Cortical interneurons are classified into several subtypes that contribute to cortical oscillatory activity. Parvalbumin (PV)-expressing cells, a type of inhibitory interneuron, are involved in the gamma oscillations of local field potentials (LFPs). Under ketamine-xylazine anesthesia or sleep, mammalian cortical circuits exhibit slow oscillations in which the active-up state and silent-down state alternate at ~1 Hz. The up state is composed of various high-frequency oscillations, including gamma oscillations. However, it is unclear how PV cells and somatostatin (SOM) cells contribute to the slow oscillations and the high-frequency oscillations nested in the up state. To address these questions, we used mice lacking glutamate decarboxylase 67, primarily in PV cells (PV-GAD67 mice) or in SOM cells (SOM-GAD67 mice). We then compared LFPs between PV-GAD67 mice and SOM-GAD67 mice. PV cells target the proximal regions of pyramidal cells, whereas SOM cells are dendrite-preferring interneurons. We found that the up state was shortened in duration in the PV-GAD67 mice, but tended to be longer in SOM-GAD67 mice. Firing rate tended to increase in PV-GAD67 mice, but tended to decrease in SOM-GAD67 mice. We also found that delta oscillations tended to increase in SOM-GAD67 mice, but tended to decrease in PV-GAD67 mice. Current source density and wavelet analyses were performed to determine the depth profiles of various high-frequency oscillations. High gamma and ripple (60–200 Hz) power decreased in the neocortical upper layers specifically in PV-GAD67 mice, but not in SOM-GAD67. In addition, beta power (15–30 Hz) increased in the deep layers, specifically in PV-GAD67 mice. These results suggest that PV cells play important roles in persistence of the up state and in the balance between gamma and beta bands across cortical layers, whereas SOM and PV cells may make an asymmetric contribution to regulate up-state and delta oscillations.
PV cells; slow oscillation; gamma oscillation; beta oscillation; neocortex; CSD; wavelet analysis; mouse
The exponential increase in available neural data has combined with the exponential growth in computing (“Moore's law”) to create new opportunities to understand neural systems at large scale and high detail. The ability to produce large and sophisticated simulations has introduced unique challenges to neuroscientists. Computational models in neuroscience are increasingly broad efforts, often involving the collaboration of experts in different domains. Furthermore, the size and detail of models have grown to levels for which understanding the implications of variability and assumptions is no longer trivial. Here, we introduce the model design platform N2A which aims to facilitate the design and validation of biologically realistic models. N2A uses a hierarchical representation of neural information to enable the integration of models from different users. N2A streamlines computational validation of a model by natively implementing standard tools in sensitivity analysis and uncertainty quantification. The part-relationship representation allows both network-level analysis and dynamical simulations. We will demonstrate how N2A can be used in a range of examples, including a simple Hodgkin-Huxley cable model, basic parameter sensitivity of an 80/20 network, and the expression of the structural plasticity of a growing dendrite and stem cell proliferation and differentiation.
neuroinformatics; computational modeling; computational neuroscience; structural plasticity; biologically realistic modeling
Interictal spikes (IISs) are spontaneous high amplitude, short time duration <400 ms events often observed in electroencephalographs (EEG) of epileptic patients. In vitro analysis of resected mesial temporal lobe tissue from patients with refractory temporal lobe epilepsy has revealed the presence of IIS in the CA1 subfield. In this paper, we develop a biophysically relevant network model of the CA1 subfield and investigate how changes in the network properties influence the susceptibility of CA1 to exhibit an IIS. We present a novel template based approach to identify conditions under which synchronization of paroxysmal depolarization shift (PDS) events evoked in CA1 pyramidal (Py) cells can trigger an IIS. The results from this analysis are used to identify the synaptic parameters of a minimal network model that is capable of generating PDS in response to afferent synaptic input. The minimal network model parameters are then incorporated into a detailed network model of the CA1 subfield in order to address the following questions: (1) How does the formation of an IIS in the CA1 depend on the degree of sprouting (recurrent connections) between the CA1 Py cells and the fraction of CA3 Shaffer collateral (SC) connections onto the CA1 Py cells? and (2) Is synchronous afferent input from the SC essential for the CA1 to exhibit IIS? Our results suggest that the CA1 subfield with low recurrent connectivity (absence of sprouting), mimicking the topology of a normal brain, has a very low probability of producing an IIS except when a large fraction of CA1 neurons (>80%) receives a barrage of quasi-synchronous afferent input (input occurring within a temporal window of ≤24 ms) via the SC. However, as we increase the recurrent connectivity of the CA1 (Psprout > 40); mimicking sprouting in a pathological CA1 network, the CA1 can exhibit IIS even in the absence of a barrage of quasi-synchronous afferents from the SC (input occurring within temporal window >80 ms) and a low fraction of CA1 Py cells (≈30%) receiving SC input. Furthermore, we find that in the presence of Poisson distributed random input via SC, the CA1 network is able to generate spontaneous periodic IISs (≈3 Hz) for high degrees of recurrent Py connectivity (Psprout > 70). We investigate the conditions necessary for this phenomenon and find that spontaneous IISs closely depend on the degree of the network's intrinsic excitability.
interictal spikes; hippocampal CA1 region; computational models; paroxysmal depolarization shift; temporal lobe epilepsy
Diabetic polyneuropathy (DPN) presents as a wide variety of sensorimotor symptoms and affects approximately 50% of diabetic patients. Changes in the neural circuits may occur in the early stages in diabetes and are implicated in the development of DPN. Therefore, we aimed to detect changes in the expression of isolectin B4 (IB4, the marker for nonpeptidergic unmyelinated fibers and their cell bodies) and calcitonin gene-related peptide (CGRP, the marker for peptidergic fibers and their cell bodies) in the dorsal root ganglion (DRG) and spinal cord of streptozotocin (STZ)-induced type 1 diabetic rats showing alterations in sensory and motor function. We also used cholera toxin B subunit (CTB) to show the morphological changes of the myelinated fibers and motor neurons. STZ-induced diabetic rats exhibited hyperglycemia, decreased body weight gain, mechanical allodynia and impaired locomotor activity. In the DRG and spinal dorsal horn, IB4-labeled structures decreased, but both CGRP immunostaining and CTB labeling increased from day 14 to day 28 in diabetic rats. In spinal ventral horn, CTB labeling decreased in motor neurons in diabetic rats. Treatment with intrathecal injection of insulin at the early stages of DPN could alleviate mechanical allodynia and impaired locomotor activity in diabetic rats. The results suggest that the alterations of the neural circuits between spinal nerve and spinal cord via the DRG and ventral root might be involved in DPN.
diabetic polyneuropathy; neural circuit; spinal cord; dorsal root ganglion; primary afferent; rat
Neocortical network activity is generated through a dynamic balance between excitation, provided by pyramidal neurons, and inhibition, provided by interneurons. Imbalance of the excitation/inhibition ratio has been identified in several neuropsychiatric diseases, such as schizophrenia, autism and epilepsy, which also present with other cognitive deficits and symptoms associated with prefrontal cortical (PFC) dysfunction. We undertook a computational approach to study how changes in the excitation/inhibition balance in a PFC microcircuit model affect the properties of persistent activity, considered the cellular correlate of working memory function in PFC. To this end, we constructed a PFC microcircuit, consisting of pyramidal neuron models and all three different interneuron types: fast-spiking (FS), regular-spiking (RS), and irregular-spiking (IS) interneurons. Persistent activity was induced in the microcircuit model with a stimulus to the proximal apical dendrites of the pyramidal neuron models, and its properties were analyzed, such as the induction profile, the interspike intervals (ISIs) and neuronal synchronicity. Our simulations showed that (a) the induction but not the firing frequency or neuronal synchronicity is modulated by changes in the NMDA-to-AMPA ratio on FS interneuron model, (b) removing or decreasing the FS model input to the pyramidal neuron models greatly limited the biophysical modulation of persistent activity induction, decreased the ISIs and neuronal synchronicity during persistent activity, (c) the induction and firing properties could not be altered by the addition of other inhibitory inputs to the soma (from RS or IS models), and (d) the synchronicity change could be reversed by the addition of other inhibitory inputs to the soma, but beyond the levels of the control network. Thus, generic somatic inhibition acts as a pacemaker of persistent activity and FS specific inhibition modulates the output of the pacemaker.
prefrontal cortex; NMDA; synchronicity; fast-spiking interneurons; connectivity; parvalbumin interneurons
The diversity of GABAA receptor (GABAAR) subunits and the numerous configurations during subunit assembly give rise to a variety of receptors with different functional properties. This heterogeneity results in variations in GABAergic conductances across numerous brain regions and cell types. Phasic inhibition is mediated by synaptically-localized receptors with a low affinity for GABA and results in a transient, rapidly desensitizing GABAergic conductance; whereas, tonic inhibition is mediated by extrasynaptic receptors with a high affinity for GABA and results in a persistent GABAergic conductance. The specific functions of tonic versus phasic GABAergic inhibition in different cell types and the impact on specific neural circuits are only beginning to be unraveled. Here we review the diversity in the magnitude of tonic GABAergic inhibition in various brain regions and cell types, and highlight the impact on neuronal excitability in different neuronal circuits. Further, we discuss the relevance of tonic inhibition in various physiological and pathological contexts as well as the potential of targeting these receptor subtypes for treatment of diseases, such as epilepsy.
tonic inhibition; GABA; neurosteroids; neuronal excitability; epilepsy
Here we propose a methodology to analyze volumetric electrical activity of neuronal masses in the somatosensory barrel field of Wistar rats. The key elements of the proposed methodology are a three-dimensional microelectrode array, which was customized by our group to observe extracellular recordings from an extended area of the barrel field, and a novel method for the current source density analysis. By means of this methodology, we were able to localize single barrels from their event-related responses to single whisker deflection. It was also possible to assess the spatiotemporal dynamics of neuronal aggregates in several barrels at the same time with the resolution of single neurons. We used simulations to study the robustness of our methodology to unavoidable physiological noise and electrode configuration. We compared the accuracy to reconstruct neocortical current sources with that obtained with a previous method. This constitutes a type of electrophysiological microscopy with high spatial and temporal resolution, which could change the way we analyze the activity of cortical neurons in the future.
CSD; LFPs; brain current sources; neuronal activity; cerebral cortex; barrel field
Olfactory sensory information passes through several processing stages before an odor percept emerges. The question how the olfactory system learns to create odor representations linking those different levels and how it learns to connect and discriminate between them is largely unresolved. We present a large-scale network model with single and multi-compartmental Hodgkin–Huxley type model neurons representing olfactory receptor neurons (ORNs) in the epithelium, periglomerular cells, mitral/tufted cells and granule cells in the olfactory bulb (OB), and three types of cortical cells in the piriform cortex (PC). Odor patterns are calculated based on affinities between ORNs and odor stimuli derived from physico-chemical descriptors of behaviorally relevant real-world odorants. The properties of ORNs were tuned to show saturated response curves with increasing concentration as seen in experiments. On the level of the OB we explored the possibility of using a fuzzy concentration interval code, which was implemented through dendro-dendritic inhibition leading to winner-take-all like dynamics between mitral/tufted cells belonging to the same glomerulus. The connectivity from mitral/tufted cells to PC neurons was self-organized from a mutual information measure and by using a competitive Hebbian–Bayesian learning algorithm based on the response patterns of mitral/tufted cells to different odors yielding a distributed feed-forward projection to the PC. The PC was implemented as a modular attractor network with a recurrent connectivity that was likewise organized through Hebbian–Bayesian learning. We demonstrate the functionality of the model in a one-sniff-learning and recognition task on a set of 50 odorants. Furthermore, we study its robustness against noise on the receptor level and its ability to perform concentration invariant odor recognition. Moreover, we investigate the pattern completion capabilities of the system and rivalry dynamics for odor mixtures.
pattern recognition; olfactory bulb; piriform cortex; large-scale neuromorphic systems; spiking neural network; BCPNN; concentration invariance; pattern rivalry
Compared with connections between the retinae and primary visual centers, relatively less is known in both mammals and insects about the functional segregation of neural pathways connecting primary and higher centers of the visual processing cascade. Here, using the Drosophila visual system as a model, we demonstrate two levels of parallel computation in the pathways that connect primary visual centers of the optic lobe to computational circuits embedded within deeper centers in the central brain. We show that a seemingly simple achromatic behavior, namely phototaxis, is under the control of several independent pathways, each of which is responsible for navigation towards unique wavelengths. Silencing just one pathway is enough to disturb phototaxis towards one characteristic monochromatic source, whereas phototactic behavior towards white light is not affected. The response spectrum of each demonstrable pathway is different from that of individual photoreceptors, suggesting subtractive computations. A choice assay between two colors showed that these pathways are responsible for navigation towards, but not for the detection itself of, the monochromatic light. The present study provides novel insights about how visual information is separated and processed in parallel to achieve robust control of an innate behavior.
Drosophila; phototaxis; wavelength-dependent; color vision; higher visual center
Mammalian brains span about four orders of magnitude in cortical volume and have to operate in different environments that require diverse behavioral skills. Despite these geometric and behavioral diversities, the examination of cerebral cortex across species reveals that it contains a substantial number of conserved characteristics that are associated with neuroanatomy and metabolism, i.e., with neuronal connectivity and function. Some of these cortical constants or invariants have been known for a long time but not sufficiently appreciated, and others were only recently discovered. The focus of this review is to present the cortical invariants and discuss their role in the efficient information processing. Global conservation in neuroanatomy and metabolism, as well as their correlated regional and developmental variability suggest that these two parallel systems are mutually coupled. It is argued that energetic constraint on cortical organization can be strong if cerebral blood supplied is either below or above a certain level, and it is rather soft otherwise. Moreover, because maximization or minimization of parameters associated with cortical connectivity, function and cost often leads to conflicts in design, it is argued that the architecture of the cerebral cortex is a result of structural and functional compromises.
cerebral cortex; conservation; connectivity; metabolism; capillary; constraints; allometry; evolutionary design
The characterization of functional network structures among multiple neurons is essential to understanding neural information processing. Information geometry (IG), a theory developed for investigating a space of probability distributions has recently been applied to spike-train analysis and has provided robust estimations of neural interactions. Although neural firing in the equilibrium state is often assumed in these studies, in reality, neural activity is non-stationary. The brain exhibits various oscillations depending on cognitive demands or when an animal is asleep. Therefore, the investigation of the IG measures during oscillatory network states is important for testing how the IG method can be applied to real neural data. Using model networks of binary neurons or more realistic spiking neurons, we studied how the single- and pairwise-IG measures were influenced by oscillatory neural activity. Two general oscillatory mechanisms, externally driven oscillations and internally induced oscillations, were considered. In both mechanisms, we found that the single-IG measure was linearly related to the magnitude of the external input, and that the pairwise-IG measure was linearly related to the sum of connection strengths between two neurons. We also observed that the pairwise-IG measure was not dependent on the oscillation frequency. These results are consistent with the previous findings that were obtained under the equilibrium conditions. Therefore, we demonstrate that the IG method provides useful insights into neural interactions under the oscillatory condition that can often be observed in the real brain.
information geometry; spikes; spiking neuron model; oscillation; neural networks
Many neural systems can store short-term information in persistently firing neurons. Such persistent activity is believed to be maintained by recurrent feedback among neurons. This hypothesis has been fleshed out in detail for the oculomotor integrator (OI) for which the so-called “line attractor” network model can explain a large set of observations. Here we show that there is a plethora of such models, distinguished by the relative strength of recurrent excitation and inhibition. In each model, the firing rates of the neurons relax toward the persistent activity states. The dynamics of relaxation can be quite different, however, and depend on the levels of recurrent excitation and inhibition. To identify the correct model, we directly measure these relaxation dynamics by performing optogenetic perturbations in the OI of zebrafish expressing halorhodopsin or channelrhodopsin. We show that instantaneous, inhibitory stimulations of the OI lead to persistent, centripetal eye position changes ipsilateral to the stimulation. Excitatory stimulations similarly cause centripetal eye position changes, yet only contralateral to the stimulation. These results show that the dynamics of the OI are organized around a central attractor state—the null position of the eyes—which stabilizes the system against random perturbations. Our results pose new constraints on the circuit connectivity of the system and provide new insights into the mechanisms underlying persistent activity.
neural integrator; optogenetics; model; zebrafish; oculomotor system; network dynamics
The primary motor cortex (M1) is involved in fine voluntary movements control. Previous studies have shown the existence of a dopamine (DA) innervation in M1 of rats and monkeys that could directly modulate M1 neuronal activity. However, none of these studies have described the precise distribution of DA terminals within M1 functional region nor have quantified the density of this innervation. Moreover, the precise role of DA on pyramidal neuron activity still remains unclear due to conflicting results from previous studies regarding D2 effects on M1 pyramidal neurons. In this study we assessed in mice the neuroanatomical characteristics of DA innervation in M1 using unbiased stereological quantification of DA transporter-immunostained fibers. We demonstrated for the first time in mice that DA innervates the deep layers of M1 targeting preferentially the forelimb representation area of M1. To address the functional role of the DA innervation on M1 neuronal activity, we performed electrophysiological recordings of single neurons activity in vivo and pharmacologically modulated D2 receptor activity. Local D2 receptor activation by quinpirole enhanced pyramidal neuron spike firing rate without changes in spike firing pattern. Altogether, these results indicate that DA innervation in M1 can increase neuronal activity through D2 receptor activation and suggest a potential contribution to the modulation of fine forelimb movement. Given the demonstrated role for DA in fine motor skill learning in M1, our results suggest that altered D2 modulation of M1 activity may be involved in the pathophysiology of movement disorders associated with disturbed DA homeostasis.
motor cortex; dopamine; mice; unbiased stereology; in vivo electrophysiology
Models of networks of Leaky Integrate-and-Fire (LIF) neurons are a widely used tool for theoretical investigations of brain function. These models have been used both with current- and conductance-based synapses. However, the differences in the dynamics expressed by these two approaches have been so far mainly studied at the single neuron level. To investigate how these synaptic models affect network activity, we compared the single neuron and neural population dynamics of conductance-based networks (COBNs) and current-based networks (CUBNs) of LIF neurons. These networks were endowed with sparse excitatory and inhibitory recurrent connections, and were tested in conditions including both low- and high-conductance states. We developed a novel procedure to obtain comparable networks by properly tuning the synaptic parameters not shared by the models. The so defined comparable networks displayed an excellent and robust match of first order statistics (average single neuron firing rates and average frequency spectrum of network activity). However, these comparable networks showed profound differences in the second order statistics of neural population interactions and in the modulation of these properties by external inputs. The correlation between inhibitory and excitatory synaptic currents and the cross-neuron correlation between synaptic inputs, membrane potentials and spike trains were stronger and more stimulus-modulated in the COBN. Because of these properties, the spike train correlation carried more information about the strength of the input in the COBN, although the firing rates were equally informative in both network models. Moreover, the network activity of COBN showed stronger synchronization in the gamma band, and spectral information about the input higher and spread over a broader range of frequencies. These results suggest that the second order statistics of network dynamics depend strongly on the choice of synaptic model.
recurrent neural network; integrate-and-fire neurons; current based neuron models; conductance based neuron models; spike correlation; local field potentials; correlation analysis; information encoding
Short-term plasticity plays a key role in synaptic transmission and has been extensively investigated for excitatory synapses. Much less is known about inhibitory synapses. Here we analyze the performance of glycinergic connections between the medial nucleus of the trapezoid body (MNTB) and the lateral superior olive (LSO) in the auditory brainstem, where high spike rates as well as fast and precise neurotransmission are hallmarks. Analysis was performed in acute mouse slices shortly after hearing onset (postnatal day (P)11) and 8 days later (P19). Stimulation was done at 37°C with 1–400 Hz for 40 s. Moreover, in a novel approach named marathon experiments, a very prolonged stimulation protocol was employed, comprising 10 trials of 1-min challenge and 1-min recovery periods at 50 and 1 Hz, respectively, thus lasting up to 20 min and amounting to >30,000 stimulus pulses. IPSC peak amplitudes displayed short-term depression (STD) and synaptic attenuation in a frequency-dependent manner. No facilitation was observed. STD in the MNTB-LSO connections was less pronounced than reported in the upstream calyx of Held-MNTB connections. At P11, the STD level and the failure rate were slightly lower within the ms-to-s range than at P19. During prolonged stimulation periods lasting 40 s, P19 connections sustained virtually failure-free transmission up to frequencies of 100 Hz, whereas P11 connections did so only up to 50 Hz. In marathon experiments, P11 synapses recuperated reproducibly from synaptic attenuation during all recovery periods, demonstrating a robust synaptic machinery at hearing onset. At 26°C, transmission was severely impaired and comprised abnormally high amplitudes after minutes of silence, indicative of imprecisely regulated vesicle pools. Our study takes a fresh look at synaptic plasticity and stability by extending conventional stimulus periods in the ms-to-s range to minutes. It also provides a framework for future analyses of synaptic plasticity.
synaptic fidelity; fast-spiking cells; short-term depression; inhibitory postsynaptic currents; synaptic attenuation; lateral superior olive; medial nucleus of the trapezoid body; high-frequency neurotransmission
Neuronal responses and topographic organization of feature selectivity in the cerebral cortex are shaped by ascending inputs and by intracortical connectivity. The mammalian primary auditory cortex has a tonotopic arrangement at large spatial scales (greater than 300 microns). This large-scale architecture breaks down in supragranular layers at smaller scales (around 300 microns), where nearby frequency and sound level tuning properties can be quite heterogeneous. Since layer 4 has a more homogeneous architecture, the heterogeneity in supragranular layers might be caused by heterogeneous ascending input or via heterogeneous intralaminar connections. Here we measure the functional 2-dimensional spatial connectivity pattern of the supragranular auditory cortex on micro-column scales. In general connection probability decreases with radial distance from each neuron, but the decrease is steeper in the isofrequency axis leading to an anisotropic distribution of connection probability with respect to the tonotopic axis. In addition to this radial decrease in connection probability we find a patchy organization of inhibitory and excitatory synaptic inputs that is also anisotropic with respect to the tonotopic axis. These periodicities are at spatial scales of ~100 and ~300 μm. While these spatial periodicities show anisotropy in auditory cortex, they are isotropic in visual cortex, indicating region specific differences in intralaminar connections. Together our results show that layer 2/3 neurons in auditory cortex show specific spatial intralaminar connectivity despite the overtly heterogeneous tuning properties.
auditory cortex; microcircuit; intracortical; excitation; inhibition; mouse
A unique delayed self-inhibitory pathway mediated by layer 5 Martinotti Cells was studied in a biologically inspired neural network simulation. Inclusion of this pathway along with layer 5 basket cell lateral inhibition caused balanced competitive learning, which led to the formation of neuronal clusters as were indeed reported in the same region. Martinotti pathway proves to act as a learning “conscience,” causing overly successful regions in the network to restrict themselves and let others fire. It thus spreads connectivity more evenly throughout the net and solves the “dead unit” problem of clustering algorithms in a local and biologically plausible manner.
competitive learning; layer 5 pyramidal cells; STDP learning; brain circuitry development; dead units problem
Acetylcholine (ACh) release in the medial prefrontal cortex (mPFC) is crucial for normal cognitive performance. Despite the fact that many have studied how ACh affects neuronal processing in the mPFC and thereby influences attention behavior, there is still a lot unknown about how this occurs. Here we will review the evidence that cholinergic modulation of the mPFC plays a role in attention and we will summarize the current knowledge about the role between ACh receptors (AChRs) and behavior and how ACh receptor activation changes processing in the cortical microcircuitry. Recent evidence implicates fast phasic release of ACh in cue detection and attention. This review will focus mainly on the fast ionotropic nicotinic receptors and less on the metabotropic muscarinic receptors. Finally, we will review limitations of the existing studies and address how innovative technologies might push the field forward in order to gain understanding into the relation between ACh, neuronal activity and behavior.
acetylcholine; nicotinic receptors; medial prefrontal cortex; attention; neurophysiology
Although many computational models have been proposed to explain orientation maps in primary visual cortex (V1), it is not yet known how similar clusters of color-selective neurons in macaque V1/V2 are connected and develop. In this work, we address the problem of understanding the cortical processing of color information with a possible mechanism of the development of the patchy distribution of color selectivity via computational modeling. Each color input is decomposed into a red, green, and blue representation and transmitted to the visual cortex via a simulated optic nerve in a luminance channel and red–green and blue–yellow opponent color channels. Our model of the early visual system consists of multiple topographically-arranged layers of excitatory and inhibitory neurons, with sparse intra-layer connectivity and feed-forward connectivity between layers. Layers are arranged based on anatomy of early visual pathways, and include a retina, lateral geniculate nucleus, and layered neocortex. Each neuron in the V1 output layer makes synaptic connections to neighboring neurons and receives the three types of signals in the different channels from the corresponding photoreceptor position. Synaptic weights are randomized and learned using spike-timing-dependent plasticity (STDP). After training with natural images, the neurons display heightened sensitivity to specific colors. Information-theoretic analysis reveals mutual information between particular stimuli and responses, and that the information reaches a maximum with fewer neurons in the higher layers, indicating that estimations of the input colors can be done using the output of fewer cells in the later stages of cortical processing. In addition, cells with similar color receptive fields form clusters. Analysis of spiking activity reveals increased firing synchrony between neurons when particular color inputs are presented or removed (ON-cell/OFF-cell).
brain modeling; visual cortex; neocortex; color; color selectivity; self-organizing color maps; self-organizing feature maps; STDP
For all neurons, a proper balance of synaptic excitation and inhibition is crucial to effect computational precision. Achievement of this balance is remarkable when one considers factors that modulate synaptic strength operate on multiple overlapping time scales and affect both pre- and postsynaptic elements. Recent studies have shown that inhibitory transmitters, glycine and GABA, are co-released in auditory nuclei involved in the computation of interaural time disparities (ITDs), a cue used to process sound source location. The co-release expressed at these synapses is heavily activity dependent, and generally occurs when input rates are high. This circuitry, in both birds and mammals, relies on inhibitory input to maintain the temporal precision necessary for ITD encoding. Studies of co-release in other brain regions suggest that GABA and glycine receptors (GlyRs) interact via cross-suppressive modulation of receptor conductance. We performed in vitro whole-cell recordings in several nuclei of the chicken brainstem auditory circuit to assess whether this cross-suppressive phenomenon was evident in the avian brainstem. We evaluated the effect of pressure-puff applied glycine on synaptically evoked inhibitory currents in nucleus magnocellularis (NM) and the superior olivary nucleus (SON). Glycine pre-application reduced the amplitude of inhibitory postsynaptic currents (IPSCs) evoked during a 100 Hz train stimulus in both nuclei. This apparent glycinergic modulation was blocked in the presence of strychnine. Further experiments showed that this modulation did not depend on postsynaptic biochemical interactions such as phosphatase activity, or direct interactions between GABA and GlyR proteins. Rather, voltage clamp experiments in which we manipulated Cl− flux during agonist application suggest that activation of one receptor will modulate the conductance of the other via local changes in Cl− ion concentration within microdomains of the postsynaptic membrane.
glycine; GABA; inhibition; cross-suppression; interaural time disparities
The neurotransmitter serotonin (5-HT) has a multifaceted function in the modulation of information processing through the activation of multiple receptor families, including G-protein-coupled receptor subtypes (5-HT1, 5-HT2, 5-HT4–7) and ligand-gated ion channels (5-HT3). The largest population of serotonergic neurons is located in the midbrain, specifically in the raphe nuclei. Although the medial and dorsal raphe nucleus (DRN) share common projecting areas, in the basal ganglia (BG) nuclei serotonergic innervations come mainly from the DRN. The BG are a highly organized network of subcortical nuclei composed of the striatum (caudate and putamen), subthalamic nucleus (STN), internal and external globus pallidus (or entopeduncular nucleus in rodents, GPi/EP and GPe) and substantia nigra (pars compacta, SNc, and pars reticulata, SNr). The BG are part of the cortico-BG-thalamic circuits, which play a role in many functions like motor control, emotion, and cognition and are critically involved in diseases such as Parkinson's disease (PD). This review provides an overview of serotonergic modulation of the BG at the functional level and a discussion of how this interaction may be relevant to treating PD and the motor complications induced by chronic treatment with L-DOPA.
5-HT; basal ganglia; electrophysiology; Parkinson's disease; L-DOPA induced dyskinesia
Bamboo sharks (Chiloscyllium griseum) were tested for their ability to perceive subjective and illusionary contours as well as line length illusions. Individuals were first trained to differentiate between squares, triangles, and rhomboids in a series of two alternative forced-choice experiments. Transfer tests then elucidated whether Kanizsa squares and triangles, grating gaps and phase shifted abutting gratings were also perceived and distinguished. The visual systems of most vertebrates and even invertebrates perceive illusionary contours despite the absence of physical luminance, color or textural differences. Sharks are no exception to the rule; all tasks were successfully mastered within 3–24 training sessions, with sharks discriminating between various sets of Kanizsa figures and alternative stimuli, as well as between subjective contours in >75% of all tests. However, in contrast to Kanizsa figures and subjective contours, sharks were not deceived by Müller-Lyer (ML) illusions. Here, two center lines of equal length are comparatively set between two arrowheads or –tails, in which case the line featuring the two arrow tails appears to be longer to most humans, primates and birds. In preparation for this experiment, lines of varying length, and lines of unequal length randomly featuring either two arrowheads or -tails on their ends, were presented first. Both sets of lines were successfully distinguished by most sharks. However, during presentation of the ML illusions sharks failed to succeed and succumbed either to side preferences or chose according to chance.
optical illusion; Kanizsa; subjective contour; Müller-Lyer deception; elasmobranch; Chiloscyllium griseum
The activation of the ventral tegmental area (VTA) can rebuild the tonotopic representation in the primary auditory cortex (A1), but the cellular mechanisms remain largely unknown. Here, we investigated the firing patterns and membrane potential dynamics of neurons in A1 under the influence of VTA activation using in vivo intracellular recording. We found that VTA activation can significantly reduce the variability of sound evoked responses and promote the firing precision and strength of A1 neurons. Furthermore, the compressed response window was caused by an early hyperpolarization as a result of enhanced circuit inhibition. Our study suggested a possible mechanism of how the reward system affects information processing in sensory cortex: VTA activation strengthens cortical inhibition, which shortens the response window of post-synaptic cortical neurons and further promotes the precision and strength of neuronal activity.
VTA; auditory cortex; intracellular; in vivo; inhibition
Patterns of synaptic connectivity in various regions of the brain are characterized by the presence of synaptic motifs, defined as unidirectional and bidirectional synaptic contacts that follow a particular configuration and link together small groups of neurons. Recent computational work proposes that a relay network (two populations communicating via a third, relay population of neurons) can generate precise patterns of neural synchronization. Here, we employ two distinct models of neuronal dynamics and show that simulated neural circuits designed in this way are caught in a global attractor of activity that prevents neurons from modulating their response on the basis of incoming stimuli. To circumvent the emergence of a fixed global attractor, we propose a mechanism of selective gain inhibition that promotes flexible responses to external stimuli. We suggest that local neuronal circuits may employ this mechanism to generate precise patterns of neural synchronization whose transient nature delimits the occurrence of a brief stimulus.
computer simulations; attractor; synchronization; oscillations; spiking neurons; mean field
Understanding how an organism's nervous system transforms sensory input into behavioral outputs requires recording and manipulating its neural activity during unrestrained behavior. Here we present an instrument to simultaneously monitor and manipulate neural activity while observing behavior in a freely moving animal, the nematode Caenorhabditis elegans. Neural activity is recorded optically from cells expressing a calcium indicator, GCaMP3. Neural activity is manipulated optically by illuminating targeted neurons expressing the optogenetic protein Channelrhodopsin. Real-time computer vision software tracks the animal's behavior and identifies the location of targeted neurons in the nematode as it crawls. Patterned illumination from a DMD is used to selectively illuminate subsets of neurons for either calcium imaging or optogenetic stimulation. Real-time computer vision software constantly updates the illumination pattern in response to the worm's movement and thereby allows for independent optical recording or activation of different neurons in the worm as it moves freely. We use the instrument to directly observe the relationship between sensory neuron activation, interneuron dynamics and locomotion in the worm's mechanosensory circuit. We record and compare calcium transients in the backward locomotion command interneurons AVA, in response to optical activation of the anterior mechanosensory neurons ALM, AVM or both.
optogenetics; calcium imaging; sensorimotor transformation; mechanosensation; behavior