Information

How are neurons selective towards specific stimuli?


I've read several papers that mention that there are specific neurons that are activated for specific things (e.g. neuron A activate only when horizontal lines appear, neuron B activate when certain sound appear, etc). How does this happen over time (unless the neurons are "born" with certain "affection" towards stimuli) and what is the mechanism that allow it? I understand that it's probably not some conformational change in proteins since the activity has to be really fast- so how can an electrical signal be selective?


Sensory receptors and neurons get their selectivity from physical processes and their position in space. For example, photoreceptor cells in the retina respond to light in a specific position in space because the lens and structure of the eye directs light incoming in a particular angle to a specific spot on the retina. Sensory receptors in the skin can sense touch in a particular place because that's where they are located. Hair cells in the cochlea respond to particular frequencies of sound because the membranes in the inner ear vibrate at different frequencies along the length.

For selectivity to lines of a particular orientation, neurons combine receptive fields of retinal cells, like this:

From https://grey.colorado.edu/CompCogNeuro/index.php/CCNBook/Perception

The "LGN" (visual thalamus) cells pictured here have receptive fields that look a lot like the retinal ganglion cells that carry output from the retina. For simplicity, you can think like they are the same. These cells are excited by light in the center (red), and inhibited (indirectly) by their neighbors (blue) (there are also "off-center" cells that respond in the opposite way, preferring dark in the center and light in the surround).

If you add up a bunch in a row, you can make a cell in primary visual cortex (V1, the green cell) that responds to edges. If you summed up the receptive fields of cells in a different orientation you'd get selectivity to a different orientation of lines. The key feature to making selective receptive fields is which cells are connected to which.

These receptive fields are created in development by spontaneous activity in the retina and eventually by actual visual input when the eyes open. The process is pretty complex and occurs in multiple stages, but you can start with a review like this one:

Huberman, A. D., Feller, M. B., & Chapman, B. (2008). Mechanisms underlying development of visual maps and receptive fields. Annu. Rev. Neurosci., 31, 479-509.

Higher in the visual processing hierarchy you will find increasing complexity as receptive fields of different types are combined and recombined.


Selective processing of all rotational and translational optic flow directions in the zebrafish pretectum and tectum

The processing of optic flow in the pretectum/accessory optic system allows animals to stabilize retinal images by executing compensatory optokinetic and optomotor behavior. The success of this behavior depends on the integration of information from both eyes to unequivocally identify all possible translational or rotational directions of motion. However, it is still unknown whether the precise direction of ego-motion is already identified in the zebrafish pretectum or later in downstream premotor areas.

Results

Here, we show that the zebrafish pretectum and tectum each contain four populations of motion-sensitive direction-selective (DS) neurons, with each population encoding a different preferred direction upon monocular stimulation. In contrast, binocular stimulation revealed the existence of pretectal and tectal neurons that are specifically tuned to only one of the many possible combinations of monocular motion, suggesting that further downstream sensory processing might not be needed to instruct appropriate optokinetic and optomotor behavior.

Conclusion

Our results suggest that local, task-specific pretectal circuits process DS retinal inputs and carry out the binocular sensory computations necessary for optokinetic and optomotor behavior.


Background

Odors are powerful drivers of a wide range of behavioral responses in fish, such as reproduction, foraging, and defensive behaviors [1,2,3]. The neural pathways underlying these stereotyped behaviors have been the focus of extensive research and are well described [4,5,6,7,8,9,10,11,12,13,14,15]. In the sea lampreys, a neural pathway comprising the olfactory bulb, posterior tuberculum, and mesencephalic locomotor region converts olfactory inputs into locomotor outputs by activating conserved brainstem pre-motor neurons (the reticulospinal neurons) [16,17,18,19], which in turn control the spinal cord locomotor centers [4]. Food-related odorants activate hypothalamic regions involved in appetite control in zebrafish [8] and evoke foraging behavior in a wide range of fish species [7, 8, 20,21,22,23]. In the zebrafish, alarm odors activate nuclei located in the dorso-medial (Dm) [13, 24] and ventral (Vv) telencephalon [24], as well as in the preoptic area [24]. Respectively, these zebrafish brain regions are homologous to the mammalian amygdala, septum, and paraventricular nucleus of the hypothalamus involved in adaptive fear response and anti-predatory behaviors [25,26,27,28]. Thus, a precise characterization of the link between ecologically relevant odors and odor-driven behaviors is an important step towards characterizing the neural circuits generating these essential behaviors and how they are affected by animal’s internal states, such as hunger, fear, or anxiety. Paradoxically, while the zebrafish olfactory circuitry is well characterized, a comprehensive description of zebrafish behavior in response to ecologically relevant odors is needed, to better relate olfactory computations to animal behavior.

A growing number of studies have begun to address this gap in knowledge by characterizing the change in zebrafish swimming patterns in response to odors, identifying clear negative (avoidance) and positive (approach) chemotactic responses [4, 5, 7,8,9,10,11,12, 14, 15]. These studies investigated the behavioral responses to odors belonging to one or two of the following categories: food-related odors [7, 8, 21], social-related odors [9, 13, 29], decay-related odors (polyamines in decomposing flesh) [30], and alarm odors [24, 31, 32]. This approach precludes the comparison of behavioral responses between all four odor categories within the same individual that is important to uncover stereotyped and odor-specific motor programs. For example, to our knowledge, the response to aversive decay-related odors and alarm odors was not compared in the same individual. Moreover, odor-driven behaviors were either measured in groups of fish, thus masking potential inter-individual variability in odor sensitivity or preferences [9, 13, 21, 24], or the inter-individual variability was not quantified [7, 8, 30,31,32,33]. Therefore, there is an important need for measuring and analyzing the behavioral responses of individual fish to a broad range of odors, spanning the natural stimulus space.

Here, we characterize zebrafish odor-driven behaviors using a medium-throughput setup allowing for exposure to well-defined odor concentrations. Using this approach, the swimming trajectories of 10 fish were recorded in response to 17 ecologically relevant odors. By selecting seven appropriate locomotor metrics, we constructed behavioral ethograms systematically describing odor-induced changes in the swimming trajectory. We found that fish reacted to most odorants with different behavioral programs. A combination of few relevant behavioral metrics was sufficient to capture most of the variance in these innate odor responses. Odors in similar categories elicited weakly clustered behavioral responses. In addition, we quantified intra- and inter-individual variability of odor-driven behaviors and suggest a small set of odors that elicit robust responses. Finally, we showed that conspecific blood and the alarm odor “skin extract” elicit very similar defensive behaviors and activate overlapping regions in the dorso-lateral olfactory bulb.


Discussion

Altogether, we found a greater proportion of target-selective units in the hippocampi of subjects who were able to effectively discriminate old from similar new items in memory compared with those who performed poorly on the test. The magnitude of decrease in the firing rate from targets versus very similar lures correlated with memory performance across the group, and this correlation was specific to the hippocampus. In contrast, firing rate changes from the target to similar lures in ERC, PHC, and amygdala neurons were not related to memory performance.

Collectively, these data show that for individual neurons in the human hippocampus, firing-rate selectivity is associated with a better performance on a task requiring mnemonic discrimination. In addition to encoding specific events, the hippocampus is thought to play a role in generalization and maintenance of flexible relationships between events (15, 16). We have also previously described MTL neural responses that reflect this kind of generalization, whereby single neurons are highly selective (e.g., respond to a particular famous person) but show a high degree of invariance (e.g., respond to multiple different images of the same person refs. 9 ⇓ ⇓ –12). In the present study, participants performed a task in which they were required to learn specific photographs and discriminate these from similar lures, and thus, forming highly specific memory representations was key to successful performance. Although we did not investigate whether the cells in the current study were invariant to the same degree as those reported (9) (only a limited number of lures were presented in this task), we note that the presence of such cells in the hippocampus is not inconsistent with the presence of invariant cells in the MTL. One possibility is that different cells of the MTL support stimulus discrimination and stimulus invariance and their relative frequency may differ across regions. Another possibility is that the same cells are involved in both processes, and the specific task demands increase the likelihood that cells will exhibit discriminative properties. Indeed, an important difference from our previous work (9) is that we used a memory test in which subjects were required to form very specific memory representations to perform well. It is thus possible that top-down influences of task instructions change the firing patterns in the hippocampus. Under these circumstances, modulation of hippocampal activity by projections from other areas may confer additional selectivity and hippocampal neurons may fire in a more restricted manner to specific memorized stimuli (e.g., particular photograph of a famous individual) compared with other MTL regions. In support of the second scenario, of the cells that exhibited invariance during the screening and encoding phases of the experiment, the majority of target-responsive neurons in all MTL regions in this study showed reduced firing for lure photographs of the same individual during the recognition memory task (Fig. S3). In neurons in the hippocampi of high performers, the reduction in firing was particularly pronounced, even for lure photographs that were very similar to the target. In the parahippocampal gyrus, firing rate in target responsive neurons was characterized by a decreasing linear trend as a function of similarity to the target (F = 4.87, P = 0.02). Interestingly, the pattern of firing in the hippocampus and surrounding parahippocampal gyrus corresponds to the predictions of dual process theories of recognition (17) with firing in the hippocampus consistent with a thresholded recollection response, decreasing sharply and similarly for all nontargets, whereas firing in the parahippocampal gyrus shows a pattern consistent with a familiarity process whereby firing decreases in a graded fashion according to similarity. This study provides unique human neuronal data supporting the idea that the hippocampus and surrounding cortex support different processes in recognition memory (18).

We note that one concern with our comparison of HP and LP epilepsy patients is that general hippocampal pathology could be contributing to the memory impairment and low level of selectivity of responsive neurons in LP groups, rather than the low selectivity being specifically related to memory performance. However, the correlation between memory for specific items and the selectivity of corresponding responsive individual hippocampal neurons demonstrates a relationship between neuronal firing and memory that does not depend on the distinction between the HP and LP groups. Second, even within the HP group, subjects who have relatively normal memory as assessed by neuropsychological testing show a significant correlation between the selectivity of neuronal firing and performance. Third, although hippocampal neurons in the LP group showed less selectivity, it was not the case that hippocampal neural activity appeared generally different from activity in the HP group. Overall firing rate and the percentage of target responsive neurons were similar in both groups as was the proportion of target-responsive neurons between the two groups. Thus, while the presence of epilepsy may have impacted hippocampal function and led to poorer memory in some of the subjects, we believe that overall, the data demonstrate that the selectivity of neuronal firing is reflective of mnemonic processing in the hippocampus. Differences between low and high performers were only apparent when comparing the proportion of significant hippocampal responses to the target versus close lure images. Additionally, there were no differences in the proportion of target-selective neurons within ERC, PHC, or amygdala regions between low and high performers, thus further supporting the results that memory performance is related specifically to the selectivity of hippocampal neurons to the target images. The results here are the first to our knowledge to demonstrate a correlation between the selective behavior of individual hippocampal neurons and human memory performance.

In the present study, we sampled neurons in both the CA3/dentate gyrus region and the subiculum. Sampling of target responsive neurons in the CA1 was limited (4 of 16 total recorded Table S3) and, thus, we were unable to assess its firing pattern compared with other hippocampal subregions. Single neuron studies in rodents suggest there are subregional differences in specific firing patterns among hippocampal neurons (19 ⇓ ⇓ –22). For example, place cells in the dentate gyrus and CA3 often show greater sensitivity to changes in context than those in CA1, at least in some instances (21, 22). Animal studies and computational models suggest dentate gyrus can be biased toward pattern separation-like properties, whereas CA1 can be biased toward pattern completion-like properties (21, 22). Although our analysis limited to the CA3DG region yielded a significant correlation between neuronal selectivity and subsequent memory of specific items, further investigation is required to understand the subtle subregional differences within hippocampal circuit and the contributions of separation vs. completion processes in human memory.

Our current study provides human neuronal evidence for the role of the hippocampus in human memory, suggesting that the formation of individual memory representations is expressed in differential firing of hippocampal neurons and that discriminant firing of these cells is directly related to declarative memory performance in humans. These results can be used to inform circuit-level models of the role of the hippocampus in the encoding and retrieval of distinct episodic memories.


Discussion

In this study, we found that a subset of amygdala neurons encodes the subjective judgment of the emotion shown in faces. Behaviorally, our epilepsy patients did not differ from healthy controls in terms of learning performance on the task, and both epilepsy patients and control subjects primarily used the eye region of the stimuli to correctly judge fear faces and primarily used the mouth region to correctly judge happy faces, findings consistent with prior studies (34, 35). Forty-one out of 185 cells significantly differentiated the two emotions, and subsequent analyses indicated that these cells encoded the patients’ subjective judgment regardless of whether it was correct or incorrect. Population permutation analysis with full independence between selection and prediction confirmed the robustness of this result when tested across the entire population. ROI analysis revealed that eyes but not the mouth strongly modulated population neuronal responses to emotions. Lastly, when we carried out identical recordings, in the same patients, from neurons within the hippocampus, we found responses driven only by the objective emotion shown in the face stimulus, and no evidence for responses driven by subjective judgment.

It is notable that the population response metric for the correct trials was further away from the null distribution relative to the incorrect trials (25.0% vs. −4.63%). It is not surprising that the strength of emotion coding in incorrect trials was weaker given fewer incorrect trials and thus potentially increased variability and decreased reliability. In addition, incorrect trials were likely a mixture of different types of error trials, such as true misidentifications of emotion, guesses, or accidental motor errors. Regardless, on average, the neural response during incorrect trials reliably indicated the subjectively perceived emotion. This suggests that a proportion of error trials was likely true misidentifications of the emotion rather than pure guesses.

Interestingly, there was a significant difference between the two types of happy subjective judgments (comparing happy-correct and fear-incorrect Fig. 4E). This might reflect a different strategy used by subjects to compare the two emotions in our specific task. Future studies with a range of different tasks will be needed to understand how relative coding of emotion identity and task demands may interact in shaping neuronal responses.

Possible Confounds.

Our stimuli were based on the well-validated set of facial emotion images from Ekman and Friesen (37), from which we chose a subset depicting fear and happy emotions with the highest reliability. We normalized these base faces for luminance, orientation, color, and spatial frequency, eliminating these low-level visual properties as possible confounds. Likewise, we showed a balanced number of male and female faces, and multiple identities, ensuring that neither sex nor individual identity of the face was driving the responses we report (each of these was completely uncorrelated with the emotion shown in the face). Nonetheless, it remains possible that our findings reflect higher-level properties that are correlated with the emotions fear and happiness—such as negative versus positive valence. Furthermore, because we only tested two facial emotions, our conclusions can only speak to the emotions that we tested and are relative to the task that we used. Different facial regions would have likely been informative for other facial emotions (had the task been a discrimination task that required a choice between, say, surprise and happiness), and we do not know whether the cells studied here might contribute to perceptual decisions for other emotions. A larger set of emotions, as well as of facial expressions without emotional meaning, would be important to study in future studies.

Our results suggest that emotion-selective neurons were not merely encoding the motor output associated with the perceived emotions (button press), as corroborated by the lack of correlation between the neuronal and behavioral response [consistent with similar prior findings (16)], and the lack of lateralization of emotion neurons given the lateralized and fixed motor output actions. Although there has been a recent report of an interaction between spatial laterality and reward coding in the primate amygdala probed with lateralized reward cues (38), that effect appeared primarily as a difference in latency but not as the lateralization of reward-coding neurons to the reward-predicting cues. It will be interesting to investigate in future studies whether these findings with basic rewards (38) can be generalized to emotions or other salient stimuli.

We initially selected emotion-selective neurons using a one-tailed t test of fear vs. happy for correct trials only. Clearly, some cells surviving this test will be false positives to quantify the robustness of the effect we thus conducted several additional analyses. First, we conducted a 50/50 split analysis procedure, which keeps the trials used for selection and prediction independent (Fig. 6). The result (Fig. 7) is an out-of-sample estimate of the true effect size and would thus not be expected to be different from chance if all selected cells were false positives. In contrast, we observed a highly reliable effect (Fig. 7), which is very unlikely to be driven by chance alone. Second, the sets of cells selected by the two different methods were comparable, showing that emotion-selective neurons were consistently selected even with a random subset of trials. Third, we rigorously established chance levels using permutation tests (Fig. 7) and found that the number of cells selected was well above chance (Fig. 6). Fourth, we conducted additional control analyses using a time window −250 ms to 750 ms relative to scramble onset (no information about the upcoming face was available during this time window). The number of selected cells was as expected by chance and we did not find the significant patterns we report in the case of responses to faces. Similarly, we also did not replicate the pattern of amygdala responses to faces when we analyzed responses from hippocampal neurons. Taken together, the last two findings provide both stimulus specificity and neuroanatomical specificity to our conclusions. Lastly, we conducted analyses using a random subset of the amygdala neurons (n = 67, the number of hippocampal neurons recorded) at each permutation run and we derived qualitatively the same results (Fig. 7B), showing that our results were not driven by a particular subset of neurons.

Selectivity of Amygdala Neurons.

Faces can be readily characterized by independent attributes, such as identity, expression, and sex, which have segregated cortical representations (13, 39), and single-unit recordings in the primate amygdala have documented responses selective for faces, their identity, or emotional expression (10, 14). We previously showed that neurons in the human amygdala selectively respond to whole faces compared with facial parts, suggesting a predominant role of the amygdala in representing global information about faces (16). How do these whole-face-selective cells overlap with the emotion-selective cells we report in the present work? We found 3 out of 24 (12.5%) fear-selective cells and 5 out of 17 (29.4%) happy-selective cells are also whole-face-selective, a ratio of whole-face cells similar to that found in the entire population (36 out of 185, 19.5%). This suggests that amygdala neurons encode whole-face information and emotion independently.

We found that face information conveyed by the eyes, but not the mouth region, modulated emotion-selective neuronal responses. Compared with our previous neuronal classification images which were based on pixelwise analyses of face regions that drive neuronal response (17), we here used a fully independent permutation test to further illustrate that when eyes are more visible, the population of neurons can discriminate the emotions better (also see Table S2). Together with a substantial prior literature, this finding supports the idea that amygdala neurons synthesize their responses based substantially on information from the eye region of faces (18, 21, 34).

The Amygdala, Consciousness, and Perception.

Does the amygdala’s response to emotional faces require, or contribute to, conscious awareness? Some studies have suggested that emotional faces can modulate amygdala activity without explicit awareness of the stimuli (40, 41), and there are reports of amygdala blood-oxygen–level dependent (BOLD) discrimination to the presentation of fear faces even if such faces are presented to patients in their blind hemifield in cases of hemianopia due to cortical lesions (42). Our finding that amygdala neurons track subjective perceptual judgment argues for a key role in conscious perception, although it does not rule out a role in nonconscious processing as well. Further support for a role in contributing to our conscious awareness of the stimuli comes from the long response latencies we observed, consistent with previous findings on long latencies in the medial temporal lobe (43). Our findings suggest that the amygdala might interact with visual cortices in the temporal lobe to construct our conscious percept of the emotion shown in a face, an interaction that likely requires additional components such as frontal cortex, whose identity remains to be fully investigated (44). In particular, because we failed to find any coding of subjectively perceived emotion in the hippocampus, it will be an important future direction to record from additional brain regions to fully understand how the amygdala responses we report might be synthesized.

Microstimulation of inferotemporal cortex in monkeys (45) and electrical brain stimulation in fusiform areas in humans (46) have suggested a causal role of the temporal cortex in face categorization and perception. Future studies using direct stimulation of the amygdala will be important to further determine the nature of its contribution to the subjective perception of facial emotion. Given the long average response latency observed in the amygdala neurons we analyzed, it may well be that the responses we report here reflect perceptual decisions that were already computed at an earlier time epoch. We would favor a distributed view, in which the subjective perceptual decision of the facial emotion emerges over some window of time, and drawing on a spatially distributed set of regions. The neuronal responses we report in the amygdala may be integral part of such computations, or they may instead reflect the readout of processes that have already occurred elsewhere in the brain. Only concurrent recordings from multiple brain regions will be able to fully resolve this issue in future studies.

Comparison with Neuroimaging Studies and Functional Role of the Amygdala.

We further compare our study with neuroimaging studies and discuss the functional role of the amygdala in SI Discussion.


Brain

Alan Gesek / Stocktrek Images / Getty Images

The brain is the control center of the body. It has a wrinkled appearance due to bulges and depressions known as gyri and sulci. One of these furrows, the medial longitudinal fissure, divides the brain into left and right hemispheres. Covering the brain is a protective layer of connective tissue known as the meninges.

The forebrain is responsible for a variety of functions including receiving and processing sensory information, thinking, perceiving, producing and understanding language, and controlling motor function. The forebrain contains structures, such as the ​thalamus and hypothalamus, which are responsible for such functions as motor control, relaying sensory information, and controlling autonomic functions. It also contains the largest part of the brain, the cerebrum.

Most of the actual information processing in the brain takes place in the cerebral cortex. The cerebral cortex is the thin layer of gray matter that covers the brain. It lies just beneath the meninges and is divided into four cortex lobes:

These lobes are responsible for various functions in the body that include everything from sensory perception to decision-making and problem-solving.

Below the cortex is the brain's white matter, which is composed of nerve cell axons that extend from the neuron cell bodies of gray matter. White matter nerve fiber tracts connect the cerebrum with different areas of the brain and spinal cord.

The midbrain and the hindbrain together make up the brainstem. The midbrain is the portion of the brainstem that connects the hindbrain and the forebrain. This region of the brain is involved in auditory and visual responses as well as motor function.

The hindbrain extends from the spinal cord and contains structures such as the pons and cerebellum. These regions assist in maintaining balance and equilibrium, movement coordination, and the conduction of sensory information. The hindbrain also contains the medulla oblongata which is responsible for controlling such autonomic functions as breathing, heart rate, and digestion.


Contents

Neurons are the primary components of the nervous system, along with the glial cells that give them structural and metabolic support. The nervous system is made up of the central nervous system, which includes the brain and spinal cord, and the peripheral nervous system, which includes the autonomic and somatic nervous systems. In vertebrates, the majority of neurons belong to the central nervous system, but some reside in peripheral ganglia, and many sensory neurons are situated in sensory organs such as the retina and cochlea.

Axons may bundle into fascicles that make up the nerves in the peripheral nervous system (like strands of wire make up cables). Bundles of axons in the central nervous system are called tracts.

Neurons are highly specialized for the processing and transmission of cellular signals. Given their diversity of functions performed in different parts of the nervous system, there is a wide variety in their shape, size, and electrochemical properties. For instance, the soma of a neuron can vary from 4 to 100 micrometers in diameter. [1]

  • The soma is the body of the neuron. As it contains the nucleus, most protein synthesis occurs here. The nucleus can range from 3 to 18 micrometers in diameter. [2]
  • The dendrites of a neuron are cellular extensions with many branches. This overall shape and structure is referred to metaphorically as a dendritic tree. This is where the majority of input to the neuron occurs via the dendritic spine.
  • The axon is a finer, cable-like projection that can extend tens, hundreds, or even tens of thousands of times the diameter of the soma in length. The axon primarily carries nerve signals away from the soma, and carries some types of information back to it. Many neurons have only one axon, but this axon may—and usually will—undergo extensive branching, enabling communication with many target cells. The part of the axon where it emerges from the soma is called the axon hillock. Besides being an anatomical structure, the axon hillock also has the greatest density of voltage-dependent sodium channels. This makes it the most easily excited part of the neuron and the spike initiation zone for the axon. In electrophysiological terms, it has the most negative threshold potential.
    • While the axon and axon hillock are generally involved in information outflow, this region can also receive input from other neurons.

    The accepted view of the neuron attributes dedicated functions to its various anatomical components however, dendrites and axons often act in ways contrary to their so-called main function. [ citation needed ]

    Axons and dendrites in the central nervous system are typically only about one micrometer thick, while some in the peripheral nervous system are much thicker. The soma is usually about 10–25 micrometers in diameter and often is not much larger than the cell nucleus it contains. The longest axon of a human motor neuron can be over a meter long, reaching from the base of the spine to the toes.

    Sensory neurons can have axons that run from the toes to the posterior column of the spinal cord, over 1.5 meters in adults. Giraffes have single axons several meters in length running along the entire length of their necks. Much of what is known about axonal function comes from studying the squid giant axon, an ideal experimental preparation because of its relatively immense size (0.5–1 millimeters thick, several centimeters long).

    Fully differentiated neurons are permanently postmitotic [3] however, stem cells present in the adult brain may regenerate functional neurons throughout the life of an organism (see neurogenesis). Astrocytes are star-shaped glial cells. They have been observed to turn into neurons by virtue of their stem cell-like characteristic of pluripotency.

    Membrane Edit

    Like all animal cells, the cell body of every neuron is enclosed by a plasma membrane, a bilayer of lipid molecules with many types of protein structures embedded in it. A lipid bilayer is a powerful electrical insulator, but in neurons, many of the protein structures embedded in the membrane are electrically active. These include ion channels that permit electrically charged ions to flow across the membrane and ion pumps that chemically transport ions from one side of the membrane to the other. Most ion channels are permeable only to specific types of ions. Some ion channels are voltage gated, meaning that they can be switched between open and closed states by altering the voltage difference across the membrane. Others are chemically gated, meaning that they can be switched between open and closed states by interactions with chemicals that diffuse through the extracellular fluid. The ion materials include sodium, potassium, chloride, and calcium. The interactions between ion channels and ion pumps produce a voltage difference across the membrane, typically a bit less than 1/10 of a volt at baseline. This voltage has two functions: first, it provides a power source for an assortment of voltage-dependent protein machinery that is embedded in the membrane second, it provides a basis for electrical signal transmission between different parts of the membrane.

    Histology and internal structure Edit

    Numerous microscopic clumps called Nissl bodies (or Nissl substance) are seen when nerve cell bodies are stained with a basophilic ("base-loving") dye. These structures consist of rough endoplasmic reticulum and associated ribosomal RNA. Named after German psychiatrist and neuropathologist Franz Nissl (1860–1919), they are involved in protein synthesis and their prominence can be explained by the fact that nerve cells are very metabolically active. Basophilic dyes such as aniline or (weakly) haematoxylin [4] highlight negatively charged components, and so bind to the phosphate backbone of the ribosomal RNA.

    The cell body of a neuron is supported by a complex mesh of structural proteins called neurofilaments, which together with neurotubules (neuronal microtubules) are assembled into larger neurofibrils. [5] Some neurons also contain pigment granules, such as neuromelanin (a brownish-black pigment that is byproduct of synthesis of catecholamines), and lipofuscin (a yellowish-brown pigment), both of which accumulate with age. [6] [7] [8] Other structural proteins that are important for neuronal function are actin and the tubulin of microtubules. Class III β-tubulin is found almost exclusively in neurons. Actin is predominately found at the tips of axons and dendrites during neuronal development. There the actin dynamics can be modulated via an interplay with microtubule. [9]

    There are different internal structural characteristics between axons and dendrites. Typical axons almost never contain ribosomes, except some in the initial segment. Dendrites contain granular endoplasmic reticulum or ribosomes, in diminishing amounts as the distance from the cell body increases.

    Neurons vary in shape and size and can be classified by their morphology and function. [11] The anatomist Camillo Golgi grouped neurons into two types type I with long axons used to move signals over long distances and type II with short axons, which can often be confused with dendrites. Type I cells can be further classified by the location of the soma. The basic morphology of type I neurons, represented by spinal motor neurons, consists of a cell body called the soma and a long thin axon covered by a myelin sheath. The dendritic tree wraps around the cell body and receives signals from other neurons. The end of the axon has branching axon terminals that release neurotransmitters into a gap called the synaptic cleft between the terminals and the dendrites of the next neuron.

    Structural classification Edit

    Polarity Edit

    Most neurons can be anatomically characterized as:

      : single process : 1 axon and 1 dendrite : 1 axon and 2 or more dendrites
        : neurons with long-projecting axonal processes examples are pyramidal cells, Purkinje cells, and anterior horn cells : neurons whose axonal process projects locally the best example is the granule cell

      Other Edit

      Some unique neuronal types can be identified according to their location in the nervous system and distinct shape. Some examples are:

        , interneurons that form a dense plexus of terminals around the soma of target cells, found in the cortex and cerebellum , large motor neurons , interneurons of the cerebellum , most neurons in the corpus striatum , huge neurons in the cerebellum, a type of Golgi I multipolar neuron , neurons with triangular soma, a type of Golgi I , neurons with both ends linked to alpha motor neurons , interneurons with unique dendrite ending in a brush-like tuft , a type of Golgi II neuron cells, motoneurons located in the spinal cord , interneurons that connect widely separated areas of the brain

      Functional classification Edit

      Direction Edit

        convey information from tissues and organs into the central nervous system and are also called sensory neurons. (motor neurons) transmit signals from the central nervous system to the effector cells. connect neurons within specific regions of the central nervous system.

      Afferent and efferent also refer generally to neurons that, respectively, bring information to or send information from the brain.

      Action on other neurons Edit

      A neuron affects other neurons by releasing a neurotransmitter that binds to chemical receptors. The effect upon the postsynaptic neuron is determined by the type of receptor that is activated, not by the presynaptic neuron or by the neurotransmitter. A neurotransmitter can be thought of as a key, and a receptor as a lock: the same neurotransmitter can activate multiple types of receptors. Receptors can be classified broadly as excitatory (causing an increase in firing rate), inhibitory (causing a decrease in firing rate), or modulatory (causing long-lasting effects not directly related to firing rate).

      The two most common (90%+) neurotransmitters in the brain, glutamate and GABA, have largely consistent actions. Glutamate acts on several types of receptors, and has effects that are excitatory at ionotropic receptors and a modulatory effect at metabotropic receptors. Similarly, GABA acts on several types of receptors, but all of them have inhibitory effects (in adult animals, at least). Because of this consistency, it is common for neuroscientists to refer to cells that release glutamate as "excitatory neurons", and cells that release GABA as "inhibitory neurons". Some other types of neurons have consistent effects, for example, "excitatory" motor neurons in the spinal cord that release acetylcholine, and "inhibitory" spinal neurons that release glycine.

      The distinction between excitatory and inhibitory neurotransmitters is not absolute. Rather, it depends on the class of chemical receptors present on the postsynaptic neuron. In principle, a single neuron, releasing a single neurotransmitter, can have excitatory effects on some targets, inhibitory effects on others, and modulatory effects on others still. For example, photoreceptor cells in the retina constantly release the neurotransmitter glutamate in the absence of light. So-called OFF bipolar cells are, like most neurons, excited by the released glutamate. However, neighboring target neurons called ON bipolar cells are instead inhibited by glutamate, because they lack typical ionotropic glutamate receptors and instead express a class of inhibitory metabotropic glutamate receptors. [12] When light is present, the photoreceptors cease releasing glutamate, which relieves the ON bipolar cells from inhibition, activating them this simultaneously removes the excitation from the OFF bipolar cells, silencing them.

      It is possible to identify the type of inhibitory effect a presynaptic neuron will have on a postsynaptic neuron, based on the proteins the presynaptic neuron expresses. Parvalbumin-expressing neurons typically dampen the output signal of the postsynaptic neuron in the visual cortex, whereas somatostatin-expressing neurons typically block dendritic inputs to the postsynaptic neuron. [13]

      Discharge patterns Edit

      Neurons have intrinsic electroresponsive properties like intrinsic transmembrane voltage oscillatory patterns. [14] So neurons can be classified according to their electrophysiological characteristics:

      • Tonic or regular spiking. Some neurons are typically constantly (tonically) active, typically firing at a constant frequency. Example: interneurons in neurostriatum.
      • Phasic or bursting. Neurons that fire in bursts are called phasic.
      • Fast spiking. Some neurons are notable for their high firing rates, for example some types of cortical inhibitory interneurons, cells in globus pallidus, retinal ganglion cells. [15][16]

      Neurotransmitter Edit

      • Cholinergic neurons—acetylcholine. Acetylcholine is released from presynaptic neurons into the synaptic cleft. It acts as a ligand for both ligand-gated ion channels and metabotropic (GPCRs) muscarinic receptors. Nicotinic receptors are pentameric ligand-gated ion channels composed of alpha and beta subunits that bind nicotine. Ligand binding opens the channel causing influx of Na + depolarization and increases the probability of presynaptic neurotransmitter release. Acetylcholine is synthesized from choline and acetyl coenzyme A.
      • Adrenergic neurons—noradrenaline. Noradrenaline (norepinephrine) is release from most postganglionic neurons in the sympathetic nervous system onto two sets of GPCRs: alpha adrenoceptors and beta adrenoceptors. Noradrenaline is one of the three common catecholamine neurotransmitter, and the most prevalent of them in the peripheral nervous system as with other catecholamines, it is synthesised from tyrosine.
      • GABAergic neurons—gamma aminobutyric acid. GABA is one of two neuroinhibitors in the central nervous system (CNS), along with glycine. GABA has a homologous function to ACh, gating anion channels that allow Cl − ions to enter the post synaptic neuron. Cl − causes hyperpolarization within the neuron, decreasing the probability of an action potential firing as the voltage becomes more negative (for an action potential to fire, a positive voltage threshold must be reached). GABA is synthesized from glutamate neurotransmitters by the enzyme glutamate decarboxylase.
      • Glutamatergic neurons—glutamate. Glutamate is one of two primary excitatory amino acid neurotransmitters, along with aspartate. Glutamate receptors are one of four categories, three of which are ligand-gated ion channels and one of which is a G-protein coupled receptor (often referred to as GPCR).
        and Kainate receptors function as cation channels permeable to Na + cation channels mediating fast excitatory synaptic transmission. receptors are another cation channel that is more permeable to Ca 2+ . The function of NMDA receptors depend on glycine receptor binding as a co-agonist within the channel pore. NMDA receptors do not function without both ligands present.
  • Metabotropic receptors, GPCRs modulate synaptic transmission and postsynaptic excitability.
    • Dopaminergic neurons—dopamine. Dopamine is a neurotransmitter that acts on D1 type (D1 and D5) Gs-coupled receptors, which increase cAMP and PKA, and D2 type (D2, D3, and D4) receptors, which activate Gi-coupled receptors that decrease cAMP and PKA. Dopamine is connected to mood and behavior and modulates both pre- and post-synaptic neurotransmission. Loss of dopamine neurons in the substantia nigra has been linked to Parkinson's disease. Dopamine is synthesized from the amino acid tyrosine. Tyrosine is catalyzed into levadopa (or L-DOPA) by tyrosine hydroxlase, and levadopa is then converted into dopamine by the aromatic amino acid decarboxylase.
    • Serotonergic neurons—serotonin. Serotonin (5-Hydroxytryptamine, 5-HT) can act as excitatory or inhibitory. Of its four 5-HT receptor classes, 3 are GPCR and 1 is a ligand-gated cation channel. Serotonin is synthesized from tryptophan by tryptophan hydroxylase, and then further by decarboxylase. A lack of 5-HT at postsynaptic neurons has been linked to depression. Drugs that block the presynaptic serotonin transporter are used for treatment, such as Prozac and Zoloft.
    • Purinergic neurons—ATP. ATP is a neurotransmitter acting at both ligand-gated ion channels (P2X receptors) and GPCRs (P2Y) receptors. ATP is, however, best known as a cotransmitter. Such purinergic signalling can also be mediated by other purines like adenosine, which particularly acts at P2Y receptors.
    • Histaminergic neurons—histamine. Histamine is a monoamine neurotransmitter and neuromodulator. Histamine-producing neurons are found in the tuberomammillary nucleus of the hypothalamus. [17] Histamine is involved in arousal and regulating sleep/wake behaviors.

    Multimodel Classification Edit

    Since 2012 there has been a push from the cellular and computational neuroscience community to come up with a universal classification of neurons that will apply to all neurons in the brain as well as across species. this is done by considering the 3 essential qualities of all neurons: electrophysiology, morphology, and the individual transcriptome of the cells. besides being universal this classification has the advantage of being able to classify astrocytes as well. A method called Patch-Seq in which all 3 qualities can be measured at once is used extensively by the Allen Institute for Brain Science. [18]

    Neurons communicate with each other via synapses, where either the axon terminal of one cell contacts another neuron's dendrite, soma or, less commonly, axon. Neurons such as Purkinje cells in the cerebellum can have over 1000 dendritic branches, making connections with tens of thousands of other cells other neurons, such as the magnocellular neurons of the supraoptic nucleus, have only one or two dendrites, each of which receives thousands of synapses.

    Synapses can be excitatory or inhibitory, either increasing or decreasing activity in the target neuron, respectively. Some neurons also communicate via electrical synapses, which are direct, electrically conductive junctions between cells. [19]

    When an action potential reaches the axon terminal, it opens voltage-gated calcium channels, allowing calcium ions to enter the terminal. Calcium causes synaptic vesicles filled with neurotransmitter molecules to fuse with the membrane, releasing their contents into the synaptic cleft. The neurotransmitters diffuse across the synaptic cleft and activate receptors on the postsynaptic neuron. High cytosolic calcium in the axon terminal triggers mitochondrial calcium uptake, which, in turn, activates mitochondrial energy metabolism to produce ATP to support continuous neurotransmission. [20]

    An autapse is a synapse in which a neuron's axon connects to its own dendrites.

    The human brain has some 8.6 x 10 10 (eighty six billion) neurons. [21] Each neuron has on average 7,000 synaptic connections to other neurons. It has been estimated that the brain of a three-year-old child has about 10 15 synapses (1 quadrillion). This number declines with age, stabilizing by adulthood. Estimates vary for an adult, ranging from 10 14 to 5 x 10 14 synapses (100 to 500 trillion). [22]

    In 1937 John Zachary Young suggested that the squid giant axon could be used to study neuronal electrical properties. [23] It is larger than but similar to human neurons, making it easier to study. By inserting electrodes into the squid giant axons, accurate measurements were made of the membrane potential.

    The cell membrane of the axon and soma contain voltage-gated ion channels that allow the neuron to generate and propagate an electrical signal (an action potential). Some neurons also generate subthreshold membrane potential oscillations. These signals are generated and propagated by charge-carrying ions including sodium (Na + ), potassium (K + ), chloride (Cl − ), and calcium (Ca 2+ ).

    Several stimuli can activate a neuron leading to electrical activity, including pressure, stretch, chemical transmitters, and changes of the electric potential across the cell membrane. [24] Stimuli cause specific ion-channels within the cell membrane to open, leading to a flow of ions through the cell membrane, changing the membrane potential. Neurons must maintain the specific electrical properties that define their neuron type. [25]

    Thin neurons and axons require less metabolic expense to produce and carry action potentials, but thicker axons convey impulses more rapidly. To minimize metabolic expense while maintaining rapid conduction, many neurons have insulating sheaths of myelin around their axons. The sheaths are formed by glial cells: oligodendrocytes in the central nervous system and Schwann cells in the peripheral nervous system. The sheath enables action potentials to travel faster than in unmyelinated axons of the same diameter, whilst using less energy. The myelin sheath in peripheral nerves normally runs along the axon in sections about 1 mm long, punctuated by unsheathed nodes of Ranvier, which contain a high density of voltage-gated ion channels. Multiple sclerosis is a neurological disorder that results from demyelination of axons in the central nervous system.

    Some neurons do not generate action potentials, but instead generate a graded electrical signal, which in turn causes graded neurotransmitter release. Such non-spiking neurons tend to be sensory neurons or interneurons, because they cannot carry signals long distances.

    Neural coding is concerned with how sensory and other information is represented in the brain by neurons. The main goal of studying neural coding is to characterize the relationship between the stimulus and the individual or ensemble neuronal responses, and the relationships among the electrical activities of the neurons within the ensemble. [26] It is thought that neurons can encode both digital and analog information. [27]

    The conduction of nerve impulses is an example of an all-or-none response. In other words, if a neuron responds at all, then it must respond completely. Greater intensity of stimulation, like brighter image/louder sound, does not produce a stronger signal, but can increase firing frequency. [28] : 31 Receptors respond in different ways to stimuli. Slowly adapting or tonic receptors respond to steady stimulus and produce a steady rate of firing. Tonic receptors most often respond to increased intensity of stimulus by increasing their firing frequency, usually as a power function of stimulus plotted against impulses per second. This can be likened to an intrinsic property of light where greater intensity of a specific frequency (color) requires more photons, as the photons can't become "stronger" for a specific frequency.

    Other receptor types include quickly adapting or phasic receptors, where firing decreases or stops with steady stimulus examples include skin which, when touched causes neurons to fire, but if the object maintains even pressure, the neurons stop firing. The neurons of the skin and muscles that are responsive to pressure and vibration have filtering accessory structures that aid their function.

    The pacinian corpuscle is one such structure. It has concentric layers like an onion, which form around the axon terminal. When pressure is applied and the corpuscle is deformed, mechanical stimulus is transferred to the axon, which fires. If the pressure is steady, stimulus ends thus, typically these neurons respond with a transient depolarization during the initial deformation and again when the pressure is removed, which causes the corpuscle to change shape again. Other types of adaptation are important in extending the function of a number of other neurons. [29]

    The German anatomist Heinrich Wilhelm Waldeyer introduced the term neuron in 1891, [30] based on the ancient Greek νεῦρον neuron 'sinew, cord, nerve'. [31]

    The word was adopted in French with the spelling neurone. That spelling was also used by many writers in English, [32] but has now become rare in American usage and uncommon in British usage. [33] [31]


    Materials and Methods

    All experimental procedures were performed in accordance with the National Institute of Health’s Guide for the Care and Use of Laboratory Animals and EU Directive 2010/63/EU, and approved by the Ethical Committee at the KU Leuven. The animals in this study were pair-housed with cage enrichment (toys, foraging devices) at the primate facility of the KU Leuven Medical School. They were fed daily with standard primate chow supplemented with nuts, raisins, prunes, and fruits. The animals received their daily water supply either during the awake experiments, or ad libitum in the cages before and after sedated experiments.

    Subjects

    All experiments were performed in four male rhesus monkeys (C: 8 kg K: 6 kg M: 5 kg T: 6 kg). All animals had a custom-made, magnetic resonance imaging (MRI)-compatible headpost and cylinder implanted on the skull using ceramic screws and dental acrylic. All surgeries were performed under isoflurane anaesthesia and sterile conditions. The cylinders were implanted in an oblique orientation (orthogonal to the IPS in monkey C, parallel to the IPS in monkeys M, K, and T) over the IPS at Horsley-Clark coordinates ranging from 10 to 0 P and from 10 to 20 L. In monkey M, the recording cylinder was repositioned before the fMRI-EM experiment in aAIP from an orientation orthogonal to the IPS (S1 Fig., upper row, red arrow) to an oblique orientation parallel to the IPS to allow electrode penetrations parallel to the IPS, targeting the aAIP patch as defined by its neuronal characteristics. Three monkeys (K, M, T) were trained in passive fixation and saccade tasks in a mock fMRI-setup. They were seated in a sphinx position [78] in a plastic monkey chair directly facing an LCD screen (viewing distance: 57 cm). Eye position was monitored at 120 Hz through the pupil position (Iscan, MA, United States). The fourth monkey (C) was scanned only under sedation.

    Electrophysiology

    All stimuli were displayed on a CRT monitor (Vision Research Graphics, equipped with P46 phosphor) operating at 120Hz.

    Stereo test. The stimulus set of the stereo experiment consisted of random-dot stereograms in which depth was defined by horizontal disparity (dot size 0.08 deg, dot density 50%, vertical size 5.5 deg) presented on a grey background [70]. All stimuli were generated using Matlab (MathWorks) and were gamma-corrected. The stimuli in the search test consisted of three types of smoothly curved depth profiles (1, one-half, or one-fourth vertical sinusoidal cycle) together with their antiphase counterparts obtained by interchanging the monocular images between the eyes (disparity amplitude within the surface: 0.5 deg), control stimuli (the monocular images presented to both eyes simultaneously), and flat surfaces at different disparities. Each of the six depth profiles was combined with one of four different circumferential shapes and appeared at two different positions in depth (mean disparity + or—0.5 deg), creating a set of 48 curved surfaces. Ferroelectric liquid crystal shutters (Displaytech) each operating at 60 Hz were used to generate dichoptic presentation. The shutters were synchronized with the vertical retrace of the display monitor. There was no measurable cross-talk between the two eyes [21]. After 200 ms of fixation, the stimulus was presented at the fixation point for 1 s.

    In the search test, all stimuli (stereo and control, curved and flat) were presented randomly interleaved at the center of the display and at the fixation plane during passive fixation. Single or multi-unit activity was recorded, and if a site was visually responsive, we isolated single neurons online and tested these neurons in more detail for higher-order disparity selectivity (i.e., selectivity for gradients of disparity) in the position-in-depth test [5]. In this test the stimulus (a combination of a depth profile and a circumferential shape) evoking the highest response in the search test was selected together with its antiphase counterpart, and presented at five different positions in depth ranging from-0.5 degree (near) to +0.5 degree (far) disparity in equal steps.

    Object test. Previous studies [23,24,58] have characterized pAIP based on the presence of selective visual responses to images of objects presented foveally during passive fixation. The same stimuli as in [23] were used to confirm the presence of object-selective responses in pAIP in three animals (M, K, and C). The stimulus set for the object test consisted of 21 two‐dimensional (2-D) area‐equalized static images of natural and artificial objects, including faces, hands, fruits, branches, and several artificial graspable objects. The presence of object-selective SUA or MUA responses was assessed using a one-way ANOVA (p < 0.05).

    Grasping test. In the visually guided grasping test, a bar attached to a plate was positioned in the monkey’s view. The animal had to rest his right hand on a sensing device in complete darkness for a variable time (inter‐trial interval ITI 3,000–5,000 ms), after which a light inside the object was illuminated, whereupon the monkey had to fixate the object (keeping its gaze inside a ±2.5‐degree fixation window). After a 500 ms fixation period, an audible go‐signal was given for initiating the grasping movement, which consisted of reaching, grasping, and pulling the object on the plate (holding time: 500–900 ms)[24].

    Saccade test. In the visually guided saccade task, monkeys had to maintain fixation within a window of 2 × 2 visual degrees around a small green spot in the center of the display for a fixed duration of 450 ms, after which a single green saccade target appeared at one of ten possible positions on the screen, spaced 15 (horizontal) or 11 (vertical) degrees apart. After a variable time, the green fixation spot dimmed, indicating to the animal to saccade towards the target location. The presence of spatially selective saccadic SUA or MUA responses was confirmed using a one-way ANOVA with factor target position (p < 0.001 for all target-selective cells).

    Scanning

    Functional images were acquired with a 3.0 T full-body scanner (TIM Trio Siemens), using a gradient-echo T2*-weighted echo-planar imaging (EPI) sequence (40 horizontal slices TR: 2s TE: 16 ms 1.25 mm 3 isotropic voxels) with a custom-built eight-channel phased-array receive coil, and a saddle-shaped, radial transmit-only surface coil [79]. Before each scanning session, a contrast agent, monocrystalline iron oxide nanoparticle (MION) (Feraheme: AMAG pharmaceuticals Rienso: Takeda) was injected into the femoral/saphenous vein (7–11 mg/kg) [78].

    To verify the stimulation positions, structural MR images (0.6 mm resolution) were acquired in every sedated scan session (prior to the start of the fMRI experiment) while the electrode was located at the exact stimulation site inside a standard recording grid (Crist Instruments, Hagerstown, MD, US). In the few sessions in which the latter could not be achieved, we inserted glass capillaries filled with a 2% copper sulphate solution into the grid at several positions, acquired structural MR images (0.6 mm resolution) and reconstructed the electrode penetrations using SPM 5 (Statistical Parametric Mapping).

    In every scanning session, a Platinum/Iridium electrode (impedance 50–200 kΩ in situ, FHC, Bowdoinham, ME) was inserted in the grid through glass capillaries serving as guide tubes (Plastics One Inc, Kent, United Kingdom FHC, Bowdoinham, ME, US). A platinum wire served as ground. The electrical microstimulation (EM) signal was produced using an eight-channel digital stimulator (DS8000, World Precision Instruments) in combination with a current isolator (DLS100, World Precision Instruments). During stimulation blocks, a single EM train was applied in every trial.

    In awake scanning sessions, the animals were either fixating a spot on a screen (Fix) or performing memory-guided saccades (Sacc) towards ten different positions contralateral to the stimulated hemisphere. Briefly, during the memory-guided saccade task a saccade target was flashed for 200 ms on the screen, and the animals had to maintain fixation (300–1,500 ms) until the dimming of the fixation point instructed an eye movement to the remembered target location. During the baseline fixation task (Fix0), only a central fixation point was displayed on the screen, while during the control fixation task (Fix1), one distractor (identical to the saccade target in the Sacc task) was shown on the screen with the same position and timing parameters as the saccade target in the memory saccade task. The color of the fixation point indicated to the animals to either maintain fixation or to make saccades. In this study, the data collected during all three tasks were combined. The three tasks were presented to the animals in blocks, and EM was administered during all three tasks, thus creating six types of blocks which were alternated in one run in pseudo-random order. We alternated between stimulation and no-stimulation blocks (each lasting 40 s), with each run lasting 245 pulses (490 s).

    Stimulation trains in awake scan sessions lasted 500 ms and were composed of biphasic square-wave pulses (repetition rate 200 Hz amplitude 200 μA). Note that pilot experiments showed that a current amplitude of less than 200 μA did not evoke increased fMRI-activations. Each pulse consisted of 190 μs of positive and 190 μs of negative voltage, with 0.1 ms between the two pulses (total pulse duration: 0.48 ms). During sedated scanning sessions, a trial-by-trial stimulation protocol was used similar to the awake sessions (one EM train every 3 s, approximately). EM trains in sedated sessions lasted 250 ms with an amplitude of 1 mA, while other EM-parameters remained similar (200 Hz, 0.48 ms pulse duration). The timing of the EM pulses during the fMRI experiment was computer controlled. Note that pilot experiments showed that a current amplitude of 200 μA (= current strength during awake sessions) during sedated sessions only caused increased fMRI-activations around the tip of the electrode.

    Sedation

    During sedated scan sessions, a 0.5/0.5 cc mixture of ketamine (Ketalar Pfizer) and medetomidine (Domitor Orion) was administered every 45 min. The animals were video-controlled during sedation, and body temperature was maintained using a heating pad.

    Data Analysis

    Off-line image reconstruction was conducted to overcome problems inherent to monkey body motion at 3T. Details about the image reconstruction protocol have been given elsewhere [79]. Briefly, the raw EPI images were corrected for lowest-order off-resonance effects and aligned with respect to the gradient-recalled-echo reference images before performing a SENSE (sensitivity encoding) image reconstruction [80]. Corrections for higher-order distortions were performed using a non-rigid slice-by-slice distortion correction.

    Data were analyzed using statistical parametric mapping (SPM5) and BrainMatch software, using a fixed-effect GLM. Realignment parameters were included as covariates of no interest to remove brain motion artifacts. Spatial preprocessing consisted of realignment and rigid coregistration with a template anatomy (M12) [11]. To compensate for echo-planar distortions in the images as well as inter-individual anatomical differences, the functional images were warped to the template anatomy using non-rigid matching BrainMatch software [81]. The algorithm computes a dense deformation field by the composition of small displacements minimizing a local correlation criterion. Regularization of the deformation field is obtained by low-pass filtering. The functional volumes were then resliced to 1 mm 3 isotropic and smoothed with an isotropic Gaussian kernel (full width at half maximum: 1.5 mm). Single subject and group analyses were performed, and the level of significance was set at p < 0.001, uncorrected for multiple comparisons. For display purposes, SPM T-maps were presented on coronal or flattened representations of the M12 anatomical template, using xjView toolbox (http://www.alivelearn.net/xjview) and Caret software (version 5.64 http://brainvis.wustl.edu/wiki/index.php/Caret:About), respectively.

    The exact locations and extents of the fMRI-activations were verified on the animal’s own EPI-images. Percent signal change was calculated in regions of interest (ROIs), and statistical significance was tested using MarsBaR (version 0.41.1). We considered a set of 32 ROIs for early visual areas and the ROIs of all brain areas connected to AIP [27], which included premotor, prefrontal, parietal, temporal, and visual ROIs (F5a, F5p, F5c, 45A, 45B, 46v, FEF, AIP, LIP, MIP, CIP, PIP, PFG, STP, OT, PITv, PITd, TE, TEr, FST, MSTv, MT, S2, V1, V2, V3A, V3, V4, V4A, V4T, V6A, V6). Moreover, we also included an additional set of ROIs of frontal areas that are not connected with AIP: F1, F2, F3, F4, F6, and F7. Note that the no-stimulation condition served as the baseline. The significance threshold for one-tailed t-tests was set at p = 0.05, corrected for multiple comparisons (32 t-tests calculated p = 0.05/32 = 0.0016). Standard fMRI analysis methods were used, as described in previous studies [30,52]. All regions of interest were described previously [11,30,62].

    To quantify the similarity between the awake and sedated states and between animals, a Pearson correlation was calculated between the percentage of significant voxels (t-value > 3.1: p < 0.001 uncorrected) per ROI in each state (awake-sedated) or in each animal, across the set of 32 ROIs of all early visual areas and all areas connected to AIP. The significance of the correlations between animals was calculated using a permutation test, in which the 32 calculated percentages of significantly (p < 0.001 uncorrected) activated voxels were randomly assigned (5,000 times) to the 32 ROIs, after which the correlations between corresponding ROIs were calculated. P-values were calculated as the proportion of correlations exceeding the actual correlation between corresponding ROIs. Moreover, to confirm the consistency of the activations across animals and states, a conjunction analysis was performed on the data of all animals (at p < 0.05 uncorrected for each animal).


    Methods

    Animals

    All procedures were performed with approval from The University of Queensland Animal Welfare Unit (in accordance with approval SBMS/305/13/ARC). Zebrafish (Danio rerio) larvae were maintained at 28.5 °C on a 14 hr ON/10 hr OFF light cycle. Adult fish were maintained, fed, and mated as previously described 72 . All experiments were carried out in elavl3:H2B-GCaMP6f larvae 42,73 , kindly provided before publication by Misha Ahrens at the Howard Hughes Medical Institute (HHMI), Janelia Farm Research Campus (Ashburn, VA, USA).

    Imaging tectal activity

    6-day post-fertilization (dpf) larvae of the transgenic strain elavl3:H2B-GCaMP6f were immobilised dorsal side up in 1.5% low melting point agarose (Progen Biosciences, Australia). Larvae were then transferred to custom-made, glass-walled imaging chambers and allowed to acclimate for 20 minutes prior to imaging under 488 nm illumination on a custom-built selective plane illumination microscope (Table S1). Imaging larval zebrafish with 488 nm light using a selective-plane illumination method has been previously shown to reduce or abolish some visual responses 74 , therefore we tuned the intensity of our plane to a level that still permitted robust visual responses. While we cannot rule out a reduction in the sensitivity of our assay, any artefacts arising from direct stimulation of the eye by the plane should be uniform across the dataset, such that they would not be expected to produce spurious results.

    A single tectal hemisphere was imaged at 10 Hz for five consecutive trials at 75 μm below the first visible tectal cell body. This plane was chosen as it was responsive to most visual stimuli in previous experiments 38 . In each trial, larvae were presented with sixteen visual stimuli, delivered at 20 second intervals. The order of stimulus presentation was randomised between fish, but not between experimental trials. Trials were separated by 90 seconds of a blank screen with no visual stimuli.

    The sixteen visual stimuli were presented contralateral to the tectum being imaged on a 7 × 5 cm LCD screen positioned 8 cm from the larva, covering approximately 50 × 35° of the visual field. The primary stimulus presented was a bright, 4° wide vertical bar on a dark background, moving from rostral to caudal across 25° of the visual field at 25°/s. Luminance of the bright bar stimulus on black background was approximately 24 cd/m 2 . This stimulus was adjusted sequentially by either rotating the bar by π/8 radians or by halving the grey-value of the vertical bar to produce a stimulus set containing sixteen stimuli. All stimuli were given such that the centre of the 4° wide bar passed from the caudal to the rostral edge of the 25° visual field in one second. All image acquisition and stimulus presentation was controlled by μManager software 75 .

    Analysis of tectal responses

    Small amounts of XY drift or motion artefacts from each tiff series were reduced by aligning each frame to its preceding frame using the ‘Rigid Body’ transformation in the StackReg plugin 76 in ImageJ (United States National Institutes of Health). Using a custom-written MATLAB code, based on previous work by Panier and colleagues 77 , the tiff series was prepared for segmenting individual somata by first generating a mean intensity projection of the series. Detection of cell outlines was improved by applying a two-dimensional Laplacian of Gaussian filter followed by a morphological tophat transformation and Gaussian lowpass filter, each with a σ of approximately half an average cell diameter (7 pixels). The resulting images were then segmented into individual regions of interest (ROI) using the watershed function in MATLAB. This algorithm finds local minima in image intensity and the continuous peak in intensity surrounding this region is marked as its border. Only regions with an area of between 10 and 200 μm 2 , and with eccentricity of less than 0.9, were classified as cells.

    In order to measure the activity of each neuron identified above, the baseline fluorescence of each ROI was determined by finding the 40 th percentile of its intensity over time (F0) prior to stimulus delivery. The raw intensity values at each time-point (Fi), minus this baseline, were then divided by the baseline fluorescence to yield a ΔF/F for each cell over time:

    Significant neuronal firing was identified using a knowledge-based detection method, similar to that proposed by Patel and colleagues 78 . Specifically, the time-varying correlation coefficient between the fluorescence trace for each cell and an ‘example spike’ was calculated. The example spike was a 6 second trace created by averaging 50 user-defined calcium events. Calcium transients with a correlation coefficient greater than 0.7 to the example spike and a peak ΔF/F greater than 10% above baseline were classified as a neuronal response.

    The proportion of active cells that were common to two given ensembles, relative to the total number of active cells across both assemblies, was determined using the matching index (MI) described by Romano and colleagues 44 . This was used to determine the repeatability of neuronal ensembles of the same functional cluster between trials, and to determine the presence of cells shared between neuronal ensembles belonging to different clusters in the same trial. The MI between two groups of cells was defined as twice the number of cells shared between both ensembles (X) divided by the total number of active cells in both ensembles (K):

    The orientation selectivity of cells was determined by averaging the peak response of the cell across all five trials to the stimulus at each orientation. For all orientations, the mean response (R1) was compared to that for the orthogonal orientation (R2) by the orientation selectivity index (OSI) using the formula:

    The maximum OSI, and the stimulus orientation to which it belongs, was then determined for each cell.


    Review

    1. Identify the three main parts of a neuron and their functions.
    2. Describe the myelin sheath and nodes of Ranvier. How does their arrangement allow nerve impulses to travel very rapidly along axons?
    3. What is a synapse?
    4. Define neurogenesis. What is the potential for neurogenesis in the human brain?
    5. Relate neurons to different types of nervous tissues.
    6. Compare and contrast sensory and motor neurons.
    7. Identify the role of interneurons.
    8. For each type of neuron below, identify whether it is a sensory neuron, motor neuron, or interneuron.
      1. A neuron in the spinal cord receives touch information and then transmits that information to another spinal cord neuron that controls the movement of an arm muscle.
      2. A neuron that takes taste information from your tongue and sends it to your brain.
      3. A spinal cord neuron stimulates a muscle to contract.
      1. Sensory neurons
      2. White neurons
      3. Peripheral nervous system neurons
      4. Glial cells

      Neuron Classification

      Stocktrek Images / Getty Images

      There are three main categories of neurons. They are multipolar, unipolar, and bipolar neurons.

      • Multipolar neurons are found in the central nervous system and are the most common of the neuron types. These neurons have a single axon ​and many dendrites extending from the cell body.​​
      • Unipolar neurons have one very short process that extends from a single cell body and branches into two processes. Unipolar neurons are found in spinal nerve cell bodies and cranial nerves.
      • Bipolar neurons are sensory neurons consisting of one axon and one dendrite that extend from the cell body. They are found in retinal cells and olfactory epithelium.

      Neurons are classified as either motor, sensory, or interneurons. Motor neurons carry information from the central nervous system to organs, glands, and muscles. Sensory neurons send information to the central nervous system from internal organs or from external stimuli. Interneurons relay signals between ​motor and sensory neurons.​