Search tips
Search criteria

Results 1-25 (30)

Clipboard (0)

Select a Filter Below

Year of Publication
1.  A genuine layer 4 in motor cortex with prototypical synaptic circuit connectivity 
eLife  null;3:e05422.
The motor cortex (M1) is classically considered an agranular area, lacking a distinct layer 4 (L4). Here, we tested the idea that M1, despite lacking a cytoarchitecturally visible L4, nevertheless possesses its equivalent in the form of excitatory neurons with input–output circuits like those of the L4 neurons in sensory areas. Consistent with this idea, we found that neurons located in a thin laminar zone at the L3/5A border in the forelimb area of mouse M1 have multiple L4-like synaptic connections: excitatory input from thalamus, largely unidirectional excitatory outputs to L2/3 pyramidal neurons, and relatively weak long-range corticocortical inputs and outputs. M1-L4 neurons were electrophysiologically diverse but morphologically uniform, with pyramidal-type dendritic arbors and locally ramifying axons, including branches extending into L2/3. Our findings therefore identify pyramidal neurons in M1 with the expected prototypical circuit properties of excitatory L4 neurons, and question the traditional assumption that motor cortex lacks this layer.
eLife digest
In 1909, a German scientist called Korbinian Brodmann published the first map of the outer layer of the human brain. After staining neurons with a dye and studying the structures of the cells and how they were organized, he realized that he could divide the cortex into 43 numbered regions.
Most Brodmann areas can be divided into a number of horizontal layers, with layer 1 being closest to the surface of the brain. Neurons in the different layers form distinct sets of connections, and the relative thickness of the layers has implications for the function carried out by that area. It is thought, for example, that the motor cortex does not have a layer 4, which suggests that the neural circuitry that controls movement differs from that in charge of vision, hearing, and other functions.
Yamawaki et al. now challenge this view by providing multiple lines of evidence for the existence of layer 4 in the motor cortex in mice. Neurons at the border between layer 3 and layer 5A in the motor cortex possess many of the same properties as the neurons in layer 4 in sensory cortex. In particular, they receive inputs from a brain region called the thalamus, and send outputs to neurons in layers 2 and 3.
Yamawaki et al. go on to characterize some of the properties of the neurons in the putative layer 4 of the motor cortex, finding that they do not look like the specialized ‘stellate’ cells that are found in some other areas of the cortex. Instead, they resemble the ‘pyramidal’ type of neuron that is found in all layers and areas of the cortex.
The discovery that the motor cortex is more similar in its circuit connections to other area of the cortex than previously thought has important implications for our understanding of this region of the brain.
PMCID: PMC4290446  PMID: 25525751
neocortex; thalamocortical; layer 4; pyramidal neuron; microcircuit; mouse
2.  Population rate dynamics and multineuron firing patterns in sensory cortex 
Cortical circuits encode sensory stimuli through the firing of neuronal ensembles, and also produce spontaneous population patterns in the absence of sensory drive. This population activity is often characterized experimentally by the distribution of multineuron “words” (binary firing vectors), and a match between spontaneous and evoked word distributions has been suggested to reflect learning of a probabilistic model of the sensory world. We analyzed multineuron word distributions in sensory cortex of anesthetized rats and cats, and found that they are dominated by fluctuations in population firing rate rather than precise interactions between individual units. Furthermore, cortical word distributions change when brain state shifts, and similar behavior is seen in simulated networks with fixed, random connectivity. Our results suggest that similarity or dissimilarity in multineuron word distributions could primarily reflect similarity or dissimilarity in population firing rate dynamics, and not necessarily the precise interactions between neurons that would indicate learning of sensory features.
PMCID: PMC3520056  PMID: 23197704
3.  High-dimensional cluster analysis with the Masked EM Algorithm 
Neural computation  2014;26(11):2379-2394.
Cluster analysis faces two problems in high dimensions: first, the “curse of dimensionality” that can lead to overfitting and poor generalization performance; and second, the sheer time taken for conventional algorithms to process large amounts of high-dimensional data. We describe a solution to these problems, designed for the application of “spike sorting” for next-generation high channel-count neural probes. In this problem, only a small subset of features provide information about the cluster member-ship of any one data vector, but this informative feature subset is not the same for all data points, rendering classical feature selection ineffective. We introduce a “Masked EM” algorithm that allows accurate and time-efficient clustering of up to millions of points in thousands of dimensions. We demonstrate its applicability to synthetic data, and to real-world high-channel-count spike sorting data.
PMCID: PMC4298163  PMID: 25149694
spike sorting; high-dimensional; clustering algorithm
4.  Laminar-dependent effects of cortical state on auditory cortical spontaneous activity 
Cortical circuits spontaneously generate coordinated activity even in the absence of external inputs. The character of this activity depends on cortical state. We investigated how state affects the organization of spontaneous activity across layers of rat auditory cortex in vivo, using juxtacellular recording of morphologically identified neurons and large-scale electrophysiological recordings. Superficial pyramidal cells (PCs) and putative fast-spiking interneurons (FSs) were consistently suppressed during cortical desynchronization. PCs in deep layers showed heterogeneous responses to desynchronization, with some cells showing increased rates, typically large tufted PCs of high baseline firing rate, but not FSs. Consistent results were found between desynchronization occurring spontaneously in unanesthetized animals, and desynchronization evoked by electrical stimulation of the pedunculopontine tegmental (PPT) nucleus under urethane anesthesia. We hypothesize that reduction in superficial layer firing may enhance the brain's extraction of behaviorally relevant signals from noisy brain activity.
PMCID: PMC3527822  PMID: 23267317
sensory cortex; cell-type; cortical circuit; ensemble recording; slow oscillation
5.  Cortical State Determines Global Variability and Correlations in Visual Cortex 
The Journal of Neuroscience  2015;35(1):170-178.
The response of neurons in sensory cortex to repeated stimulus presentations is highly variable. To investigate the nature of this variability, we compared the spike activity of neurons in the primary visual cortex (V1) of cats with that of their afferents from lateral geniculate nucleus (LGN), in response to similar stimuli. We found variability to be much higher in V1 than in LGN. To investigate the sources of the additional variability, we measured the spiking activity of large V1 populations and found that much of the variability was shared across neurons: the variable portion of the responses of one neuron could be well predicted from the summed activity of the rest of the neurons. Variability thus mostly reflected global fluctuations affecting all neurons. The size and prevalence of these fluctuations, both in responses to stimuli and in ongoing activity, depended on cortical state, being larger in synchronized states than in more desynchronized states. Contrary to previous reports, these fluctuations invested the overall population, regardless of preferred orientation. The global fluctuations substantially increased variability in single neurons and correlations among pairs of neurons. Once this effect was removed, pairwise correlations were reduced and were similar regardless of cortical state. These results highlight the importance of cortical state in controlling cortical operation and can help reconcile previous studies, which differed widely in their estimate of neuronal variability and pairwise correlations.
PMCID: PMC4287140  PMID: 25568112
vision; thalamus; cerebral cortex; neural populations; brain states
6.  State-dependent representation of amplitude-modulated noise stimuli in rat auditory cortex 
Cortical responses can vary greatly between repeated presentations of an identical stimulus. Here we report that both trial-to-trial variability and faithfulness of auditory cortical stimulus representations depend critically on brain state. A frozen amplitude-modulated white noise stimulus was repeatedly presented while recording neuronal populations and local field potentials (LFPs) in auditory cortex of urethane-anesthetized rats. An information-theoretic measure was used to predict neuronal spiking activity from either the stimulus envelope or simultaneously recorded LFP. Evoked LFPs and spiking more faithfully followed high-frequency temporal modulations when the cortex was in a “desynchronized” state. In the “synchronized” state, neural activity was poorly predictable from the stimulus envelope, but the spiking of individual neurons could still be predicted from the ongoing LFP. Our results suggest that although auditory cortical activity remains coordinated as a population in the synchronized state, the ability of continuous auditory stimuli to control this activity is greatly diminished.
PMCID: PMC3099304  PMID: 21525282
information theory; auditory system; brain state; desynchronized; synchronized
7.  “CLASSIC NMR”: An In-Situ NMR Strategy for Mapping the Time-Evolution of Crystallization Processes by Combined Liquid-State and Solid-State Measurements** 
A new in-situ NMR strategy (termed CLASSIC NMR) for mapping the evolution of crystallization processes is reported, involving simultaneous measurement of both liquid-state and solid-state NMR spectra as a function of time. This combined strategy allows complementary information to be obtained on the evolution of both the solid and liquid phases during the crystallization process. In particular, as crystallization proceeds (monitored by solid-state NMR), the solution state becomes more dilute, leading to changes in solution-state speciation and the modes of molecular aggregation in solution, which are monitored by liquid-state NMR. The CLASSIC NMR experiment is applied here to yield new insights into the crystallization of m-aminobenzoic acid.
PMCID: PMC4227553  PMID: 25044662
crystal growth; in-situ studies; solid-state nmr spectroscopy; time-dependent processes
8.  Laminar structure of spontaneous and sensory-evoked population activity in auditory cortex 
Neuron  2009;64(3):404-418.
Spontaneous activity plays an important role in the function of neural circuits. Although many similarities between spontaneous and sensory-evoked neocortical activity have been reported, little is known about consistent differences between them. Here, using simultaneously recorded cortical populations and morphologically identified pyramidal cells, we compare the laminar structure of spontaneous and sensory-evoked population activity in rat auditory cortex. Spontaneous and evoked patterns both exhibited sparse, spatially localized activity in layer 2/3 pyramidal cells, with densely distributed activity in larger layer 5 pyramidal cells and putative interneurons. However, the propagation of spontaneous and evoked activity differed, with spontaneous activity spreading upward from deep layers and slowly across columns, but sensory responses initiating in presumptive thalamorecipient layers, spreading rapidly across columns. The similarity of sparseness patterns for both neural events, and distinct spread of activity may reflect similarity of local processing, and differences in the flow of information through cortical circuits, respectively.
PMCID: PMC2778614  PMID: 19914188
9.  A simple model of cortical dynamics explains variability and state-dependence of sensory responses in urethane-anesthetized auditory cortex 
The responses of neocortical cells to sensory stimuli are variable and state-dependent. It has been hypothesized that intrinsic cortical dynamics play an important role in trial-to-trial variability; the precise nature of this dependence, however, is poorly understood. We show here that in auditory cortex of urethane-anesthetized rats, population responses to click stimuli can be quantitatively predicted on a trial-by-trial basis by a simple dynamical system model estimated from spontaneous activity immediately preceding stimulus presentation. Changes in cortical state correspond consistently to changes in model dynamics, reflecting a nonlinear self-exciting system in synchronized states and an approximately linear system in desynchronized states. We propose that the complex and state-dependent pattern of trial-to-trial variability can be explained by a simple principle: that sensory responses are shaped by the same intrinsic dynamics that govern ongoing spontaneous activity.
PMCID: PMC2861166  PMID: 19710313
cortex; dynamics; state; auditory; dynamical system; variability
10.  Population coding of tone stimuli in auditory cortex: dynamic rate vector analysis 
The European journal of neuroscience  2009;30(9):1767-1778.
Neural representations of even temporally unstructured stimuli can show complex temporal dynamics. In many systems, neuronal population codes show “progressive differentiation,” whereby population responses to different stimuli grow further apart during a stimulus presentation. Here we analyzed the response of auditory cortical populations in rats to extended tones. At onset (up to 300 ms), tone responses involved strong excitation of a large number of neurons; during sustained responses (after 500 ms) overall firing rate decreased, but most cells still showed a statistically significant difference in firing rate. Population vector trajectories evoked by different tone frequencies expanded rapidly along an initially similar trajectory in the first tens of ms after tone onset, later diverging to smaller amplitude fixed points corresponding to sustained responses. The angular difference between onset and sustained responses to the same tone was greater than between different tones in the same stimulus epoch. No clear orthogonalization of responses was found with time, and predictability of the stimulus from population activity also decreased during this period compared to onset. The question of whether population activity grew more or less sparse with time depended on the precise mathematical sense given to this term. We conclude that auditory cortical population responses to tones differ from those reported in many other systems, with progressive differentiation not seen for sustained stimuli. Sustained acoustic stimuli are typically not behaviorally salient: we hypothesize that the dynamics we observe may instead allow an animal to maintain a representation of such sounds, at low energetic cost.
PMCID: PMC2861167  PMID: 19840110
11.  Ongoing Network State Controls the Length of Sleep Spindles via Inhibitory Activity 
Neuron  2014;82(6):1367-1379.
Sleep spindles are major transient oscillations of the mammalian brain. Spindles are generated in the thalamus; however, what determines their duration is presently unclear. Here, we measured somatic activity of excitatory thalamocortical (TC) cells together with axonal activity of reciprocally coupled inhibitory reticular thalamic cells (nRTs) and quantified cycle-by-cycle alterations in their firing in vivo. We found that spindles with different durations were paralleled by distinct nRT activity, and nRT firing sharply dropped before the termination of all spindles. Both initial nRT and TC activity was correlated with spindle length, but nRT correlation was more robust. Analysis of spindles evoked by optogenetic activation of nRT showed that spindle probability, but not spindle length, was determined by the strength of the light stimulus. Our data indicate that during natural sleep a dynamically fluctuating thalamocortical network controls the duration of sleep spindles via the major inhibitory element of the circuits, the nRT.
•Coupled excitatory-inhibitory thalamic populations were recorded during spindles•Spindle termination is preceded by a drop in nRT activity•Spindles of different lengths have distinct nRT activity trajectories•Spindle duration is strongly influenced by the initial network state
Barthó et al. record coupled excitatory-inhibitory activity from thalamic populations during sleep spindles showing that ongoing network state controls the length of sleep spindles via the major inhibitory element of the circuit, the nRT.
PMCID: PMC4064116  PMID: 24945776
12.  Integration of visual motion and locomotion in mouse visual cortex 
Nature neuroscience  2013;16(12):1864-1869.
Successful navigation through the world requires accurate estimation of one’s own speed. To derive this estimate, animals integrate visual speed gauged from optic flow and run speed gauged from proprioceptive and locomotor systems. The primary visual cortex (V1) carries signals related to visual speed, and its responses are also affected by run speed. To study how V1 combines these signals during navigation, we recorded from mice that traversed a virtual environment. Nearly half of the V1 neurons were reliably driven by combinations of visual speed and run speed. These neurons performed a weighted sum of the two speeds. The weights were diverse across neurons, and typically positive. As a population, V1 neurons predicted a linear combination of visual and run speed better than visual or run speeds alone. These data indicate that V1 in the mouse participates in a multimodal processing system that integrates visual motion and locomotion during navigation.
PMCID: PMC3926520  PMID: 24185423
13.  Supervised learning with decision margins in pools of spiking neurons 
Learning to categorise sensory inputs by generalising from a few examples whose category is precisely known is a crucial step for the brain to produce appropriate behavioural responses. At the neuronal level, this may be performed by adaptation of synaptic weights under the influence of a training signal, in order to group spiking patterns impinging on the neuron. Here we describe a framework that allows spiking neurons to perform such “supervised learning”, using principles similar to the Support Vector Machine, a well-established and robust classifier. Using a hinge-loss error function, we show that requesting a margin similar to that of the SVM improves performance on linearly non-separable problems. Moreover, we show that using pools of neurons to discriminate categories can also increase the performance by sharing the load among neurons.
Electronic supplementary material
The online version of this article (doi:10.1007/s10827-014-0505-9) contains supplementary material, which is available to authorized users.
PMCID: PMC4159595  PMID: 24862859
Keywords; Supervised learning; Spiking neurons; Tempotron; Support vector machine
14.  Sleep and the single neuron: the role of global slow oscillations in individual cell rest 
Nature reviews. Neuroscience  2013;14(6):443-451.
Sleep is universal in animals, but its specific functions remain elusive. We propose that sleep’s primary function is to allow individual neurons to perform prophylactic cellular maintenance. Just as muscle cells must rest after strenuous exercise to prevent long-term damage, brain cells must rest after intense synaptic activity. We suggest that periods of reduced synaptic input (‘off periods’ or ‘down states’) are necessary for such maintenance. This in turn requires a state of globally synchronized neuronal activity, reduced sensory input and behavioural immobility — the well-known manifestations of sleep.
PMCID: PMC3972489  PMID: 23635871
15.  Controlling Spatial Distributions of Molecules in Multicomponent Organic Crystals, with Quantitative Mapping by Confocal Raman Microspectrometry 
Journal of the American Chemical Society  2013;135(39):14512-14515.
We report four experimental strategies for controlling the three-dimensional arrangement of molecules in multicomponent organic crystals, exploiting confocal Raman microspectrometry to quantify the three-dimensional spatial distributions. Specifically, we focus on controlling the distribution of two types of guest molecule in solid organic inclusion compounds to produce composite core–shell crystals, crystals with a homogeneous distribution of the components, crystals with continuous compositional variation from the core to the surface, and crystals with alternating shells of the components. In this context, confocal Raman microspectrometry is particularly advantageous over optical microscopy as it is nondestructive, offers micrometric spatial resolution, and relies only on the component molecules having different vibrational properties.
PMCID: PMC3876744  PMID: 24004273
16.  Exploiting the Synergy of Powder X-ray Diffraction and Solid-State NMR Spectroscopy in Structure Determination of Organic Molecular Solids 
We report a strategy for structure determination of organic materials in which complete solid-state nuclear magnetic resonance (NMR) spectral data is utilized within the context of structure determination from powder X-ray diffraction (XRD) data. Following determination of the crystal structure from powder XRD data, first-principles density functional theory-based techniques within the GIPAW approach are exploited to calculate the solid-state NMR data for the structure, followed by careful scrutiny of the agreement with experimental solid-state NMR data. The successful application of this approach is demonstrated by structure determination of the 1:1 cocrystal of indomethacin and nicotinamide. The 1H and 13C chemical shifts calculated for the crystal structure determined from the powder XRD data are in excellent agreement with those measured experimentally, notably including the two-dimensional correlation of 1H and 13C chemical shifts for directly bonded 13C–1H moieties. The key feature of this combined approach is that the quality of the structure determined is assessed both against experimental powder XRD data and against experimental solid-state NMR data, thus providing a very robust validation of the veracity of the structure.
PMCID: PMC3876745  PMID: 24386493
17.  The Convallis Rule for Unsupervised Learning in Cortical Networks 
PLoS Computational Biology  2013;9(10):e1003272.
The phenomenology and cellular mechanisms of cortical synaptic plasticity are becoming known in increasing detail, but the computational principles by which cortical plasticity enables the development of sensory representations are unclear. Here we describe a framework for cortical synaptic plasticity termed the “Convallis rule”, mathematically derived from a principle of unsupervised learning via constrained optimization. Implementation of the rule caused a recurrent cortex-like network of simulated spiking neurons to develop rate representations of real-world speech stimuli, enabling classification by a downstream linear decoder. Applied to spike patterns used in in vitro plasticity experiments, the rule reproduced multiple results including and beyond STDP. However STDP alone produced poorer learning performance. The mathematical form of the rule is consistent with a dual coincidence detector mechanism that has been suggested by experiments in several synaptic classes of juvenile neocortex. Based on this confluence of normative, phenomenological, and mechanistic evidence, we suggest that the rule may approximate a fundamental computational principle of the neocortex.
Author Summary
The circuits of the sensory cortex are able to extract useful information from sensory inputs because of their exquisitely organized synaptic connections. These connections are wired largely through experience-dependent synaptic plasticity. Although many details of both the phenomena and cellular mechanisms of cortical synaptic plasticity are now known, an understanding of the computational principles by which synaptic plasticity wires cortical networks lags far behind this experimental data. In this study, we provide a theoretical framework for cortical plasticity termed the “Convallis rule”. The computational power of this rule is demonstrated by its ability to cause simulated cortical networks to learn representations of real-world speech data. Application of the rule to paradigms used to probe synaptic plasticity in vitro reproduced a large number of experimental findings, and the mathematical form of the rule is consistent with a dual coincidence detector mechanism that has been suggested experimentally in juvenile neocortex. Based on this confluence of normative, phenomenological, and mechanistic evidence, we suggest that the rule may approximate a fundamental computational principle of the neocortex.
PMCID: PMC3808450  PMID: 24204224
18.  Top-Down Control of Cortical State 
Neuron  2013;79(3):408-410.
Sensory cortices receive inputs not only from thalamus but also from higher-order cortical regions. Here, Zagha et al. (2013) show that motor cortical inputs can switch barrel cortex into a desynchronized state that enables more faithful representation of subtle sensory stimuli.
PMCID: PMC3739006  PMID: 23931991
19.  Gating of sensory input by spontaneous cortical activity 
The activity of neural populations is determined not only by sensory inputs but also by internally-generated patterns. During quiet wakefulness, the brain produces spontaneous firing events which can spread over large areas of cortex, and have been suggested to underlie processes such as memory recall and consolidation. Here we demonstrate a different role for spontaneous activity in sensory cortex: gating of sensory inputs. We show that population activity in rat auditory cortex is composed of transient 50-100ms packets of spiking activity which occur irregularly during silence and sustained tone stimuli, but reliably at tone onset. Population activity within these packets had broadly consistent spatiotemporal structure, but the rate and also precise relative timing of action potentials varied between stimuli. Packet frequency varied with cortical state, with desynchronized state activity consistent with superposition of multiple overlapping packets. We suggest that such packets reflect the sporadic opening of a “gate” that allows auditory cortex to broadcast a representation of external sounds to other brain regions.
PMCID: PMC3672963  PMID: 23345241
20.  Semi-automatic spike sorting with high-count channel probes 
BMC Neuroscience  2013;14(Suppl 1):P160.
PMCID: PMC3704276
21.  Towards reliable spike-train recordings from thousands of neurons with multielectrodes 
The new generation of silicon-based multielectrodes comprising hundreds or more electrode contacts offers unprecedented possibilities for simultaneous recordings of spike trains from thousands of neurons. Such data will not only be invaluable for finding out how neural networks in the brain work, but will likely be important also for neural prosthesis applications. This opportunity can only be realized if efficient, accurate and validated methods for automatic spike sorting are provided. In this review we describe some of the challenges that must be met to achieve this goal, and in particular argue for the critical need of realistic model data to be used as ground truth in the validation of spike-sorting algorithms.
PMCID: PMC3314330  PMID: 22023727
22.  Hardware-accelerated interactive data visualization for neuroscience in Python 
Large datasets are becoming more and more common in science, particularly in neuroscience where experimental techniques are rapidly evolving. Obtaining interpretable results from raw data can sometimes be done automatically; however, there are numerous situations where there is a need, at all processing stages, to visualize the data in an interactive way. This enables the scientist to gain intuition, discover unexpected patterns, and find guidance about subsequent analysis steps. Existing visualization tools mostly focus on static publication-quality figures and do not support interactive visualization of large datasets. While working on Python software for visualization of neurophysiological data, we developed techniques to leverage the computational power of modern graphics cards for high-performance interactive data visualization. We were able to achieve very high performance despite the interpreted and dynamic nature of Python, by using state-of-the-art, fast libraries such as NumPy, PyOpenGL, and PyTables. We present applications of these methods to visualization of neurophysiological data. We believe our tools will be useful in a broad range of domains, in neuroscience and beyond, where there is an increasing need for scalable and fast interactive visualization.
PMCID: PMC3867689  PMID: 24391582
data visualization; graphics card; OpenGL; Python; electrophysiology
23.  Cortical State and Attention 
Nature Reviews. Neuroscience  2011;12(9):509-523.
The brain continuously adapts its processing machinery to behavioural demands. To achieve this it rapidly modulates the operating mode of cortical circuits, controlling the way information is transformed and routed. This article will focus on two experimental approaches by which the control of cortical information processing has been investigated: the study of state-dependent cortical processing in rodents, and attention in the primate visual system. Both processes involve a modulation of low-frequency activity fluctuations and spiking correlation, and are mediated by common receptor systems. We suggest that selective attention involves processes similar to state change, operating at a local columnar level to enhance the representation of otherwise nonsalient features while suppressing internally generated activity patterns.
PMCID: PMC3324821  PMID: 21829219
24.  How do neurons work together? Lessons from auditory cortex 
Hearing research  2010;271(1-2):37-53.
Recordings of single neurons have yielded great insights into the way acoustic stimuli are represented in auditory cortex. However, any one neuron functions as part of a population whose combined activity underlies cortical information processing. Here we review some results obtained by recording simultaneously from auditory cortical populations and individual morphologically identified neurons, in urethane-anesthetized and unanesthetized passively listening rats. Auditory cortical populations produced structured activity patterns both in response to acoustic stimuli, and spontaneously without sensory input. Population spike time patterns were broadly conserved across multiple sensory stimuli and spontaneous events, exhibiting a generally conserved sequential organization lasting approximately 100ms. Both spontaneous and evoked events exhibited sparse, spatially localized activity in layer 2/3 pyramidal cells, and densely distributed activity in larger layer 5 pyramidal cells and putative interneurons. Laminar propagation differed however, with spontaneous activity spreading upward from deep layers and slowly across columns, but sensory responses initiating in presumptive thalamorecipient layers, spreading rapidly across columns. In both unanesthetized and urethanized rats, global activity fluctuated between “desynchronized” state characterized by low amplitude, high-frequency local field potentials and a “synchronized” state of larger, lower-frequency waves. Computational studies suggested that responses could be predicted by a simple dynamical system model fitted to the spontaneous activity immediately preceding stimulus presentation. Fitting this model to the data yielded a nonlinear self-exciting system model in synchronized states and an approximately linear system in desynchronized states. We comment on the significance of these results for auditory cortical processing of acoustic and non-acoustic information.
PMCID: PMC2992581  PMID: 20603208
25.  Methods for predicting cortical UP and DOWN states from the phase of deep layer local field potentials 
During anesthesia, slow-wave sleep and quiet wakefulness, neuronal membrane potentials collectively switch between de- and hyperpolarized levels, the cortical UP and DOWN states. Previous studies have shown that these cortical UP/DOWN states affect the excitability of individual neurons in response to sensory stimuli, indicating that a significant amount of the trial-to-trial variability in neuronal responses can be attributed to ongoing fluctuations in network activity. However, as intracellular recordings are frequently not available, it is important to be able to estimate their occurrence purely from extracellular data. Here, we combine in vivo whole cell recordings from single neurons with multi-site extracellular microelectrode recordings, to quantify the performance of various approaches to predicting UP/DOWN states from the deep-layer local field potential (LFP). We find that UP/ DOWN states in deep cortical layers of rat primary auditory cortex (A1) are predictable from the phase of LFP at low frequencies (< 4 Hz), and that the likelihood of a given state varies sinusoidally with the phase of LFP at these frequencies. We introduce a novel method of detecting cortical state by combining information concerning the phase of the LFP and ongoing multi-unit activity.
PMCID: PMC3094772  PMID: 20225075
UP and DOWN states; LFP; State dependent coding; Neural coding; Spontaneous activity; Neural oscillations

Results 1-25 (30)