Linearity of summation of synaptic potentials underlying direction selectivity in simple cells of the cat visual cortex

Optimization in Visual Motion Estimation

Sighted animals use visual signals to discern directional motion in their environment. Motion is not directly detected by visual neurons, and it must instead be computed from light signals that vary over space and time. This makes visual motion estimation a near universal neural computation, and decades of research have revealed much about the algorithms and mechanisms that generate directional signals. The idea that sensory systems are optimized for performance in natural environments has deeply impacted this research. In this article, we review the many ways that optimization has been used to quantitatively model visual motion estimation and reveal its underlying principles. We emphasize that no single optimization theory has dominated the literature. Instead, researchers have adeptly incorporated different computational demands and biological constraints that are pertinent to the specific brain system and animal model under study. The successes and failures of the resulting optimization models have thereby provided insights into how computational demands and biological constraints together shape neural computation.

Direct comparison reveals algorithmic similarities in fly and mouse visual motion detection

Evolution has equipped vertebrates and invertebrates with neural circuits that selectively encode visual motion. While similarities in the computations performed by these circuits in mouse and fruit fly have been noted, direct experimental comparisons have been lacking. Because molecular mechanisms and neuronal morphology in the two species are distinct, we directly compared motion encoding in these two species at the algorithmic level, using matched stimuli and focusing on a pair of analogous neurons, the mouse ON starburst amacrine cell (ON SAC) and Drosophila T4 neurons. We find that the cells share similar spatiotemporal receptive field structures, sensitivity to spatiotemporal correlations, and tuning to sinusoidal drifting gratings, but differ in their responses to apparent motion stimuli. Both neuron types showed a response to summed sinusoids that deviates from models for motion processing in these cells, underscoring the similarities in their processing and identifying response features that remain to be explained.

The computational rules of cross-modality suppression in the visual posterior sylvian area

The macaque visual posterior sylvian area (VPS) is an area with neurons responding selectively to heading direction in both visual and vestibular modalities, but how VPS neurons combined these two sensory signals is still unknown. In contrast to the subadditive characteristics in the medial superior temporal area (MSTd), responses in VPS were dominated by vestibular signals, with approximately a winner-take-all competition. The conditional Fisher information analysis shows that VPS neural population encodes information from distinct sensory modalities under large and small offset conditions, which differs from MSTd whose neural population contains more information about visual stimuli in both conditions. However, the combined responses of single neurons in both areas can be well fit by weighted linear sums of unimodal responses. Furthermore, a normalization model captured most vestibular and visual interaction characteristics for both VPS and MSTd, indicating the divisive normalization mechanism widely exists in the cortex.

Predicting individual neuron responses with anatomically constrained task optimization

Artificial neural networks trained to solve sensory tasks can develop statistical representations that match those in biological circuits. However, it remains unclear whether they can reproduce properties of individual neurons. Here, we investigated how artificial networks predict individual neuron properties in the visual motion circuits of the fruit fly Drosophila. We trained anatomically constrained networks to predict movement in natural scenes, solving the same inference problem as fly motion detectors. Units in the artificial networks adopted many properties of analogous individual neurons, even though they were not explicitly trained to match these properties. Among these properties was the split into ON and OFF motion detectors, which is not predicted by classical motion detection models. The match between model and neurons was closest when models were trained to be robust to noise. These results demonstrate how anatomical, task, and noise constraints can explain properties of individual neurons in a small neural network.

The physiological basis for contrast opponency in motion computation in Drosophila

In Drosophila, direction-selective neurons implement a mechanism of motion computation similar to cortical neurons, using contrast-opponent receptive fields with ON and OFF subfields. It is not clear how the presynaptic circuitry of direction-selective neurons in the OFF pathway supports this computation if all major inputs are OFF-rectified neurons. Here, we reveal the biological substrate for motion computation in the OFF pathway. Three interneurons, Tm2, Tm9 and CT1, provide information about ON stimuli to the OFF direction-selective neuron T5 across its receptive field, supporting a contrast-opponent receptive field organization. Consistent with its prominent role in motion detection, variability in Tm9 receptive field properties transfers to T5, and calcium decrements in Tm9 in response to ON stimuli persist across behavioral states, while spatial tuning is sharpened by active behavior. Together, our work shows how a key neuronal computation is implemented by its constituent neuronal circuit elements to ensure direction selectivity.

Micropipet-Based Navigation in a Microvascular Model for Imaging Endothelial Cell Topography Using Scanning Ion Conductance Microscopy

Scanning ion conductance microscopy (SICM) has enabled cell surface topography at a high resolution with low invasiveness. However, SICM has not been applied to the observation of cell surfaces in hydrogels, which can serve as scaffolds for three-dimensional cell culture. In this study, we applied SICM for imaging a cell surface in a microvascular lumen reconstructed in a hydrogel. To achieve this goal, we developed a micropipet navigation technique using ionic current to detect the position of a microvascular lumen. Combining this navigation technique with SICM, endothelial cells in a microvascular model and blebs were visualized successfully at the single-cell level. To the best of our knowledge, this is the first report on visualizing cell surfaces in hydrogels using a SICM. This technique will be useful for furthering our understanding of the mechanism of intravascular diseases.

A minimal synaptic model for direction selective neurons in Drosophila

Visual motion estimation is a canonical neural computation. In Drosophila, recent advances have identified anatomic and functional circuitry underlying direction-selective computations. Models with varying levels of abstraction have been proposed to explain specific experimental results but have rarely been compared across experiments. Here we use the wealth of available anatomical and physiological data to construct a minimal, biophysically inspired synaptic model for Drosophila’s first-order direction-selective T4 cells. We show how this model relates mathematically to classical models of motion detection, including the Hassenstein-Reichardt correlator model. We used numerical simulation to test how well this synaptic model could reproduce measurements of T4 cells across many datasets and stimulus modalities. These comparisons include responses to sinusoid gratings, to apparent motion stimuli, to stochastic stimuli, and to natural scenes. Without fine-tuning this model, it sufficed to reproduce many, but not all, response properties of T4 cells. Since this model is flexible and based on straightforward biophysical properties, it provides an extensible framework for developing a mechanistic understanding of T4 neural response properties. Moreover, it can be used to assess the sufficiency of simple biophysical mechanisms to describe features of the direction-selective computation and identify where our understanding must be improved.

Synaptic and intrinsic mechanisms underlying development of cortical direction selectivity

Modifications of synaptic inputs and cell-intrinsic properties both contribute to neuronal plasticity and development. To better understand these mechanisms, we undertook an intracellular analysis of the development of direction selectivity in the ferret visual cortex, which occurs rapidly over a few days after eye opening. We found strong evidence of developmental changes in linear spatiotemporal receptive fields of simple cells, implying alterations in circuit inputs. Further, this receptive field plasticity was accompanied by increases in near-spike-threshold excitability and input-output gain that resulted in dramatically increased spiking responses in the experienced state. Increases in subthreshold membrane responses induced by the receptive field plasticity and the increased input-output spiking gain were both necessary to explain the elevated firing rates in experienced ferrets. These results demonstrate that cortical direction selectivity develops through a combination of plasticity in inputs and in cell-intrinsic properties.

The computation of directional selectivity in the Drosophila OFF motion pathway

In flies, the direction of moving ON and OFF features is computed separately. T4 (ON) and T5 (OFF) are the first neurons in their respective pathways to extract a directionally selective response from their non-selective inputs. Our recent study of T4 found that the integration of offset depolarizing and hyperpolarizing inputs is critical for the generation of directional selectivity. However, T5s lack small-field inhibitory inputs, suggesting they may use a different mechanism. Here we used whole-cell recordings of T5 neurons and found a similar receptive field structure: fast depolarization and persistent, spatially offset hyperpolarization. By assaying pairwise interactions of local stimulation across the receptive field, we found no amplifying responses, only suppressive responses to the non-preferred motion direction. We then evaluated passive, biophysical models and found that a model using direct inhibition, but not the removal of excitation, can accurately predict T5 responses to a range of moving stimuli.

Sequential Nonlinear Filtering of Local Motion Cues by Global Motion Circuits

Many animals guide their movements using optic flow, the displacement of stationary objects across the retina caused by self-motion. How do animals selectively synthesize a global motion pattern from its local motion components? To what extent does this feature selectivity rely on circuit mechanisms versus dendritic processing? Here we used in vivo calcium imaging to identify pre- and postsynaptic mechanisms for processing local motion signals in global motion detection circuits in Drosophila. Lobula plate tangential cells (LPTCs) detect global motion by pooling input from local motion detectors, T4/T5 neurons. We show that T4/T5 neurons suppress responses to adjacent local motion signals whereas LPTC dendrites selectively amplify spatiotemporal sequences of local motion signals consistent with preferred global patterns. We propose that sequential nonlinear suppression and amplification operations allow optic flow circuitry to simultaneously prevent saturating responses to local signals while creating selectivity for global motion patterns critical to behavior.

Linear Summation Underlies Direction Selectivity in Drosophila

While linear mechanisms lay the foundations of feature selectivity in many brain areas, direction selectivity in the elementary motion detector (EMD) of the fly has become a paradigm of nonlinear neuronal computation. We have bridged this divide by demonstrating that linear spatial summation can generate direction selectivity in the fruit fly Drosophila. Using linear systems analysis and two-photon imaging of a genetically encoded voltage indicator, we measure the emergence of direction-selective (DS) voltage signals in the Drosophila OFF pathway. Our study is a direct, quantitative investigation of the algorithm underlying directional signals, with the striking finding that linear spatial summation is sufficient for the emergence of direction selectivity. A linear stage of the fly EMD strongly resembles similar computations in vertebrate visual cortex, demands a reappraisal of the role of upstream nonlinearities, and implicates the voltage-to-calcium transformation in the refinement of feature selectivity in this system. VIDEO ABSTRACT.

Elementary Motion Detection in Drosophila: Algorithms and Mechanisms

Motion in the visual world provides critical information to guide the behavior of sighted animals. Furthermore, as visual motion estimation requires comparisons of signals across inputs and over time, it represents a paradigmatic and generalizable neural computation. Focusing on the Drosophila visual system, where an explosion of technological advances has recently accelerated experimental progress, we review our understanding of how, algorithmically and mechanistically, motion signals are first computed.

Simple integration of fast excitation and offset, delayed inhibition computes directional selectivity in Drosophila

A neuron that extracts directionally selective motion information from upstream signals lacking this selectivity must compare visual responses from spatially offset inputs. Distinguishing among prevailing algorithmic models for this computation requires measuring fast neuronal activity and inhibition. In the Drosophila melanogaster visual system, a fourth-order neuron-T4-is the first cell type in the ON pathway to exhibit directionally selective signals. Here we use in vivo whole-cell recordings of T4 to show that directional selectivity originates from simple integration of spatially offset fast excitatory and slow inhibitory inputs, resulting in a suppression of responses to the nonpreferred motion direction. We constructed a passive, conductance-based model of a T4 cell that accurately predicts the neuron's response to moving stimuli. These results connect the known circuit anatomy of the motion pathway to the algorithmic mechanism by which the direction of motion is computed.

Linearization of excitatory synaptic integration at no extra cost

In many theories of neural computation, linearly summed synaptic activation is a pervasive assumption for the computations performed by individual neurons. Indeed, for certain nominally optimal models, linear summation is required. However, the biophysical mechanisms needed to produce linear summation may add to the energy-cost of neural processing. Thus, the benefits provided by linear summation may be outweighed by the energy-costs. Using voltage-gated conductances in a relatively simple neuron model, this paper quantifies the cost of linearizing dendritically localized synaptic activation. Different combinations of voltage-gated conductances were examined, and many are found to produce linearization; here, four of these models are presented. Comparing the energy-costs to a purely passive model, reveals minimal or even no additional costs in some cases.

Whole-Cell Recording of Neuronal Membrane Potential during Behavior

Neuronal membrane potential is of fundamental importance for the mechanistic understanding of brain function. This review discusses progress in whole-cell patch-clamp recordings for low-noise measurement of neuronal membrane potential in awake behaving animals. Whole-cell recordings can be combined with two-photon microscopy to target fluorescently labeled neurons, revealing cell-type-specific membrane potential dynamics of retrogradely or genetically labeled neurons. Dual whole-cell recordings reveal behavioral modulation of membrane potential synchrony and properties of synaptic transmission in vivo. Optogenetic manipulations are also readily integrated with whole-cell recordings, providing detailed information about the effect of specific perturbations on the membrane potential of diverse types of neurons. Exciting developments for future behavioral experiments include dendritic whole-cell recordings and imaging, and use of the whole-cell recording pipette for single-cell delivery of drugs and DNA, as well as RNA expression profiling. Whole-cell recordings therefore offer unique opportunities for investigating the neuronal circuits and synaptic mechanisms driving membrane potential dynamics during behavior.

A Neural Signature of Divisive Normalization at the Level of Multisensory Integration in Primate Cortex

Studies of multisensory integration by single neurons have traditionally emphasized empirical principles that describe nonlinear interactions between inputs from two sensory modalities. We previously proposed that many of these empirical principles could be explained by a divisive normalization mechanism operating in brain regions where multisensory integration occurs. This normalization model makes a critical diagnostic prediction: a non-preferred sensory input from one modality, which activates the neuron on its own, should suppress the response to a preferred input from another modality. We tested this prediction by recording from neurons in macaque area MSTd that integrate visual and vestibular cues regarding self-motion. We show that many MSTd neurons exhibit the diagnostic form of cross-modal suppression, whereas unisensory neurons in area MT do not. The normalization model also fits population responses better than a model based on subtractive inhibition. These findings provide strong support for a divisive normalization mechanism in multisensory integration.

The divisive normalization model of V1 neurons: a comprehensive comparison of physiological data and model predictions

The physiological responses of simple and complex cells in the primary visual cortex (V1) have been studied extensively and modeled at different levels. At the functional level, the divisive normalization model (DNM; Heeger DJ. Vis Neurosci 9: 181-197, 1992) has accounted for a wide range of single-cell recordings in terms of a combination of linear filtering, nonlinear rectification, and divisive normalization. We propose standardizing the formulation of the DNM and implementing it in software that takes static grayscale images as inputs and produces firing rate responses as outputs. We also review a comprehensive suite of 30 empirical phenomena and report a series of simulation experiments that qualitatively replicate dozens of key experiments with a standard parameter set consistent with physiological measurements. This systematic approach identifies novel falsifiable predictions of the DNM. We show how the model simultaneously satisfies the conflicting desiderata of flexibility and falsifiability. Our key idea is that, while adjustable parameters are needed to accommodate the diversity across neurons, they must be fixed for a given individual neuron. This requirement introduces falsifiable constraints when this single neuron is probed with multiple stimuli. We also present mathematical analyses and simulation experiments that explicate some of these constraints.

A consensus layer V pyramidal neuron can sustain interpulse-interval coding

In terms of a single neuron's long-distance communication, interpulse intervals (IPIs) are an attractive alternative to rate and binary codes. As a proxy for an IPI, a neuron's time-to-spike can be found in the biophysical and experimental intracellular literature. Using the current, consensus layer V pyramidal neuron, the present study examines the feasibility of IPI-coding and examines the noise sources that limit the information rate of such an encoding. In descending order of importance, the noise sources are (i) synaptic variability, (ii) sodium channel shot-noise, followed by (iii) thermal noise. The biophysical simulations allow the calculation of mutual information, which is about 3.0 bits/spike. More importantly, while, by any conventional definition, the biophysical model is highly nonlinear, the underlying function that relates input intensity to the defined output variable is linear. When one assumes the perspective of a neuron coding via first hitting-time, this result justifies a pervasive and simplifying assumption of computational modelers-that a class of cortical neurons can be treated as linearly additive, computational devices.

Cortical amplification models of experience-dependent development of selective columns and response sparsification

The development of direction-selective cortical columns requires visual experience, but the neural circuits and plasticity mechanisms that are responsible for this developmental transition are unknown. To gain insight into the mechanisms that could underlie experience-dependent increases in selectivity, we explored families of cortical amplifier models that enhance weakly biased feedforward signals. Here we focused exclusively on possible contributions of cortico-cortical connections and took feedforward input to be constant. We modeled pairs of interconnected columns that received equal and oppositely biased inputs. In a single-element model of cortical columns, we found two ways that cortical columns could receive biased feedforward input and exhibit strong but unselective responses to stimuli: 1) within-column recurrent excitatory connections could be strong enough to amplify both strong and weak feedforward input, or 2) columns that received differently biased inputs could have strong excitatory cross-connections that destroy selectivity. A Hebbian plasticity rule combined with simulated experience with stimuli weakened these strong cross-connections across cortical columns, allowing the individual columns to respond selectively to their biased inputs. In a model that included both excitatory and inhibitory neurons in each column, an additional means of obtaining selectivity through the cortical circuit was uncovered: cross-column suppression of inhibition-stabilized networks. When each column operated as an inhibition-stabilized network, cross-column excitation onto inhibitory neurons forced competition between the columns but in a manner that did not involve strong null-direction inhibition, consistent with experimental measurements of direction selectivity in visual cortex. Experimental predictions of these possible contributions of cortical circuits are discussed.NEW & NOTEWORTHY Sensory circuits are initially constructed via mechanisms that are independent of sensory experience, but later refinement requires experience. We constructed models of how circuits that receive biased feedforward inputs can be initially unselective and then be modified by experience and plasticity so that the resulting circuit exhibits increased selectivity. We propose that neighboring cortical columns may initially exhibit coupling that is too strong for selectivity. Experience-dependent mechanisms decrease this coupling so individual columns can exhibit selectivity.

Direction Selectivity in Drosophila Emerges from Preferred-Direction Enhancement and Null-Direction Suppression

Across animal phyla, motion vision relies on neurons that respond preferentially to stimuli moving in one, preferred direction over the opposite, null direction. In the elementary motion detector of Drosophila, direction selectivity emerges in two neuron types, T4 and T5, but the computational algorithm underlying this selectivity remains unknown. We find that the receptive fields of both T4 and T5 exhibit spatiotemporally offset light-preferring and dark-preferring subfields, each obliquely oriented in spacetime. In a linear-nonlinear modeling framework, the spatiotemporal organization of the T5 receptive field predicts the activity of T5 in response to motion stimuli. These findings demonstrate that direction selectivity emerges from the enhancement of responses to motion in the preferred direction, as well as the suppression of responses to motion in the null direction. Thus, remarkably, T5 incorporates the essential algorithmic strategies used by the Hassenstein-Reichardt correlator and the Barlow-Levick detector. Our model for T5 also provides an algorithmic explanation for the selectivity of T5 for moving dark edges: our model captures all two- and three-point spacetime correlations relevant to motion in this stimulus class. More broadly, our findings reveal the contribution of input pathway visual processing, specifically center-surround, temporally biphasic receptive fields, to the generation of direction selectivity in T5. As the spatiotemporal receptive field of T5 in Drosophila is common to the simple cell in vertebrate visual cortex, our stimulus-response model of T5 will inform efforts in an experimentally tractable context to identify more detailed, mechanistic models of a prevalent computation.

SIGNIFICANCE STATEMENT

Feature selective neurons respond preferentially to astonishingly specific stimuli, providing the neurobiological basis for perception. Direction selectivity serves as a paradigmatic model of feature selectivity that has been examined in many species. While insect elementary motion detectors have served as premiere experimental models of direction selectivity for 60 years, the central question of their underlying algorithm remains unanswered. Using in vivo two-photon imaging of intracellular calcium signals, we measure the receptive fields of the first direction-selective cells in the Drosophila visual system, and define the algorithm used to compute the direction of motion. Computational modeling of these receptive fields predicts responses to motion and reveals how this circuit efficiently captures many useful correlations intrinsic to moving dark edges.

Principles underlying sensory map topography in primary visual cortex

The primary visual cortex contains a detailed map of the visual scene, which is represented according to multiple stimulus dimensions including spatial location, ocular dominance and orientation. The maps for spatial location and ocular dominance originate from the spatial arrangement of thalamic axons in cortex. However, the origin of the other maps remains unclear. Here we demonstrate that the cortical maps for orientation, direction and retinal disparity are all strongly related to the organization for spatial location of light (ON) and dark (OFF) stimuli, an organization that we show is OFF-dominated, OFF-centric and runs orthogonal to ocular dominance columns. Because this ON/OFF organization originates from the clustering of ON and OFF thalamic afferents in visual cortex, we conclude that all main features of cortical topography, including orientation, direction and retinal disparity, follow a common organizing principle that arranges thalamic axons with similar retinotopy and ON/OFF polarity in neighboring cortical regions.

Direction selectivity of neurons in the visual cortex is non-linear and lamina-dependent

Neurons in the visual cortex are generally selective to direction of movement of a stimulus. Although models of this direction selectivity (DS) assume linearity, experimental data show stronger degrees of DS than those predicted by linear models. Our current study was intended to determine the degree of non-linearity of the DS mechanism for cells within different laminae of the cat's primary visual cortex. To do this, we analysed cells in our database by using neurophysiological and histological approaches to quantify non-linear components of DS in four principal cortical laminae (layers 2/3, 4, 5, and 6). We used a DS index (DSI) to quantify degrees of DS in our sample. Our results showed laminar differences. In layer 4, the main thalamic input region, most neurons were of the simple type and showed high DSI values. For complex cells in layer 4, there was a broad distribution of DSI values. Similar features were observed in layer 2/3, but complex cells were dominant. In deeper layers (5 and 6), DSI value distributions were characterized by clear peaks at high values. Independently of specific lamina, high DSI values were accompanied by narrow orientation tuning widths. Differences in orientation tuning for non-preferred vs. preferred directions were smallest in layer 4 and largest in layer 6. These results are consistent with a non-linear process of intra-cortical inhibition that enhances DS by selective suppression of neuronal firing for non-preferred directions of stimulus motion in a lamina-dependent manner. Other potential mechanisms are also considered.

Species specificity of temporal processing in the auditory midbrain of gray treefrogs: long-interval neurons

In recently diverged gray treefrogs (Hyla chrysoscelis and H. versicolor), advertisement calls that differ primarily in pulse shape and pulse rate act as an important premating isolation mechanism. Temporally selective neurons in the anuran inferior colliculus may contribute to selective behavioral responses to these calls. Here we present in vivo extracellular and whole-cell recordings from long-interval-selective neurons (LINs) made during presentation of pulses that varied in shape and rate. Whole-cell recordings revealed that interplay between excitation and inhibition shapes long-interval selectivity. LINs in H. versicolor showed greater selectivity for slow-rise pulses, consistent with the slow-rise pulse characteristics of their calls. The steepness of pulse-rate tuning functions, but not the distributions of best pulse rates, differed between the species in a manner that depended on whether pulses had slow or fast-rise shape. When tested with stimuli representing the temporal structure of the advertisement calls of H. chrysoscelis or H. versicolor, approximately 27 % of LINs in H. versicolor responded exclusively to the latter stimulus type. The LINs of H. chrysoscelis were less selective. Encounter calls, which are produced at similar pulse rates in both species (≈5 pulses/s), are likely to be effective stimuli for the LINs of both species.

Inhibition facilitates direction selectivity in a noisy cortical environment

In a broad class of models, direction selectivity in primary visual cortical neurons arises from the linear summation of spatially offset and temporally lagged inputs combined with a spike threshold. Here, we characterize the robustness of this class of models to input noise and background activity that is uncorrelated with the visual stimulus. When only excitatory inputs were considered, moderate levels of noise substantially degraded direction selectivity. By contrast, the inclusion of inhibition produced a direction-selective neuron even at high noise levels. Moreover, if inhibitory inputs were tuned, mirroring excitatory inputs but lagging by a fixed delay, they promoted a highly direction-selective response by suppressing all excitatory inputs in the null direction while minimally affecting excitatory inputs in the preferred direction. Additionally, tuned inhibition strongly reduced trial-by-trial variability, such that the neuron produced a consistent direction-selective response to multiple presentation of the same stimulus. This work illustrates how inhibition could be used by cortical circuits to reliably extract information on a single-trial basis from feed-forward inputs in a noisy, high-background context.

A fast BK-type KCa current acts as a postsynaptic modulator of temporal selectivity for communication signals

Temporal patterns of spiking often convey behaviorally relevant information. Various synaptic mechanisms and intrinsic membrane properties can influence neuronal selectivity to temporal patterns of input. However, little is known about how synaptic mechanisms and intrinsic properties together determine the temporal selectivity of neuronal output. We tackled this question by recording from midbrain electrosensory neurons in mormyrid fish, in which the processing of temporal intervals between communication signals can be studied in a reduced in vitro preparation. Mormyrids communicate by varying interpulse intervals (IPIs) between electric pulses. Within the midbrain posterior exterolateral nucleus (ELp), the temporal patterns of afferent spike trains are filtered to establish single-neuron IPI tuning. We performed whole-cell recording from ELp neurons in a whole-brain preparation and examined the relationship between intrinsic excitability and IPI tuning. We found that spike frequency adaptation of ELp neurons was highly variable. Postsynaptic potentials (PSPs) of strongly adapting (phasic) neurons were more sharply tuned to IPIs than weakly adapting (tonic) neurons. Further, the synaptic filtering of IPIs by tonic neurons was more faithfully converted into variation in spiking output, particularly at short IPIs. Pharmacological manipulation under current- and voltage-clamp revealed that tonic firing is mediated by a fast, large-conductance Ca(2+)-activated K(+) (KCa) current (BK) that speeds up action potential repolarization. These results suggest that BK currents can shape the temporal filtering of sensory inputs by modifying both synaptic responses and PSP-to-spike conversion. Slow SK-type KCa currents have previously been implicated in temporal processing. Thus, both fast and slow KCa currents can fine-tune temporal selectivity.

Frequency-dependent signal processing in apical dendrites of hippocampal CA1 pyramidal cells

Depending on an animal's behavioral state, hippocampal CA1 pyramidal cells receive distinct patterns of excitatory and inhibitory synaptic inputs. The time-dependent changes in the frequencies of these inputs and the nonuniform distribution of voltage-gated channels lead to dynamic fluctuations in membrane conductance. In this study, using a whole-cell patch-clamp method, we attempted to record and analyze the frequency dependencies of membrane responsiveness in Wistar rat hippocampal CA1 pyramidal cells following noise current injection directly into dendrites and somata under pharmacological blockade of all synaptic inputs. To estimate the frequency-dependent properties of membrane potential, membrane impedance was determined from the voltage response divided by the input current in the frequency domain. The cell membrane of most neurons showed low-pass filtering properties in all regions. In particular, the properties were strongly expressed in the somata or proximal dendrites. Moreover, the data revealed nonuniform distribution of dendritic impedance, which was high in the intermediate segment of the apical dendritic shaft (∼220-260μm from the soma). The low-pass filtering properties in the apical dendrites were more enhanced by membrane depolarization than those in the somata. Coherence spectral analysis revealed high coherence between the input signal and the output voltage response in the theta-gamma frequency range, and large lags emerged in the distal dendrites in the gamma frequency range. Our results suggest that apical dendrites of hippocampal CA1 pyramidal cells integrate synaptic inputs according to the frequency components of the input signal along the dendritic segments receiving the inputs.

The Touch and Zap method for in vivo whole-cell patch recording of intrinsic and visual responses of cortical neurons and glial cells

Whole-cell patch recording is an essential tool for quantitatively establishing the biophysics of brain function, particularly in vivo. This method is of particular interest for studying the functional roles of cortical glial cells in the intact brain, which cannot be assessed with extracellular recordings. Nevertheless, a reasonable success rate remains a challenge because of stability, recording duration and electrical quality constraints, particularly for voltage clamp, dynamic clamp or conductance measurements. To address this, we describe "Touch and Zap", an alternative method for whole-cell patch clamp recordings, with the goal of being simpler, quicker and more gentle to brain tissue than previous approaches. Under current clamp mode with a continuous train of hyperpolarizing current pulses, seal formation is initiated immediately upon cell contact, thus the "Touch". By maintaining the current injection, whole-cell access is spontaneously achieved within seconds from the cell-attached configuration by a self-limited membrane electroporation, or "Zap", as seal resistance increases. We present examples of intrinsic and visual responses of neurons and putative glial cells obtained with the revised method from cat and rat cortices in vivo. Recording parameters and biophysical properties obtained with the Touch and Zap method compare favourably with those obtained with the traditional blind patch approach, demonstrating that the revised approach does not compromise the recorded cell. We find that the method is particularly well-suited for whole-cell patch recordings of cortical glial cells in vivo, targeting a wider population of this cell type than the standard method, with better access resistance. Overall, the gentler Touch and Zap method is promising for studying quantitative functional properties in the intact brain with minimal perturbation of the cell's intrinsic properties and local network. Because the Touch and Zap method is performed semi-automatically, this approach is more reproducible and less dependent on experimenter technique.

Strengthening of Direction Selectivity by Broadly Tuned and Spatiotemporally Slightly Offset Inhibition in Mouse Visual Cortex

Direction selectivity (DS) of neuronal responses is fundamental for motion detection. How the integration of synaptic excitation and inhibition contributes to DS however remains not well-understood. Here, in vivo whole-cell voltage-clamp recordings in mouse primary visual cortex (V1) revealed that layer 4 simple cells received direction-tuned excitatory inputs but barely tuned inhibitory inputs under drifting-bar stimulation. Excitation and inhibition exhibited differential temporal offsets under movements of opposite directions: excitation peaked earlier than inhibition at the preferred direction, and vice versa at the null direction. This could be attributed to a small spatial mismatch between overlapping excitatory and inhibitory receptive fields: the distribution of excitatory input strengths was skewed and the skewness was strongly correlated with the DS of excitatory input, whereas that of inhibitory input strengths was spatially symmetric. Neural modeling revealed that the relatively stronger inhibition under null directional movements, as well as the specific spatial-temporal offsets between excitation and inhibition, allowed inhibition to enhance the DS of output responses by suppressing the null response more effectively than the preferred response. Our data demonstrate that while tuned excitatory input provides the basis for DS in mouse V1, the largely untuned and spatiotemporally offset inhibition contributes importantly to sharpening of DS.

Sensory-evoked synaptic integration in cerebellar and cerebral cortical neurons

Neurons integrate synaptic inputs across time and space, a process that determines the transformation of input signals into action potential output. This article explores how synaptic integration contributes to the richness of sensory signalling in the cerebellar and cerebral cortices. Whether a neuron receives a few or a few thousand discrete inputs, most evoked synaptic activity generates only subthreshold membrane potential fluctuations. Sensory tuning of synaptic inputs is typically broad, but short-term dynamics and the interplay between excitation and inhibition restrict action potential firing to narrow windows of opportunity. We highlight the challenges and limitations of the use of somatic recordings in the study of synaptic integration and the importance of active dendritic mechanisms in sensory processing.

Directional hearing by linear summation of binaural inputs at the medial superior olive

Neurons in the medial superior olive (MSO) enable sound localization by their remarkable sensitivity to submillisecond interaural time differences (ITDs). Each MSO neuron has its own "best ITD" to which it responds optimally. A difference in physical path length of the excitatory inputs from both ears cannot fully account for the ITD tuning of MSO neurons. As a result, it is still debated how these inputs interact and whether the segregation of inputs to opposite dendrites, well-timed synaptic inhibition, or asymmetries in synaptic potentials or cellular morphology further optimize coincidence detection or ITD tuning. Using in vivo whole-cell and juxtacellular recordings, we show here that ITD tuning of MSO neurons is determined by the timing of their excitatory inputs. The inputs from both ears sum linearly, whereas spike probability depends nonlinearly on the size of synaptic inputs. This simple coincidence detection scheme thus makes accurate sound localization possible.

Binocular activation elicits differences in neurometabolic coupling in visual cortex

Non-invasive brain imaging requires comprehensive interpretation of hemodynamic signals. In functional magnetic resonance imaging, blood oxygen level dependent (BOLD) signals are used to infer neural processes. This necessitates a clear understanding of how BOLD signals and neural activity are related. One fundamental question concerns the relative importance of synaptic activity and spiking discharge. Although these two components are related, most previous work shows that synaptic activity is better reflected in the BOLD signal. However, the mechanisms of this relationship are not clear. The BOLD signal depends on relative changes in cerebral blood flow and cerebral metabolic rate of oxygen. Oxygen metabolism changes are difficult to measure with current imaging techniques, but it is possible to obtain direct quantitative simultaneous in vivo measurement of tissue oxygen and co-localized underlying neural activity. Here, we use this approach with a specific binocular stimulus protocol in order to activate inhibitory and excitatory neuronal pathways in the visual cortex. During excitatory binocular interaction, we find that metabolic, spiking, and local field potential responses are correlated. However, during suppressive binocular interaction, spiking activity and local field potentials (LFP) are dissociated while only the latter is coupled with metabolic response. These results suggest that inhibitory connections may be a key factor in the dissociation between LFP and spiking activity, which may contribute substantially to the close coupling between the BOLD signal and synchronized synaptic activity in the brain.

A new method to infer higher-order spike correlations from membrane potentials

What is the role of higher-order spike correlations for neuronal information processing? Common data analysis methods to address this question are devised for the application to spike recordings from multiple single neurons. Here, we present a new method which evaluates the subthreshold membrane potential fluctuations of one neuron, and infers higher-order correlations among the neurons that constitute its presynaptic population. This has two important advantages: Very large populations of up to several thousands of neurons can be studied, and the spike sorting is obsolete. Moreover, this new approach truly emphasizes the functional aspects of higher-order statistics, since we infer exactly those correlations which are seen by a neuron. Our approach is to represent the subthreshold membrane potential fluctuations as presynaptic activity filtered with a fixed kernel, as it would be the case for a leaky integrator neuron model. This allows us to adapt the recently proposed method CuBIC (cumulant based inference of higher-order correlations from the population spike count; Staude et al., J Comput Neurosci 29(1-2):327-350, 2010c) with which the maximal order of correlation can be inferred. By numerical simulation we show that our new method is reasonably sensitive to weak higher-order correlations, and that only short stretches of membrane potential are required for their reliable inference. Finally, we demonstrate its remarkable robustness against violations of the simplifying assumptions made for its construction, and discuss how it can be employed to analyze in vivo intracellular recordings of membrane potentials.

Combining pharmacology and whole-cell patch recording from CNS neurons, in vivo

Whole-cell patch neurophysiology and pharmacological manipulations have provided unprecedented insight into the functions of central neurons, but their combined use has been largely restricted to in vitro preparations. We describe a method for performing whole-cell patch recording and focal application of pharmacological agents in vivo. A key feature of this technique involves iontophoresis of glutamate to establish proximity of drug and recording pipettes. We show data from iontophoresis of glutamate during extracellular and whole-cell recordings made in vivo from auditory neurons in the midbrain of the leopard frog, Rana pipiens, and the effects of blocking GABA(A) receptors while making a whole-cell recording. This methodology should accelerate our understanding of the roles of particular neurotransmitter systems in normal and pathological conditions, and facilitate investigation of the in vivo effects of drugs and the mechanisms underlying computations.

Inter-neuronal correlation distinguishes mechanisms of direction selectivity in cortical circuit models

Direction selectivity is a fundamental physiological property that arises from primary visual cortex (V1) circuitry, yet basic questions of how direction-selective (DS) receptive fields are constructed remain unanswered. We built a set of simple, plausible neuronal circuits that produce DS cells via different mechanisms and tested these circuits to determine how they can be distinguished experimentally. Our models consisted of populations of spiking units representing physiological cell classes ranging from LGN cells to V1 complex DS cells. They differed in network architecture and DS mechanism, including linear summation of non-DS simple-cell inputs or nonlinear pairwise combinations of non-DS inputs. The circuits also varied in the location of the DS time delay and whether the DS interaction was facilitatory or suppressive. We tested the models with visual stimuli often used experimentally, including sinusoidal gratings and flashed bars, and computed shuffle-corrected cross-correlograms (CCGs) of spike trains from pairs of units that would be accessible to extracellular recording. We found that CCGs revealed fundamental features of the DS models, including the location of signal delays in the DS circuit and the sign (facilitatory or suppressive) of DS interactions. We also found that correlation was strongly stimulus-dependent, changing with direction and temporal frequency in a manner that generalized across model architectures. Our models make specific predictions for designing, optimizing, and interpreting electrophysiology experiments aimed at resolving DS circuitry and provide new insights into mechanisms that could underlie stimulus-dependent correlation. The models are available and easy to explore at www.imodel.org.

Neuronal recordings with solid-conductor intracellular nanoelectrodes (SCINEs)

Direct electrical recording of the neuronal transmembrane potential has been crucial to our understanding of the biophysical mechanisms subserving neuronal computation. Existing intracellular recording techniques, however, limit the accuracy and duration of such measurements by changing intracellular biochemistry and/or by damaging the plasma membrane. Here we demonstrate that nanoengineered electrodes can be used to record neuronal transmembrane potentials in brain tissue without causing these physiological perturbations. Using focused ion beam milling, we have fabricated Solid-Conductor Intracellular NanoElectrodes (SCINEs), from conventional tungsten microelectrodes. SCINEs have tips that are <300 nm in diameter for several micrometers, but can be easily handled and can be inserted into brain tissue. Performing simultaneous whole-cell patch recordings, we show that SCINEs can record action potentials (APs) as well as slower, subthreshold neuronal potentials without altering cellular properties. These results show a key role for nanotechnology in the development of new electrical recording techniques in neuroscience.

The beginning of intracellular recording in spinal neurons: facts, reflections, and speculations

Intracellular (IC) recording of action potentials in neurons of the vertebrate central nervous system (CNS) was first reported by John Eccles and two colleagues, Walter Brock and John Coombs, in Dunedin, NZL in 1951/1952 and by Walter Woodbury and Harry Patton in Seattle, WA, USA in 1952. Both groups studied spinal cord neurons of the adult cat. In this review, we discuss the precedents to their notable achievement and reflect and speculate on some of the scientific and personal nuances of their work and its immediate and later impact. We then briefly discuss early achievements in IC recording in the study of CNS neurobiology in other laboratories around the world, and some of the methods that led to enhancement of CNS IC-recording techniques. Our modern understanding of CNS neurophysiology directly emanates from the pioneering endeavors of the five who wrote the seminal 1951/1952 articles.

FM velocity selectivity in the inferior colliculus is inherited from velocity-selective inputs and enhanced by spike threshold

Frequency modulation (FM) is computed from the temporal sequence of activated auditory nerve fibers representing different frequencies. Most studies in the inferior colliculus (IC) have inferred from extracellular recordings that the precise timing of nonselective inputs creates selectivity for FM direction and velocity (Andoni S, Li N, Pollak GD. J Neurosci 27: 4882-4893, 2007; Fuzessery ZM, Richardson MD, Coburn MS. J Neurophysiol 96: 1320-1336, 2006; Gordon M, O'Neill WE. Hear Res 122: 97-108, 1998). We recently reported that two additional mechanisms were more important than input timing for directional selectivity in some IC cells: spike threshold and inputs that were already selective (Gittelman JX, Li N, Pollak GD. J Neurosci 29: 13030-13041, 2009). Here, we show that these same mechanisms, selective inputs and spike threshold, underlie selectivity for FM velocity and intensity. From whole cell recordings in awake bats, we recorded spikes and postsynaptic potentials (PSPs) evoked by downward and upward FMs that swept identical frequencies at different velocities and intensities. To determine the synaptic mechanisms underlying PSP selectivity (relative PSP height), we derived sweep-evoked synaptic conductances. Changing FM velocity or intensity changed conductance timing and size. Modeling indicated that excitatory conductance size contributed more to PSP selectivity than conductance timing, indicating that the number of afferent spikes carried more FM information to the IC than precise spike timing. However, excitation alone produced mostly suprathreshold PSPs. Inhibition reduced absolute PSP heights, without necessarily altering PSP selectivity, thereby rendering some PSPs subthreshold. Spike threshold then sharpened selectivity in the spikes by rectifying the smaller PSPs. This indicates the importance of spike threshold, and that inhibition enhances selectivity via a different mechanism than previously proposed.

Mechanisms of direction selectivity in cat primary visual cortex as revealed by visual adaptation

In contrast to neurons of the lateral geniculate nucleus (LGN), neurons in the primary visual cortex (V1) are selective for the direction of visual motion. Cortical direction selectivity could emerge from the spatiotemporal configuration of inputs from thalamic cells, from intracortical inhibitory interactions, or from a combination of thalamic and intracortical interactions. To distinguish between these possibilities, we studied the effect of adaptation (prolonged visual stimulation) on the direction selectivity of intracellularly recorded cortical neurons. It is known that adaptation selectively reduces the responses of cortical neurons, while largely sparing the afferent LGN input. Adaptation can therefore be used as a tool to dissect the relative contribution of afferent and intracortical interactions to the generation of direction selectivity. In both simple and complex cells, adaptation caused a hyperpolarization of the resting membrane potential (-2.5 mV, simple cells, -0.95 mV complex cells). In simple cells, adaptation in either direction only slightly reduced the visually evoked depolarization; this reduction was similar for preferred and null directions. In complex cells, adaptation strongly reduced visual responses in a direction-dependent manner: the reduction was largest when the stimulus direction matched that of the adapting motion. As a result, adaptation caused changes in the direction selectivity of complex cells: direction selectivity was reduced after preferred direction adaptation and increased after null direction adaptation. Because adaptation in the null direction enhanced direction selectivity rather than reduced it, it seems unlikely that inhibition from the null direction is the primary mechanism for creating direction selectivity.

From prestimulus alpha oscillation to visual-evoked response: an inverted-U function and its attentional modulation

Understanding the relation between prestimulus neural activity and subsequent stimulus processing has become an area of active investigation. Computational modeling, as well as in vitro and in vivo single-unit recordings in animal preparations, have explored mechanisms by which background synaptic activity can influence the responsiveness of cortical neurons to afferent input. How these mechanisms manifest in humans is not well understood. Although numerous EEG/MEG studies have considered the role of prestimulus alpha oscillations in the genesis of visual-evoked potentials, no consensus has emerged, and divergent reports continue to appear. The present work addresses this problem in three stages. First, a theoretical model was developed in which the background synaptic activity and the firing rate of a neural ensemble are related through a sigmoidal function. The derivative of this function, referred to as local gain, has an inverted-U shape and is postulated to be proportional to the trial-by-trial response evoked by a transient stimulus. Second, the theoretical model was extended to noninvasive studies of human visual processing, where the model variables are reinterpreted in terms of ongoing EEG oscillations and event-related potentials. Predictions were derived from the model and tested by recording high-density scalp EEG from healthy volunteers performing a trial-by-trial cued spatial visual attention task. Finally, enhanced stimulus processing by attention was linked to an increase in the overall slope of the sigmoidal function. The commonly observed reduction of alpha magnitude with attention was interpreted as signaling a shift of the underlying neural ensemble toward an optimal excitability state that enables the increase in global gain.

Applicability of White-Noise Techniques to Analyzing Motion Responses

Motion processing in visual neurons is often understood in terms of how they integrate light stimuli in space and time. These integrative properties, known as the spatiotemporal receptive fields (STRFs), are sometimes obtained using white-noise techniques where a continuous random contrast sequence is delivered to each spatial location within the cell's field of view. In contrast, motion stimuli such as moving bars are usually presented intermittently. Here we compare the STRF prediction of a neuron's response to a moving bar with the measured response in second-order interneurons (L-neurons) of dragonfly ocelli (simple eyes). These low-latency neurons transmit sudden changes in intensity and motion information to mediate flight and gaze stabilization reflexes. A white-noise analysis is made of the responses of L-neurons to random bar stimuli delivered either every frame (densely) or intermittently (sparsely) with a temporal sequence matched to the bar motion stimulus. Linear STRFs estimated using the sparse stimulus were significantly better at predicting the responses to moving bars than the STRFs estimated using a traditional dense white-noise stimulus, even when second-order nonlinear terms were added. Our results strongly suggest that visual adaptation significantly modifies the linear STRF properties of L-neurons in dragonfly ocelli during dense white-noise stimulation. We discuss the ability to predict the responses of visual neurons to arbitrary stimuli based on white-noise analysis. We also discuss the likely functional advantages that adaptive receptive field structures provide for stabilizing attitude during hover and forward flight in dragonflies.

How and when the fMRI BOLD signal relates to underlying neural activity: the danger in dissociation

Functional magnetic resonance imaging (fMRI) has become the dominant means of measuring behavior-related neural activity in the human brain. Yet the relation between the blood oxygen-level dependent (BOLD) signal and underlying neural activity remains an open and actively researched question. A widely accepted model, established for sensory neo-cortex, suggests that the BOLD signal reflects peri-synaptic activity in the form of the local field potential rather than the spiking rate of individual neurons. Several recent experimental results, however, suggest situations in which BOLD, spiking, and the local field potential dissociate. Two different models are discussed, based on the literature reviewed to account for this dissociation, a circuitry-based and vascular-based explanation. Both models are found to account for existing data under some testing situations and in certain brain regions. Because both the vascular and local circuitry-based explanations challenge the BOLD-LFP coupling model, these models provide guidance in predicting when BOLD can be expected to reflect neural processing and when the underlying relation with BOLD may be more complex than a direct correspondence.

Differences in the time course of short-term depression across receptive fields are correlated with directional selectivity in electrosensory neurons

Directional selectivity, in which neurons respond preferentially to one direction of movement ("preferred") over the opposite direction ("null"), is a critical computation that is found in the nervous systems of many animals. Here we show the first experimental evidence for a correlation between differences in short-term depression and direction-selective responses to moving objects. As predicted by quantitative models, the observed differences in the time courses of short-term depression at different locations within receptive fields were correlated with measures of direction selectivity in awake, behaving weakly electric fish (Apteronotus leptorhynchus). Because short-term depression is ubiquitous in the central nervous systems of vertebrate animals, it may be a common mechanism used for the generation of directional selectivity and other spatiotemporal computations.

Temporal-pattern recognition by single neurons in a sensory pathway devoted to social communication behavior

Sensory systems often encode stimulus information into the temporal pattern of action potential activity. However, little is known about how the information contained within these patterns is extracted by postsynaptic neurons. Similar to temporal coding by sensory neurons, social information in mormyrid fish is encoded into the temporal patterning of an electric organ discharge. In the current study, sensitivity to temporal patterns of electrosensory stimuli was found to arise within the midbrain posterior exterolateral nucleus (ELp). Whole-cell patch recordings from ELp neurons in vivo revealed three patterns of interpulse interval (IPI) tuning: low-pass neurons tuned to long intervals, high-pass neurons tuned to short intervals, and bandpass neurons tuned to intermediate intervals. Many neurons within each class also responded preferentially to either increasing or decreasing IPIs. Playback of electric signaling patterns recorded from freely behaving fish revealed that the IPI and direction tuning of ELp neurons resulted in selective responses to particular social communication displays characterized by distinct IPI patterns. The postsynaptic potential responses of many neurons indicated a combination of excitatory and inhibitory synaptic input, and the IPI tuning of ELp neurons was directly related to rate-dependent changes in the direction and amplitude of postsynaptic potentials. These results suggest that differences in the dynamics of short-term synaptic plasticity in excitatory and inhibitory pathways may tune central sensory neurons to particular temporal patterns of presynaptic activity. This may represent a general mechanism for the processing of behaviorally relevant stimulus information encoded into temporal patterns of activity by sensory neurons.

Intracortical connections are not required for oscillatory activity in the visual cortex

Synchronized oscillatory discharge in the visual cortex has been proposed to underlie the linking of retinotopically disparate features into perceptually coherent objects. These proposals have largely relied on the premise that the oscillations arise from intracortical circuitry. However, strong oscillations within both the retina and the lateral geniculate nucleus (LGN) have been reported recently. To evaluate the possibility that cortical oscillations arise from peripheral pathways, we have developed two plausible models of single cell oscillatory discharge that specifically exclude intracortical networks. In the first model, cortical oscillatory discharge near 50 Hz in frequency arises from the integration of signals from strongly oscillatory cells within the LGN. The model also predicts the incidence of 50-Hz oscillatory cells within the cortex. Oscillatory discharge around 30 Hz is explained in a second model by the presence of intrinsically oscillatory cells within cortical layer 5. Both models generate spike trains whose power spectra and mean firing rates are in close agreement with experimental observations of simple and complex cells. Considered together, the two models can largely account for the nature and incidence of oscillatory discharge in the cat's visual cortex. The validity of these models is consistent with the possibility that oscillations are generated independently of intracortical interactions. Because these models rely on intrinsic stimulus-independent oscillators within the retina and cortex, the results further suggest that oscillatory activity within the cortex is not necessarily associated with the processing of high-order visual information.

Intracortical connections are not required for oscillatory activity in the visual cortex

arises from the integration of signals from strongly oscillatory cells within the LGN. The model also predicts the incidence of 50-Hz oscillatory cells within the cortex. Oscillatory discharge around 30 Hz is explained in a second model by the presence of intrinsically oscillatory cells within cortical layer 5. Both models generate spike trains whose power spectra and mean firing rates are in close agreement with experimental observations of simple and complex cells. Considered together, the two models can largely account for the nature and incidence of oscillatory discharge in the cat's visual cortex. The validity of these models is consistent with the possibility that oscillations are generated independently of intracortical interactions. Because these models rely on intrinsic stimulus-independent oscillators within the retina and cortex, the results further suggest that oscillatory activity within the cortex is not necessarily associated with the processing of high-order visual information.

The cost of linearization

Linear additivity of synaptic input is a pervasive assumption in computational neuroscience, and previously Bernander et al. (Journal of Neurophysiology 72:2743-2753, 1994) point out that the sublinear additivity of a passive neuronal model can be linearized with voltage-dependent currents. Here we re-examine this perspective in light of more recent findings and issues. Based on in vivo intracellular recordings, three voltage-dependent conductances seem to be of interest for pyramidal cells of the forebrain: two of them are amplifying, I ( NaP ) and I ( h ); and one of them is attenuating, I ( A ). Based on particular I-V characteristics reported in the literature, each of these three voltage-dependent currents linearizes a particular range of synaptic excitation. Computational simulations use a steady-state, one-compartment model. They establish maximal linear ranges, where supralinear effects-due to adding too much of any one conductance-limit these ranges. Specific, carefully selected pairwise combinations of these currents can linearize larger ranges than either current alone. In terms of parameters, the steady-state I-V characteristics of each current are critical. On the other hand, the relationships between the results here and resting conductance to ground, synaptic conductance, and number of active synapses are simple and easily scaled; thus in regard to these three latter dependences, the results here are easily generalized. Finally, to improve our understanding of evolved function, the relative metabolic costs of linearization are quantified. In one case, there is a clear preference arising from this cost consideration (a particular I ( h ), I ( NaP ) pairing is less costly compared to a particular I ( A ), I ( NaP ) pairing that produces an equivalent, linearized range). However in other cases, a preference will depend on the required range; but in any event, the largest linearized range observed here (28 mV), from a combination of I ( h ) and I ( A ), is significantly more costly than the 20 mV range that the I ( h ), I ( NaP ) pair produces.

Facilitatory mechanisms underlying selectivity for the direction and rate of frequency modulated sweeps in the auditory cortex

Neurons selective for frequency modulated (FM) sweeps are common in auditory systems across different vertebrate groups and may underlie representation of species-specific vocalizations. Studies on mechanisms of FM sweep selectivity have primarily focused on sideband inhibition. Here, we present the first evidence for facilitatory mechanisms of FM sweep selectivity. Facilitatory interactions were found in 46 of 264 (17%) neurons tuned in the echolocation range (25-60 kHz) in the auditory cortex of the pallid bat. These neurons respond poorly to individual tones but are facilitated by combinations of tones with specific spectral and temporal intervals. Facilitation neurons show remarkable sensitivity to sub-millisecond differences in time delays between the two tones. Interestingly, the range of delays eliciting facilitation is not fixed but varies systematically with frequency difference between the two tones. Properties of facilitation strongly predict selectivity for the direction and rate of FM sweeps. Together with previous studies, there appear to be at least three mechanisms underlying FM rate and direction selectivity: sideband inhibition, duration tuning, and facilitation. Interestingly, similar mechanisms underlie direction and velocity tuning in the visual system, suggesting the evolution of similar computations across sensory systems to process dynamic sensory stimuli.

Mechanisms of long-interval selectivity in midbrain auditory neurons: roles of excitation, inhibition, and plasticity

Stereotyped intervals between successive sound pulses characterize the acoustic signals of anurans and other organisms and provide critical information to receivers. One class of midbrain neuron responds selectively when pulses are repeated at slow rates (long intervals). To examine the mechanisms that underlie long-interval selectivity, we made whole cell recordings, in vivo, from neurons in the anuran inferior colliculus (anuran IC). In most cases, long-pass interval selectivity appeared to arise from interplay between excitation and inhibition; in approximately 25% of these cases, the delayed inhibition to a pulse overlapped with the excitation to the following pulse at fast pulse repetition rates (PRRs), resulting in a phasic "onset" response. In the remaining cases, inhibition appeared to precede excitation. These neurons did not respond to fast PRRs apparently because delayed excitation to a pulse overlapped with the inhibition to the following pulse. These results suggest that the relative timing of inhibition and excitation govern differences in the response properties of these two cell types. Loading cells with cesium increased their responses to fast AM rates, supporting a role for inhibition in long-interval selectivity. Three cells showed little or no evidence of inhibition and exhibited strong depression of excitation. These findings are discussed in the context of current models for long-pass interval selectivity.