivo. Here, we recorded the membrane potentials of cortical neurons during ongoing and visually evoked activity. We observed a strong relationship between network and single-neuron evoked activity spanning multiple temporal scales. The membrane potential perspective of cortical dynamics thus highlights the influence of intrinsic network properties on visual processing.

Adaptation modulates correlated subthreshold response variability in visual cortex

Cortical sensory responses are highly variable across stimulus presentations. This variability can be correlated across neurons (due to some combination of dense intracortical connectivity, cortical activity level, and cortical state), with fundamental implications for population coding. Yet the interpretation of correlated response variability (or "noise correlation") has remained fraught with difficulty, in part because of the restriction to extracellular neuronal spike recordings. Here, we measured response variability and its correlation at the most microscopic level of electrical neural activity, the membrane potential, by obtaining dual whole cell recordings from pairs of cortical pyramidal neurons during visual processing in the turtle whole brain ex vivo preparation. We found that during visual stimulation, correlated variability adapts toward an intermediate level and that this correlation dynamic is likely mediated by intracortical mechanisms. A model network with external inputs, synaptic depression, and structure reproduced the observed dynamics of correlated variability. These results suggest that intracortical adaptation self-organizes cortical circuits toward a balanced regime at which correlated variability is maintained at an intermediate level.NEW & NOTEWORTHY Correlated response variability has profound implications for stimulus encoding, yet our understanding of this phenomenon is based largely on spike data. Here, we investigate the dynamics and mechanisms of membrane potential-correlated variability (CC) in visual cortex with a combined experimental and computational approach. We observe a visually evoked increase in CC, followed by a fast return to baseline. Our results further suggest a link between this observation and the adaptation-mediated dynamics of emergent network phenomena.

Adaptation towards scale-free dynamics improves cortical stimulus discrimination at the cost of reduced detection

Fundamental to the function of nervous systems is the ability to reorganize to cope with changing sensory input. Although well-studied in single neurons, how such adaptive versatility manifests in the collective population dynamics and function of cerebral cortex remains unknown. Here we measured population neural activity with microelectrode arrays in turtle visual cortex while visually stimulating the retina. First, we found that, following the onset of stimulation, adaptation tunes the collective population dynamics towards a special regime with scale-free spatiotemporal activity, after an initial large-scale transient response. Concurrently, we observed an adaptive tradeoff between two important aspects of population coding-sensory detection and discrimination. As adaptation tuned the cortex toward scale-free dynamics, stimulus discrimination was enhanced, while stimulus detection was reduced. Finally, we used a network-level computational model to show that short-term synaptic depression was sufficient to mechanistically explain our experimental results. In the model, scale-free dynamics emerge only when the model operates near a special regime called criticality. Together our model and experimental results suggest unanticipated functional benefits and costs of adaptation near criticality in visual cortex.

Inferring presynaptic population spiking from single-trial membrane potential recordings

BACKGROUND

The time-varying membrane potential of a cortical neuron contains important information about the network activity. Extracting this information requires separating excitatory and inhibitory synaptic inputs from single-trial membrane potential recordings without averaging across trials.

NEW METHOD

We propose a method to extract the time course of excitatory and inhibitory synaptic inputs to a neuron from a single-trial membrane potential recording. The method takes advantage of the differences in the time constants and the reversal potentials of the excitatory and inhibitory synaptic currents, which allows the untangling of the two conductance types.

RESULTS

We evaluate the applicability of the method on a leaky integrate-and-fire model neuron and find high quality of estimation of excitatory synaptic conductance changes and presynaptic population spikes. Application of the method to a real cortical neuron with known synaptic inputs in a brain slice returns high-quality estimation of the time course of the excitatory synaptic conductance. Application of the method to membrane potential recordings from a cortical pyramidal neuron of an intact brain reveals complex network activity.

COMPARISON WITH EXISTING METHODS

Existing methods are based on repeated trials and thus are limited to estimating the statistical features of synaptic conductance changes, or, when based on single trials, are limited to special cases, have low temporal resolution, or are impractically complicated.

CONCLUSIONS

We propose and test an efficient method for estimating the full time course of excitatory and inhibitory synaptic conductances from single-trial membrane potential recordings. The method is sufficiently simple to ensure widespread use in neuroscience.