Coding of envelope modulation in the auditory nerve and anteroventral cochlear nucleus.

We have investigated responses of the auditory nerve fibres (ANFS) and anteroventral cochlear nucleus (AVCN) units to narrowband 'single-formant' stimuli (SFSS). We found that low and medium spontaneous rate (SR) ANFS maintain greater amplitude modulation (AM) in their responses at high sound levels than do high SR units when sound level is considered in dB SPL. However, this partitioning of high and low SR units disappears if sound level is considered in dB relative to unit threshold. Stimuli with carrier frequencies away from unit best frequency (BF) were found to generate higher AM in responses at high sound levels than that observed even in most low and medium SR units for stimuli with carrier frequencies near BF. AVCN units were shown to have increased modulation depth in their responses when compared with high SR ANFS with similar BFS and to have increased or comparable modulation depth when compared with low SR ANFS. At sound levels where AM almost completely disappears in high SR ANFS, most AVCN units we studied still show significant AM in their responses. Using a dendritic model, we investigated possible mechanisms of enhanced AM in AVCN units, including the convergence of inputs from different SR groups of ANFS and a postsynaptic threshold mechanism in the soma.     

Morphological Characterization of Bushy Cells and Their Inputs in the Laboratory Mouse (Mus musculus) Anteroventral Cochlear Nucleus

Spherical and globular bushy cells of the AVCN receive huge auditory nerve endings specialized for high fidelity neural transmission in response to acoustic events. Recent studies in mice and other rodent species suggest that the distinction between bushy cell subtypes is not always straightforward. We conducted a systematic investigation of mouse bushy cells along the rostral-caudal axis in an effort to understand the morphological variation that gives rise to reported response properties in mice. We combined quantitative light and electron microscopy to investigate variations in cell morphology, immunostaining, and the distribution of primary and non-primary synaptic inputs along the rostral-caudal axis. Overall, large regional differences in bushy cell characteristics were not found; however, rostral bushy cells received a different complement of axosomatic input compared to caudal bushy cells. The percentage of primary auditory nerve terminals was larger in caudal AVCN, whereas non-primary excitatory and inhibitory inputs were more common in rostral AVCN. Other ultrastructural characteristics of primary auditory nerve inputs were similar across the rostral and caudal AVCN. Cross sectional area, postsynaptic density length and curvature, and mitochondrial volume fraction were similar for axosomatic auditory nerve terminals, although rostral auditory nerve terminals contained a greater concentration of synaptic vesicles near the postsynaptic densities. These data demonstrate regional differences in synaptic organization of inputs to mouse bushy cells rather than the morphological characteristic of the cells themselves.     

Modelling the sensitivity of cells in the anteroventral cochlear nucleus to spatiotemporal discharge patterns.

This study investigates a potential mechanism for the processing of acoustic information that is encoded in the spatiotemporal discharge patterns of auditory nerve (AN) fibres. Recent physiological evidence has demonstrated that some low-frequency cells in the anteroventral cochlear nucleus (AVCN) are sensitive to manipulations of the phase spectrum of complex sounds (Carney 1990b). These manipulations result in systematic changes in the spatiotemporal discharge patterns across groups of low-frequency AN fibres having different characteristic frequencies (CFS). One interpretation of these results is that these neurons in the AVCN receive convergent inputs from AN fibres with different CFS, and that the cells perform a coincidence detection or cross-correlation upon their inputs. This report presents a model that was developed to test this interpretation.     

Slope-Based Stochastic Resonance: How Noise Enables Phasic Neurons to Encode Slow Signals

Fundamental properties of phasic firing neurons are usually characterized in a noise-free condition. In the absence of noise, phasic neurons exhibit Class 3 excitability, which is a lack of repetitive firing to steady current injections. For time-varying inputs, phasic neurons are band-pass filters or slope detectors, because they do not respond to inputs containing exclusively low frequencies or shallow slopes. However, we show that in noisy conditions, response properties of phasic neuron models are distinctly altered. Noise enables a phasic model to encode low-frequency inputs that are outside of the response range of the associated deterministic model. Interestingly, this seemingly stochastic-resonance (SR) like effect differs significantly from the classical SR behavior of spiking systems in both the signal-to-noise ratio and the temporal response pattern. Instead of being most sensitive to the peak of a subthreshold signal, as is typical in a classical SR system, phasic models are most sensitive to the signal''s rising and falling phases where the slopes are steep. This finding is consistent with the fact that there is not an absolute input threshold in terms of amplitude; rather, a response threshold is more properly defined as a stimulus slope/frequency. We call the encoding of low-frequency signals with noise by phasic models a slope-based SR, because noise can lower or diminish the slope threshold for ramp stimuli. We demonstrate here similar behaviors in three mechanistic models with Class 3 excitability in the presence of slow-varying noise and we suggest that the slope-based SR is a fundamental behavior associated with general phasic properties rather than with a particular biological mechanism.     

Gamma oscillations in human primary somatosensory cortex reflect pain perception

Successful behavior requires selection and preferred processing of relevant sensory information. The cortical representation of relevant sensory information has been related to neuronal oscillations in the gamma frequency band. Pain is of invariably high behavioral relevance and, thus, nociceptive stimuli receive preferred processing. Here, by using magnetoencephalography, we show that selective nociceptive stimuli induce gamma oscillations between 60 and 95 Hz in primary somatosensory cortex. Amplitudes of pain-induced gamma oscillations vary with objective stimulus intensity and subjective pain intensity. However, around pain threshold, perceived stimuli yielded stronger gamma oscillations than unperceived stimuli of equal stimulus intensity. These results show that pain induces gamma oscillations in primary somatosensory cortex that are particularly related to the subjective perception of pain. Our findings support the hypothesis that gamma oscillations are related to the internal representation of behaviorally relevant stimuli that should receive preferred processing.     

An efficient coding hypothesis links sparsity and selectivity of neural responses

To what extent are sensory responses in the brain compatible with first-order principles? The efficient coding hypothesis projects that neurons use as few spikes as possible to faithfully represent natural stimuli. However, many sparsely firing neurons in higher brain areas seem to violate this hypothesis in that they respond more to familiar stimuli than to nonfamiliar stimuli. We reconcile this discrepancy by showing that efficient sensory responses give rise to stimulus selectivity that depends on the stimulus-independent firing threshold and the balance between excitatory and inhibitory inputs. We construct a cost function that enforces minimal firing rates in model neurons by linearly punishing suprathreshold synaptic currents. By contrast, subthreshold currents are punished quadratically, which allows us to optimally reconstruct sensory inputs from elicited responses. We train synaptic currents on many renditions of a particular bird''s own song (BOS) and few renditions of conspecific birds'' songs (CONs). During training, model neurons develop a response selectivity with complex dependence on the firing threshold. At low thresholds, they fire densely and prefer CON and the reverse BOS (REV) over BOS. However, at high thresholds or when hyperpolarized, they fire sparsely and prefer BOS over REV and over CON. Based on this selectivity reversal, our model suggests that preference for a highly familiar stimulus corresponds to a high-threshold or strong-inhibition regime of an efficient coding strategy. Our findings apply to songbird mirror neurons, and in general, they suggest that the brain may be endowed with simple mechanisms to rapidly change selectivity of neural responses to focus sensory processing on either familiar or nonfamiliar stimuli. In summary, we find support for the efficient coding hypothesis and provide new insights into the interplay between the sparsity and selectivity of neural responses.     

Neuronal selectivity to complex vocalization features emerges in the superficial layers of primary auditory cortex

Early in auditory processing, neural responses faithfully reflect acoustic input. At higher stages of auditory processing, however, neurons become selective for particular call types, eventually leading to specialized regions of cortex that preferentially process calls at the highest auditory processing stages. We previously proposed that an intermediate step in how nonselective responses are transformed into call-selective responses is the detection of informative call features. But how neural selectivity for informative call features emerges from nonselective inputs, whether feature selectivity gradually emerges over the processing hierarchy, and how stimulus information is represented in nonselective and feature-selective populations remain open question. In this study, using unanesthetized guinea pigs (GPs), a highly vocal and social rodent, as an animal model, we characterized the neural representation of calls in 3 auditory processing stages—the thalamus (ventral medial geniculate body (vMGB)), and thalamorecipient (L4) and superficial layers (L2/3) of primary auditory cortex (A1). We found that neurons in vMGB and A1 L4 did not exhibit call-selective responses and responded throughout the call durations. However, A1 L2/3 neurons showed high call selectivity with about a third of neurons responding to only 1 or 2 call types. These A1 L2/3 neurons only responded to restricted portions of calls suggesting that they were highly selective for call features. Receptive fields of these A1 L2/3 neurons showed complex spectrotemporal structures that could underlie their high call feature selectivity. Information theoretic analysis revealed that in A1 L4, stimulus information was distributed over the population and was spread out over the call durations. In contrast, in A1 L2/3, individual neurons showed brief bursts of high stimulus-specific information and conveyed high levels of information per spike. These data demonstrate that a transformation in the neural representation of calls occurs between A1 L4 and A1 L2/3, leading to the emergence of a feature-based representation of calls in A1 L2/3. Our data thus suggest that observed cortical specializations for call processing emerge in A1 and set the stage for further mechanistic studies.

A study of the neuronal representations elicited in guinea pigs by conspecific calls at different auditory processing stages reveals insights into where call-selective neuronal responses emerge; the transformation from nonselective to call-selective responses occurs in the superficial layers of the primary auditory cortex.     

Expression of caveolin‐1 in hepatic cells increases oxidized LDL uptake and preserves the expression of lipoprotein receptors

Oxidized LDL (OxLDL) that are positively associated with the risk of developing cardiovascular diseases are ligands of scavenger receptor‐class B type I (SR‐BI) and cluster of differentiation‐36 (CD36) which can be found in caveolae. The contribution of these receptors in human hepatic cell is however unknown. The HepG2 cell, a human hepatic parenchymal cell model, expresses these receptors and is characterized by a very low level of caveolin‐1. Our aim was to define the contribution of human CD36, SR‐BI, and caveolin‐1 in the metabolism of OxLDL in HepG2 cells and conversely the effects of OxLDL on the levels/localization of these receptors. By comparing mildly (M)‐ and heavily (H)‐OxLDL metabolism between control HepG2 cells and HepG2 cells overexpressing CD36, SR‐BI, or caveolin‐1, we found that (1) CD36 increases M‐ and H‐OxLDL‐protein uptake; (2) SR‐BI drives M‐OxLDL through a degradation pathway at the expense of the cholesterol ester (CE) selective uptake pathway; (3) caveolin‐1 increases M‐ and H‐OxLDL‐protein uptake and decreases CE selective uptake from M‐OxLDL. Also, incubation with M‐ or H‐OxLDL decreases the levels of SR‐BI and LDL‐receptor in control HepG2 cells which can be overcome by caveolin‐1 expression. In addition, OxLDL move CD36 from low to high buoyant density membrane fractions, as well as caveolin‐1 in cells overexpressing this protein. Thus, hepatic caveolin‐1 expression has significant effects on OxLDL metabolism and on lipoprotein receptor levels. J. Cell. Biochem. 108: 906–915, 2009. © 2009 Wiley‐Liss, Inc.     

Effects of dietary retinoid and triglyceride on the lipid composition of rat liver stellate cells and stellate cell lipid droplets

Hepatic stellate cells store the majority of the liver's retinoid (vitamin A) reserves as retinyl esters in stellate cell lipid droplets. A study was conducted to explore the effects of differences in dietary retinoid and triglyceride intake on the composition of the stellate cell lipid droplets. Weanling rats were placed on one of five diets that differed in retinoid or triglyceride contents. The dietary groups were: 1) control (2.4 mg retinol (as retinyl acetate)/kg diet and 20.5% of the calories supplied by triglyceride (as peanut oil]; 2) low retinol (0.6 mg retinol/kg diet and control triglyceride levels); 3) high retinol (24 mg retinol/kg diet and control triglyceride levels); 4) low triglyceride (2.4 mg retinol/kg diet and 5% of the calories supplied by triglyceride); and 5) high triglyceride (2.4 mg retinol/kg diet and 45% of the calories supplied by triglyceride). Stellate cells were isolated using the pronase-collagenase method and stellate cell lipid droplets were isolated by differential centrifugation. The levels of retinoids and other lipids were measured by high performance liquid chromatography. The stellate cells from control rats contained 113 micrograms total lipid/10(6) cells. Control stellate cell lipid droplets had the following mean percent lipid composition: 39.5% retinyl ester; 31.7% triglyceride; 15.4% cholesteryl ester; 4.7% cholesterol; 6.3% phospholipids; and 2.4% free fatty acids. Both the concentration of stellate cell lipids and the composition of stellate cell lipid droplets were markedly altered by changes in dietary retinoid. The low and high retinol groups contained, respectively, 82 and 566 micrograms total lipid/10(6) cells, with retinyl ester representing, respectively, 13.6% and 65.4% of the lipid present in the stellate cell lipid droplets. Low and high triglyceride groups were similar to controls in both stellate cell lipid content and the composition of the stellate cell lipid droplets. These findings indicate that the composition of stellate cell lipid droplets is strongly regulated by dietary retinoid status but not by dietary triglyceride intake.     

A Push-Pull CORF Model of a Simple Cell with Antiphase Inhibition Improves SNR and Contour Detection

We propose a computational model of a simple cell with push-pull inhibition, a property that is observed in many real simple cells. It is based on an existing model called Combination of Receptive Fields or CORF for brevity. A CORF model uses as afferent inputs the responses of model LGN cells with appropriately aligned center-surround receptive fields, and combines their output with a weighted geometric mean. The output of the proposed model simple cell with push-pull inhibition, which we call push-pull CORF, is computed as the response of a CORF model cell that is selective for a stimulus with preferred orientation and preferred contrast minus a fraction of the response of a CORF model cell that responds to the same stimulus but of opposite contrast. We demonstrate that the proposed push-pull CORF model improves signal-to-noise ratio (SNR) and achieves further properties that are observed in real simple cells, namely separability of spatial frequency and orientation as well as contrast-dependent changes in spatial frequency tuning. We also demonstrate the effectiveness of the proposed push-pull CORF model in contour detection, which is believed to be the primary biological role of simple cells. We use the RuG (40 images) and Berkeley (500 images) benchmark data sets of images with natural scenes and show that the proposed model outperforms, with very high statistical significance, the basic CORF model without inhibition, Gabor-based models with isotropic surround inhibition, and the Canny edge detector. The push-pull CORF model that we propose is a contribution to a better understanding of how visual information is processed in the brain as it provides the ability to reproduce a wider range of properties exhibited by real simple cells. As a result of push-pull inhibition a CORF model exhibits an improved SNR, which is the reason for a more effective contour detection.     

Network Models of Frequency Modulated Sweep Detection

Frequency modulated (FM) sweeps are common in species-specific vocalizations, including human speech. Auditory neurons selective for the direction and rate of frequency change in FM sweeps are present across species, but the synaptic mechanisms underlying such selectivity are only beginning to be understood. Even less is known about mechanisms of experience-dependent changes in FM sweep selectivity. We present three network models of synaptic mechanisms of FM sweep direction and rate selectivity that explains experimental data: (1) The ‘facilitation’ model contains frequency selective cells operating as coincidence detectors, summing up multiple excitatory inputs with different time delays. (2) The ‘duration tuned’ model depends on interactions between delayed excitation and early inhibition. The strength of delayed excitation determines the preferred duration. Inhibitory rebound can reinforce the delayed excitation. (3) The ‘inhibitory sideband’ model uses frequency selective inputs to a network of excitatory and inhibitory cells. The strength and asymmetry of these connections results in neurons responsive to sweeps in a single direction of sufficient sweep rate. Variations of these properties, can explain the diversity of rate-dependent direction selectivity seen across species. We show that the inhibitory sideband model can be trained using spike timing dependent plasticity (STDP) to develop direction selectivity from a non-selective network. These models provide a means to compare the proposed synaptic and spectrotemporal mechanisms of FM sweep processing and can be utilized to explore cellular mechanisms underlying experience- or training-dependent changes in spectrotemporal processing across animal models. Given the analogy between FM sweeps and visual motion, these models can serve a broader function in studying stimulus movement across sensory epithelia.     

Neural modeling of intrinsic and spike-discharge properties of cochlear nucleus neurons

The purpose of this study was to develop neurobiologically plausible models to account for the response properties of several types of cochlear nucleus neurons. Three cell types--the bushy cells, stellate cells, and fusiform cells--were selected because useful data from intracellular recordings were available for these cell types, and because these three cell types exhibit distinct contrasts in their neuronal signal coding strategies. Stellate cells have primarily linear current-voltage (I-V) characteristics, but both bushy and fusiform cells have highly non-linear I-V characteristics. In light of this, we hypothesize that some of these cells have non-linear voltage-dependent conductances which alter their response properties. We modeled the bushy cell membrane conductance as an exponentially increasing function of membrane voltage, that of the fusiform cell as an exponentially decreasing function of the voltage, and that of the stellate cell as being voltage-independent. We have combined the voltage-dependent non-linear conductances of the cell membrane with a simple R-C circuit type of neuron model. These models reproduced the salient features of the experimentally observed I-V characteristics of the cells. In addition, we found that the models reproduced the spike discharge behavior to intracellularly injected current steps. Moreover, a more detailed study of stellate cell 'chopper'-type response patterns yielded hypotheses regarding the nature of the current that must exist at the soma during a pure-tone stimulus in order for the cells to exhibit various chopper subtype patterns, such as chop-S, chop-T, and Oc. The chop-S pattern requires a steady average current level with a relatively small variability during the tone-burst stimulus. The chop-T pattern, in contrast, requires that the current become more irregular during the tone-burst stimulus. The Oc pattern arises, however, when the input is similar to the chop-T case but the intrinsic properties of the cell model have been changed to increase the accommodation of the threshold. The implications of these findings for circuitry in the cochlear nucleus are discussed. Our analysis of these models revealed that this approach can be used to simulate neuronal cell types where I-V characteristics are known but more detailed ion channel data are not known.     

Retinal and cortical nonlinearities combine to produce masking in V1 responses to plaids

The visual response of a cell in the primary visual cortex (V1) to a drifting grating stimulus at the cell’s preferred orientation decreases when a second, perpendicular, grating is superimposed. This effect is called masking. To understand the nonlinear masking effect, we model the response of Macaque V1 simple cells in layer 4Cα to input from magnocellular Lateral Geniculate Nucleus (LGN) cells. The cortical model network is a coarse-grained reduction of an integrate-and-fire network with excitation from LGN input and inhibition from other cortical neurons. The input is modeled as a sum of LGN cell responses. Each LGN cell is modeled as the convolution of a spatio-temporal filter with the visual stimulus, normalized by a retinal contrast gain control, and followed by rectification representing the LGN spike threshold. In our model, the experimentally observed masking arises at the level of LGN input to the cortex. The cortical network effectively induces a dynamic threshold that forces the test grating to have high contrast before it can overcome the masking provided by the perpendicular grating. The subcortical nonlinearities and the cortical network together account for the masking effect. Melinda Koelling is formerly from Center for Neural Science and Courant Institute, New York University.     

Inhibitory Contribution to Suprathreshold Corticostriatal Responses: An Experimental and Modeling Study

Neostriatal neurons may undergo events of spontaneous synchronization as those observed in recurrent networks of excitatory neurons, even when cortical afferents are transected. It is necessary to explain these events because the neostriatum is a recurrent network of inhibitory neurons. Synchronization of neuronal activity may be caused by plateau-like depolarizations. Plateau-like orthodromic depolarizations that resemble up-states in medium spiny neostriatal neurons (MSNs) may be induced by a single corticostriatal suprathreshold stimulus. Slow synaptic depolarizations may last hundreds of milliseconds, decay slower than the monosynaptic glutamatergic synaptic potentials that induce them, and sustain repetitive firing. Because inhibitory inputs impinging onto MSNs have a reversal potential above the resting membrane potential but below the threshold for firing, they conform a type of “shunting inhibition”. This work asks if shunting GABAergic inputs onto MSNs arrive asynchronously enough as to help in sustaining the plateau-like corticostriatal response after a single cortical stimulus. This may help to begin explaining autonomous processing in the striatal micro-circuitry in the presence of a tonic excitatory drive and independently of spatio-temporally organized inputs. It is shown here that besides synaptic currents from AMPA/KA- and NMDA-receptors, as well as L-type intrinsic Ca²⁺- currents, inhibitory synapses help in maintaining the slow depolarization, although they accomplish the role of depressing firing at the beginning of the response. We then used a NEURON model of spiny cells to show that inhibitory synapses arriving asynchronously on the dendrites can help to simulate a plateau potential similar to that observed experimentally. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Mathematical models of cochlear nucleus onset neurons: I. Point neuron with many weak synaptic inputs

The cochlear nucleus (CN) presents a unique opportunity for quantitatively studying input-output transformations by neurons because it gives rise to a variety of different response types from a relatively homogeneous input source, the auditory nerve (AN). Particularly interesting among CN neurons are Onset (On) neurons, which have a prominent response to the onset of sustained sounds followed by little or no response in the steady-state. On neurons contrast sharply with their AN inputs, which respond vigorously throughout stimuli. On neurons can entrain to stimuli (firing once per cycle of a periodic stimulus) at up to 1000 Hz, unlike their AN inputs. To understand the mechanisms underlying these response patterns, we tested whether an integrate-to-threshold point-neuron model with a fixed refractory period can account for On discharge patterns for tones, systematically examining the effect of membrane time constant and the number and strength of the exclusively excitatory AN synaptic inputs. To produce both onset responses to high-frequency tone bursts and entrainment to a broad range of low-frequency tones, the model must have a short time constant (0.125 ms) and a large number (>100) of weak synaptic inputs, properties that are consistent with the electrical properties and anatomy of On-responding cells. With these parameters, the model acts like a coincidence detector with a threshold-like relationship between the instantaneous discharge rates of the output and the inputs. Onset responses to high-frequency tone bursts result because the threshold effect enhances the initial response of the AN inputs and suppresses their relatively lower sustained response. However, when the model entrains across a broad range of frequencies, it also produces short interspike intervals at the onset of high-frequency tone bursts, a response pattern not found in all types of On neurons. These results show a tradeoff, that may be a general property of many neurons, between following rapid stimulus fluctuations and responding without short interspike intervals at the onset of sustained stimuli.     

Lateral feedback from monophasic horizontal cells to cones in carp retina. I. Experiments

The spatial and color coding of the monophasic horizontal cells were studied in light- and dark-adapted retinae. Slit displacement experiments revealed differences in integration area for the different cone inputs of the monophasic horizontal cells. The integration area measured with a 670-nm stimulus was larger than that measured with a 570-nm stimulus. Experiments in which the diameter of the test spot was varied, however, revealed at high stimulus intensities a larger summation area for 520-nm stimuli than for 670-nm stimuli. The reverse was found for low stimulus intensities. To investigate whether these differences were due to interaction between the various cone inputs to the monophasic horizontal cell, adaptation experiments were performed. It was found that the various cone inputs were not independent. Finally, some mechanisms for the spatial and color coding will be discussed.     

Adaptive Spike Threshold Enables Robust and Temporally Precise Neuronal Encoding

Neural processing rests on the intracellular transformation of information as synaptic inputs are translated into action potentials. This transformation is governed by the spike threshold, which depends on the history of the membrane potential on many temporal scales. While the adaptation of the threshold after spiking activity has been addressed before both theoretically and experimentally, it has only recently been demonstrated that the subthreshold membrane state also influences the effective spike threshold. The consequences for neural computation are not well understood yet. We address this question here using neural simulations and whole cell intracellular recordings in combination with information theoretic analysis. We show that an adaptive spike threshold leads to better stimulus discrimination for tight input correlations than would be achieved otherwise, independent from whether the stimulus is encoded in the rate or pattern of action potentials. The time scales of input selectivity are jointly governed by membrane and threshold dynamics. Encoding information using adaptive thresholds further ensures robust information transmission across cortical states i.e. decoding from different states is less state dependent in the adaptive threshold case, if the decoding is performed in reference to the timing of the population response. Results from in vitro neural recordings were consistent with simulations from adaptive threshold neurons. In summary, the adaptive spike threshold reduces information loss during intracellular information transfer, improves stimulus discriminability and ensures robust decoding across membrane states in a regime of highly correlated inputs, similar to those seen in sensory nuclei during the encoding of sensory information.     

Representation of Time-Varying Stimuli by a Network Exhibiting Oscillations on a Faster Time Scale

Sensory processing is associated with gamma frequency oscillations (30–80 Hz) in sensory cortices. This raises the question whether gamma oscillations can be directly involved in the representation of time-varying stimuli, including stimuli whose time scale is longer than a gamma cycle. We are interested in the ability of the system to reliably distinguish different stimuli while being robust to stimulus variations such as uniform time-warp. We address this issue with a dynamical model of spiking neurons and study the response to an asymmetric sawtooth input current over a range of shape parameters. These parameters describe how fast the input current rises and falls in time. Our network consists of inhibitory and excitatory populations that are sufficient for generating oscillations in the gamma range. The oscillations period is about one-third of the stimulus duration. Embedded in this network is a subpopulation of excitatory cells that respond to the sawtooth stimulus and a subpopulation of cells that respond to an onset cue. The intrinsic gamma oscillations generate a temporally sparse code for the external stimuli. In this code, an excitatory cell may fire a single spike during a gamma cycle, depending on its tuning properties and on the temporal structure of the specific input; the identity of the stimulus is coded by the list of excitatory cells that fire during each cycle. We quantify the properties of this representation in a series of simulations and show that the sparseness of the code makes it robust to uniform warping of the time scale. We find that resetting of the oscillation phase at stimulus onset is important for a reliable representation of the stimulus and that there is a tradeoff between the resolution of the neural representation of the stimulus and robustness to time-warp.     

Analysis of excitatory microcircuitry in the medial entorhinal cortex reveals cell-type-specific differences

Medial entorhinal cortex (MEC) plays an important role in physiological processes underlying navigation, learning, and memory. Excitatory cells in the different MEC layers project in a region-specific manner to the hippocampus. However, the intrinsic microcircuitry of the main excitatory cells in the superficial MEC layers is largely unknown. Using scanning photostimulation, we investigated the functional microcircuitry of two such cell types, stellate and pyramidal cells. We found cell-type-specific intralaminar and ascending interlaminar feedback inputs. The ascending interlaminar inputs display distinct organizational principles depending on the cell-type and its position within the superficial lamina: the spatial spread of inputs for stellate cells is narrower than for pyramidal cells, while inputs to pyramidal cells in layer 3, but not in layer 2, exhibit an asymmetric offset to the medial side of the cell's main axis. Differential laminar sources of excitatory inputs might contribute to the functional diversity of stellate and pyramidal cells.     

A novel somatosensory spatial navigation system outside the hippocampal formation

Spatially selective firing of place cells, grid cells, boundary vector/border cells and head direction cells constitutes the basic building blocks of a canonical spatial navigation system centered on the hippocampal-entorhinal complex. While head direction cells can be found throughout the brain, spatial tuning outside the hippocampal formation is often non-specific or conjunctive to other representations such as a reward. Although the precise mechanism of spatially selective firing activity is not understood, various studies show sensory inputs, particularly vision, heavily modulate spatial representation in the hippocampal-entorhinal circuit. To better understand the contribution of other sensory inputs in shaping spatial representation in the brain, we performed recording from the primary somatosensory cortex in foraging rats. To our surprise, we were able to detect the full complement of spatially selective firing patterns similar to that reported in the hippocampal-entorhinal network, namely, place cells, head direction cells, boundary vector/border cells, grid cells and conjunctive cells, in the somatosensory cortex. These newly identified somatosensory spatial cells form a spatial map outside the hippocampal formation and support the hypothesis that location information modulates body representation in the somatosensory cortex. Our findings provide transformative insights into our understanding of how spatial information is processed and integrated in the brain, as well as functional operations of the somatosensory cortex in the context of rehabilitation with brain-machine interfaces.Subject terms: Biological techniques, Cell biology