Browsing by Subject "Action Potentials"
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Item Open Access Anatomical identification of extracellularly recorded cells in large-scale multielectrode recordings.(J Neurosci, 2015-03-18) Li, Peter H; Gauthier, Jeffrey L; Schiff, Max; Sher, Alexander; Ahn, Daniel; Field, Greg D; Greschner, Martin; Callaway, Edward M; Litke, Alan M; Chichilnisky, EJThis study combines for the first time two major approaches to understanding the function and structure of neural circuits: large-scale multielectrode recordings, and confocal imaging of labeled neurons. To achieve this end, we develop a novel approach to the central problem of anatomically identifying recorded cells, based on the electrical image: the spatiotemporal pattern of voltage deflections induced by spikes on a large-scale, high-density multielectrode array. Recordings were performed from identified ganglion cell types in the macaque retina. Anatomical images of cells in the same preparation were obtained using virally transfected fluorescent labeling or by immunolabeling after fixation. The electrical image was then used to locate recorded cell somas, axon initial segments, and axon trajectories, and these signatures were used to identify recorded cells. Comparison of anatomical and physiological measurements permitted visualization and physiological characterization of numerically dominant ganglion cell types with high efficiency in a single preparation.Item Open Access Auditory signals evolve from hybrid- to eye-centered coordinates in the primate superior colliculus.(Journal of neurophysiology, 2012-07) Lee, Jungah; Groh, Jennifer MVisual and auditory spatial signals initially arise in different reference frames. It has been postulated that auditory signals are translated from a head-centered to an eye-centered frame of reference compatible with the visual spatial maps, but, to date, only various forms of hybrid reference frames for sound have been identified. Here, we show that the auditory representation of space in the superior colliculus involves a hybrid reference frame immediately after the sound onset but evolves to become predominantly eye centered, and more similar to the visual representation, by the time of a saccade to that sound. Specifically, during the first 500 ms after the sound onset, auditory response patterns (N = 103) were usually neither head nor eye centered: 64% of neurons showed such a hybrid pattern, whereas 29% were more eye centered and 8% were more head centered. This differed from the pattern observed for visual targets (N = 156): 86% were eye centered, <1% were head centered, and only 13% exhibited a hybrid of both reference frames. For auditory-evoked activity observed within 20 ms of the saccade (N = 154), the proportion of eye-centered response patterns increased to 69%, whereas the hybrid and head-centered response patterns dropped to 30% and <1%, respectively. This pattern approached, although did not quite reach, that observed for saccade-related activity for visual targets: 89% were eye centered, 11% were hybrid, and <1% were head centered (N = 162). The plainly eye-centered visual response patterns and predominantly eye-centered auditory motor response patterns lie in marked contrast to our previous study of the intraparietal cortex, where both visual and auditory sensory and motor-related activity used a predominantly hybrid reference frame (Mullette-Gillman et al. 2005, 2009). Our present findings indicate that auditory signals are ultimately translated into a reference frame roughly similar to that used for vision, but suggest that such signals might emerge only in motor areas responsible for directing gaze to visual and auditory stimuli.Item Open Access Bistability and up/down state alternations in inhibition-dominated randomly connected networks of LIF neurons.(Scientific reports, 2017-09-20) Tartaglia, Elisa M; Brunel, NicolasElectrophysiological recordings in cortex in vivo have revealed a rich variety of dynamical regimes ranging from irregular asynchronous states to a diversity of synchronized states, depending on species, anesthesia, and external stimulation. The average population firing rate in these states is typically low. We study analytically and numerically a network of sparsely connected excitatory and inhibitory integrate-and-fire neurons in the inhibition-dominated, low firing rate regime. For sufficiently high values of the external input, the network exhibits an asynchronous low firing frequency state (L). Depending on synaptic time constants, we show that two scenarios may occur when external inputs are decreased: (1) the L state can destabilize through a Hopf bifucation as the external input is decreased, leading to synchronized oscillations spanning d δ to β frequencies; (2) the network can reach a bistable region, between the low firing frequency network state (L) and a quiescent one (Q). Adding an adaptation current to excitatory neurons leads to spontaneous alternations between L and Q states, similar to experimental observations on UP and DOWN states alternations.Item Open Access Burst-Dependent Bidirectional Plasticity in the Cerebellum Is Driven by Presynaptic NMDA Receptors.(Cell reports, 2016-04) Bouvier, Guy; Higgins, David; Spolidoro, Maria; Carrel, Damien; Mathieu, Benjamin; Léna, Clément; Dieudonné, Stéphane; Barbour, Boris; Brunel, Nicolas; Casado, MarianoNumerous studies have shown that cerebellar function is related to the plasticity at the synapses between parallel fibers and Purkinje cells. How specific input patterns determine plasticity outcomes, as well as the biophysics underlying plasticity of these synapses, remain unclear. Here, we characterize the patterns of activity that lead to postsynaptically expressed LTP using both in vivo and in vitro experiments. Similar to the requirements of LTD, we find that high-frequency bursts are necessary to trigger LTP and that this burst-dependent plasticity depends on presynaptic NMDA receptors and nitric oxide (NO) signaling. We provide direct evidence for calcium entry through presynaptic NMDA receptors in a subpopulation of parallel fiber varicosities. Finally, we develop and experimentally verify a mechanistic plasticity model based on NO and calcium signaling. The model reproduces plasticity outcomes from data and predicts the effect of arbitrary patterns of synaptic inputs on Purkinje cells, thereby providing a unified description of plasticity.Item Open Access Cell type-specific changes in retinal ganglion cell function induced by rod death and cone reorganization in rats.(Journal of neurophysiology, 2017-07) Yu, Wan-Qing; Grzywacz, Norberto M; Lee, Eun-Jin; Field, Greg DWe have determined the impact of rod death and cone reorganization on the spatiotemporal receptive fields (RFs) and spontaneous activity of distinct retinal ganglion cell (RGC) types. We compared RGC function between healthy and retinitis pigmentosa (RP) model rats (S334ter-3) at a time when nearly all rods were lost but cones remained. This allowed us to determine the impact of rod death on cone-mediated visual signaling, a relevant time point because the diagnosis of RP frequently occurs when patients are nightblind but daytime vision persists. Following rod death, functionally distinct RGC types persisted; this indicates that parallel processing of visual input remained largely intact. However, some properties of cone-mediated responses were altered ubiquitously across RGC types, such as prolonged temporal integration and reduced spatial RF area. Other properties changed in a cell type-specific manner, such as temporal RF shape (dynamics), spontaneous activity, and direction selectivity. These observations identify the extent of functional remodeling in the retina following rod death but before cone loss. They also indicate new potential challenges to restoring normal vision by replacing lost rod photoreceptors.NEW & NOTEWORTHY This study provides novel and therapeutically relevant insights to retinal function following rod death but before cone death. To determine changes in retinal output, we used a large-scale multielectrode array to simultaneously record from hundreds of retinal ganglion cells (RGCs). These recordings of large-scale neural activity revealed that following the death of all rods, functionally distinct RGCs remain. However, the receptive field properties and spontaneous activity of these RGCs are altered in a cell type-specific manner.Item Open Access Cerebellar learning using perturbations.(eLife, 2018-11-12) Bouvier, Guy; Aljadeff, Johnatan; Clopath, Claudia; Bimbard, Célian; Ranft, Jonas; Blot, Antonin; Nadal, Jean-Pierre; Brunel, Nicolas; Hakim, Vincent; Barbour, BorisThe cerebellum aids the learning of fast, coordinated movements. According to current consensus, erroneously active parallel fibre synapses are depressed by complex spikes signalling movement errors. However, this theory cannot solve the credit assignment problem of processing a global movement evaluation into multiple cell-specific error signals. We identify a possible implementation of an algorithm solving this problem, whereby spontaneous complex spikes perturb ongoing movements, create eligibility traces and signal error changes guiding plasticity. Error changes are extracted by adaptively cancelling the average error. This framework, stochastic gradient descent with estimated global errors (SGDEGE), predicts synaptic plasticity rules that apparently contradict the current consensus but were supported by plasticity experiments in slices from mice under conditions designed to be physiological, highlighting the sensitivity of plasticity studies to experimental conditions. We analyse the algorithm's convergence and capacity. Finally, we suggest SGDEGE may also operate in the basal ganglia.Item Open Access Computational inference of neural information flow networks.(PLoS Comput Biol, 2006-11-24) Smith, V Anne; Yu, Jing; Smulders, Tom V; Hartemink, Alexander J; Jarvis, Erich DDetermining how information flows along anatomical brain pathways is a fundamental requirement for understanding how animals perceive their environments, learn, and behave. Attempts to reveal such neural information flow have been made using linear computational methods, but neural interactions are known to be nonlinear. Here, we demonstrate that a dynamic Bayesian network (DBN) inference algorithm we originally developed to infer nonlinear transcriptional regulatory networks from gene expression data collected with microarrays is also successful at inferring nonlinear neural information flow networks from electrophysiology data collected with microelectrode arrays. The inferred networks we recover from the songbird auditory pathway are correctly restricted to a subset of known anatomical paths, are consistent with timing of the system, and reveal both the importance of reciprocal feedback in auditory processing and greater information flow to higher-order auditory areas when birds hear natural as opposed to synthetic sounds. A linear method applied to the same data incorrectly produces networks with information flow to non-neural tissue and over paths known not to exist. To our knowledge, this study represents the first biologically validated demonstration of an algorithm to successfully infer neural information flow networks.Item Open Access Corollary discharge across the animal kingdom.(Nat Rev Neurosci, 2008-08) Crapse, Trinity B; Sommer, Marc AOur movements can hinder our ability to sense the world. Movements can induce sensory input (for example, when you hit something) that is indistinguishable from the input that is caused by external agents (for example, when something hits you). It is critical for nervous systems to be able to differentiate between these two scenarios. A ubiquitous strategy is to route copies of movement commands to sensory structures. These signals, which are referred to as corollary discharge (CD), influence sensory processing in myriad ways. Here we review the CD circuits that have been uncovered by neurophysiological studies and suggest a functional taxonomic classification of CD across the animal kingdom. This broad understanding of CD circuits lays the groundwork for more challenging studies that combine neurophysiology and psychophysics to probe the role of CD in perception.Item Unknown Cortical dynamics during naturalistic sensory stimulations: experiments and models.(Journal of physiology, Paris, 2011-01) Mazzoni, Alberto; Brunel, Nicolas; Cavallari, Stefano; Logothetis, Nikos K; Panzeri, StefanoWe report the results of our experimental and theoretical investigations of the neural response dynamics in primary visual cortex (V1) during naturalistic visual stimulation. We recorded Local Field Potentials (LFPs) and spiking activity from V1 of anaesthetized macaques during binocular presentation of Hollywood color movies. We analyzed these recordings with information theoretic methods, and found that visual information was encoded mainly by two bands of LFP responses: the network fluctuations measured by the phase and power of low-frequency (less than 12 Hz) LFPs; and fast gamma-range (50-100 Hz) oscillations. Both the power and phase of low frequency LFPs carried information largely complementary to that carried by spikes, whereas gamma range oscillations carried information largely redundant to that of spikes. To interpret these results within a quantitative theoretical framework, we then simulated a sparsely connected recurrent network of excitatory and inhibitory neurons receiving slowly varying naturalistic inputs, and we determined how the LFPs generated by the network encoded information about the inputs. We found that this simulated recurrent network reproduced well the experimentally observed dependency of LFP information upon frequency. This network encoded the overall strength of the input into the power of gamma-range oscillations generated by inhibitory-excitatory neural interactions, and encoded slow variations in the input by entraining the network LFP at the corresponding frequency. This dynamical behavior accounted quantitatively for the independent information carried by high and low frequency LFPs, and for the experimentally observed cross-frequency coupling between phase of slow LFPs and the power of gamma LFPs. We also present new results showing that the model's dynamics also accounted for the extra visual information that the low-frequency LFP phase of spike firing carries beyond that carried by spike rates. Overall, our results suggest biological mechanisms by which cortex can multiplex information about naturalistic sensory environments.Item Unknown Drivers from the deep: the contribution of collicular input to thalamocortical processing.(Prog Brain Res, 2005) Wurtz, Robert H; Sommer, Marc A; Cavanaugh, JamesA traditional view of the thalamus is that it is a relay station which receives sensory input and conveys this information to cortex. This sensory input determines most of the properties of first order thalamic neurons, and so is said to drive, rather than modulate, these neurons. This holds as a rule for first order thalamic nuclei, but in contrast, higher order thalamic nuclei receive much of their driver input back from cerebral cortex. In addition, higher order thalamic neurons receive inputs from subcortical movement-related centers. In the terminology popularized from studies of the sensory system, can we consider these ascending motor inputs to thalamus from subcortical structures to be modulators, subtly influencing the activity of their target neurons, or drivers, dictating the activity of their target neurons? This chapter summarizes relevant evidence from neuronal recording, inactivation, and stimulation of pathways projecting from the superior colliculus through thalamus to cerebral cortex. The study concludes that many inputs to the higher order nuclei of the thalamus from subcortical oculomotor areas - from the superior colliculus and probably other midbrain and pontine regions - should be regarded as motor drivers analogous to the sensory drivers at the first order thalamic nuclei. These motor drivers at the thalamus are viewed as being at the top of a series of feedback loops that provide information on impending actions, just as sensory drivers provide information about the external environment.Item Unknown Effects of frequency-dependent membrane capacitance on neural excitability.(Journal of neural engineering, 2015-10) Howell, Bryan; Medina, Leonel E; Grill, Warren MObjective
Models of excitable cells consider the membrane specific capacitance as a ubiquitous and constant parameter. However, experimental measurements show that the membrane capacitance declines with increasing frequency, i.e., exhibits dispersion. We quantified the effects of frequency-dependent membrane capacitance, c(f), on the excitability of cells and nerve fibers across the frequency range from dc to hundreds of kilohertz.Approach
We implemented a model of c(f) using linear circuit elements, and incorporated it into several models of neurons with different channel kinetics: the Hodgkin-Huxley model of an unmyelinated axon, the McIntyre-Richardson-Grill (MRG) of a mammalian myelinated axon, and a model of a cortical neuron from prefrontal cortex (PFC). We calculated thresholds for excitation and kHz frequency conduction block, the conduction velocity, recovery cycle, strength-distance relationship and firing rate.Main results
The impact of c(f) on activation thresholds depended on the stimulation waveform and channel kinetics. We observed no effect using rectangular pulse stimulation, and a reduction for frequencies of 10 kHz and above using sinusoidal signals only for the MRG model. c(f) had minimal impact on the recovery cycle and the strength-distance relationship, whereas the conduction velocity increased by up to 7.9% and 1.7% for myelinated and unmyelinated fibers, respectively. Block thresholds declined moderately when incorporating c(f), the effect was greater at higher frequencies, and the maximum reduction was 11.5%. Finally, c(f) marginally altered the firing pattern of a model of a PFC cell, reducing the median interspike interval by less than 2%.Significance
This is the first comprehensive analysis of the effects of dispersive capacitance on neural excitability, and as the interest on stimulation with kHz signals gains more attention, it defines the regions over which frequency-dependent membrane capacitance, c(f), should be considered.Item Unknown Estimating network parameters from combined dynamics of firing rate and irregularity of single neurons.(Journal of neurophysiology, 2011-01) Hamaguchi, Kosuke; Riehle, Alexa; Brunel, NicolasHigh firing irregularity is a hallmark of cortical neurons in vivo, and modeling studies suggest a balance of excitation and inhibition is necessary to explain this high irregularity. Such a balance must be generated, at least partly, from local interconnected networks of excitatory and inhibitory neurons, but the details of the local network structure are largely unknown. The dynamics of the neural activity depends on the local network structure; this in turn suggests the possibility of estimating network structure from the dynamics of the firing statistics. Here we report a new method to estimate properties of the local cortical network from the instantaneous firing rate and irregularity (CV(2)) under the assumption that recorded neurons are a part of a randomly connected sparse network. The firing irregularity, measured in monkey motor cortex, exhibits two features; many neurons show relatively stable firing irregularity in time and across different task conditions; the time-averaged CV(2) is widely distributed from quasi-regular to irregular (CV(2) = 0.3-1.0). For each recorded neuron, we estimate the three parameters of a local network [balance of local excitation-inhibition, number of recurrent connections per neuron, and excitatory postsynaptic potential (EPSP) size] that best describe the dynamics of the measured firing rates and irregularities. Our analysis shows that optimal parameter sets form a two-dimensional manifold in the three-dimensional parameter space that is confined for most of the neurons to the inhibition-dominated region. High irregularity neurons tend to be more strongly connected to the local network, either in terms of larger EPSP and inhibitory PSP size or larger number of recurrent connections, compared with the low irregularity neurons, for a given excitatory/inhibitory balance. Incorporating either synaptic short-term depression or conductance-based synapses leads many low CV(2) neurons to move to the excitation-dominated region as well as to an increase of EPSP size.Item Unknown Evaluation and resolution of many challenges of neural spike sorting: a new sorter.(Journal of neurophysiology, 2021-12) Hall, Nathan J; Herzfeld, David J; Lisberger, Stephen GWe evaluate existing spike sorters and present a new one that resolves many sorting challenges. The new sorter, called "full binary pursuit" or FBP, comprises multiple steps. First, it thresholds and clusters to identify the waveforms of all unique neurons in the recording. Second, it uses greedy binary pursuit to optimally assign all the spike events in the original voltages to separable neurons. Third, it resolves spike events that are described more accurately as the superposition of spikes from two other neurons. Fourth, it resolves situations where the recorded neurons drift in amplitude or across electrode contacts during a long recording session. Comparison with other sorters on ground-truth data sets reveals many of the failure modes of spike sorting. We examine overall spike sorter performance in ground-truth data sets and suggest postsorting analyses that can improve the veracity of neural analyses by minimizing the intrusion of failure modes into analysis and interpretation of neural data. Our analysis reveals the tradeoff between the number of channels a sorter can process, speed of sorting, and some of the failure modes of spike sorting. FBP works best on data from 32 channels or fewer. It trades speed and number of channels for avoidance of specific failure modes that would be challenges for some use cases. We conclude that all spike sorting algorithms studied have advantages and shortcomings, and the appropriate use of a spike sorter requires a detailed assessment of the data being sorted and the experimental goals for analyses.NEW & NOTEWORTHY Electrophysiological recordings from multiple neurons across multiple channels pose great difficulty for spike sorting of single neurons. We propose methods that improve the ability to determine the number of individual neurons present in a recording and resolve near-simultaneous spike events from single neurons. We use ground-truth data sets to demonstrate the pros and cons of several current sorting algorithms and suggest strategies for determining the accuracy of spike sorting when ground-truth data are not available.Item Unknown From spiking neuron models to linear-nonlinear models.(PLoS Comput Biol, 2011-01-20) Ostojic, Srdjan; Brunel, NicolasNeurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN) cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF), exponential integrate-and-fire (EIF) and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates.Item Unknown Frontal eye field neurons assess visual stability across saccades.(J Neurosci, 2012-02-22) Crapse, Trinity B; Sommer, Marc AThe image on the retina may move because the eyes move, or because something in the visual scene moves. The brain is not fooled by this ambiguity. Even as we make saccades, we are able to detect whether visual objects remain stable or move. Here we test whether this ability to assess visual stability across saccades is present at the single-neuron level in the frontal eye field (FEF), an area that receives both visual input and information about imminent saccades. Our hypothesis was that neurons in the FEF report whether a visual stimulus remains stable or moves as a saccade is made. Monkeys made saccades in the presence of a visual stimulus outside of the receptive field. In some trials, the stimulus remained stable, but in other trials, it moved during the saccade. In every trial, the stimulus occupied the center of the receptive field after the saccade, thus evoking a reafferent visual response. We found that many FEF neurons signaled, in the strength and timing of their reafferent response, whether the stimulus had remained stable or moved. Reafferent responses were tuned for the amount of stimulus translation, and, in accordance with human psychophysics, tuning was better (more prevalent, stronger, and quicker) for stimuli that moved perpendicular, rather than parallel, to the saccade. Tuning was sometimes present as well for nonspatial transaccadic changes (in color, size, or both). Our results indicate that FEF neurons evaluate visual stability during saccades and may be general purpose detectors of transaccadic visual change.Item Unknown High-sensitivity rod photoreceptor input to the blue-yellow color opponent pathway in macaque retina.(Nat Neurosci, 2009-09) Field, Greg D; Greschner, Martin; Gauthier, Jeffrey L; Rangel, Carolina; Shlens, Jonathon; Sher, Alexander; Marshak, David W; Litke, Alan M; Chichilnisky, EJSmall bistratified cells (SBCs) in the primate retina carry a major blue-yellow opponent signal to the brain. We found that SBCs also carry signals from rod photoreceptors, with the same sign as S cone input. SBCs exhibited robust responses under low scotopic conditions. Physiological and anatomical experiments indicated that this rod input arose from the AII amacrine cell-mediated rod pathway. Rod and cone signals were both present in SBCs at mesopic light levels. These findings have three implications. First, more retinal circuits may multiplex rod and cone signals than were previously thought to, efficiently exploiting the limited number of optic nerve fibers. Second, signals from AII amacrine cells may diverge to most or all of the approximately 20 retinal ganglion cell types in the peripheral primate retina. Third, rod input to SBCs may be the substrate for behavioral biases toward perception of blue at mesopic light levels.Item Unknown Induced pluripotent stem cell-derived cardiac progenitors differentiate to cardiomyocytes and form biosynthetic tissues.(PLoS One, 2013) Christoforou, Nicolas; Liau, Brian; Chakraborty, Syandan; Chellapan, Malathi; Bursac, Nenad; Leong, Kam WThe mammalian heart has little capacity to regenerate, and following injury the myocardium is replaced by non-contractile scar tissue. Consequently, increased wall stress and workload on the remaining myocardium leads to chamber dilation, dysfunction, and heart failure. Cell-based therapy with an autologous, epigenetically reprogrammed, and cardiac-committed progenitor cell source could potentially reverse this process by replacing the damaged myocardium with functional tissue. However, it is unclear whether cardiac progenitor cell-derived cardiomyocytes are capable of attaining levels of structural and functional maturity comparable to that of terminally-fated cardiomyocytes. Here, we first describe the derivation of mouse induced pluripotent stem (iPS) cells, which once differentiated allow for the enrichment of Nkx2-5(+) cardiac progenitors, and the cardiomyocyte-specific expression of the red fluorescent protein. We show that the cardiac progenitors are multipotent and capable of differentiating into endothelial cells, smooth muscle cells and cardiomyocytes. Moreover, cardiac progenitor selection corresponds to cKit(+) cell enrichment, while cardiomyocyte cell-lineage commitment is concomitant with dual expression of either cKit/Flk1 or cKit/Sca-1. We proceed to show that the cardiac progenitor-derived cardiomyocytes are capable of forming electrically and mechanically coupled large-scale 2D cell cultures with mature electrophysiological properties. Finally, we examine the cell progenitors' ability to form electromechanically coherent macroscopic tissues, using a physiologically relevant 3D culture model and demonstrate that following long-term culture the cardiomyocytes align, and form robust electromechanical connections throughout the volume of the biosynthetic tissue construct. We conclude that the iPS cell-derived cardiac progenitors are a robust cell source for tissue engineering applications and a 3D culture platform for pharmacological screening and drug development studies.Item Unknown Inferring learning rules from distributions of firing rates in cortical neurons.(Nat Neurosci, 2015-12) Lim, Sukbin; McKee, Jillian L; Woloszyn, Luke; Amit, Yali; Freedman, David J; Sheinberg, David L; Brunel, NicolasInformation about external stimuli is thought to be stored in cortical circuits through experience-dependent modifications of synaptic connectivity. These modifications of network connectivity should lead to changes in neuronal activity as a particular stimulus is repeatedly encountered. Here we ask what plasticity rules are consistent with the differences in the statistics of the visual response to novel and familiar stimuli in inferior temporal cortex, an area underlying visual object recognition. We introduce a method that allows one to infer the dependence of the presumptive learning rule on postsynaptic firing rate, and we show that the inferred learning rule exhibits depression for low postsynaptic rates and potentiation for high rates. The threshold separating depression from potentiation is strongly correlated with both mean and s.d. of the firing rate distribution. Finally, we show that network models implementing a rule extracted from data show stable learning dynamics and lead to sparser representations of stimuli.Item Unknown Microcircuits for attention.(Neuron, 2007-07-05) Sommer, Marc AResearchers who study the neuronal basis of cognition face a paradox. If they extract the brain, its cognitive functions cannot be assessed. On the other hand, the brain's microcircuits are difficult to study in the intact animal. In this issue of Neuron, Mitchell et al. make use of a promising approach based on waveform analysis to reveal new details about neuronal interactions during visual attention.Item Unknown Modulation of network excitability by persistent activity: how working memory affects the response to incoming stimuli.(PLoS Comput Biol, 2015-02) Tartaglia, Elisa M; Brunel, Nicolas; Mongillo, GianluigiPersistent activity and match effects are widely regarded as neuronal correlates of short-term storage and manipulation of information, with the first serving active maintenance and the latter supporting the comparison between memory contents and incoming sensory information. The mechanistic and functional relationship between these two basic neurophysiological signatures of working memory remains elusive. We propose that match signals are generated as a result of transient changes in local network excitability brought about by persistent activity. Neurons more active will be more excitable, and thus more responsive to external inputs. Accordingly, network responses are jointly determined by the incoming stimulus and the ongoing pattern of persistent activity. Using a spiking model network, we show that this mechanism is able to reproduce most of the experimental phenomenology of match effects as exposed by single-cell recordings during delayed-response tasks. The model provides a unified, parsimonious mechanistic account of the main neuronal correlates of working memory, makes several experimentally testable predictions, and demonstrates a new functional role for persistent activity.