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ABSTRACT: Lateral habenula (LHb) has attracted growing interest as a regulator of serotonergic and dopaminergic neurons in the CNS. However, it
remains unclear how the LHb modulates brain states in animals. To identify the neural substrates that are under the influence of LHb
regulation, we examined the effects of rat LHb lesions on the hippocampal oscillatory activity associated with the transition of brain
states. Our results showed that the LHb lesion shortened the theta activity duration both in anesthetized and sleeping rats. Furthermore,
this inhibitory effect of LHb lesion on theta maintenance depended upon an intact serotonergic median raphe, suggesting that LHb
activity plays an essential role in maintaining hippocampal theta oscillation via the serotonergic raphe. Multiunit recording of sleeping
rats further revealed that firing of LHb neurons showed significant phase-locking activity at each theta oscillation cycle in the hippocampus.
LHb neurons showing activity that was coordinated with that of the hippocampal theta were localized in the medial LHb division,
which receives afferents from the diagonal band of Broca (DBB), a pacemaker region for the hippocampal theta oscillation. Thus, our
findings indicate that the DBB may pace not only the hippocampus, but also the LHb, during rapid eye movement sleep. Since serotonin
is known to negatively regulate theta oscillation in the hippocampus, phase-locking activity of the LHb neurons may act, under the
influence of the DBB, to maintain the hippocampal theta oscillation by modulating the activity of serotonergic neurons.
Journal of Neuroscience 05/2013; 33(20):8909-8921. · 7.11 Impact Factor
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ABSTRACT: The brain has to analyze and respond to external events that can change rapidly from time to time, suggesting that information processing by the brain may be essentially dynamic rather than static. The dynamical features of neural computation are of significant importance in motor cortex that governs the process of movement generation and learning. In this paper, we discuss these features based primarily on our recent findings on neural dynamics and information coding in the microcircuit of rat motor cortex. In fact, cortical neurons show a variety of dynamical behavior from rhythmic activity in various frequency bands to highly irregular spike firing. Of particular interest are the similarity and dissimilarity of the neuronal response properties in different layers of motor cortex. By conducting electrophysiological recordings in slice preparation, we report the phase response curves (PRCs) of neurons in different cortical layers to demonstrate their layer-dependent synchronization properties. We then study how motor cortex recruits task-related neurons in different layers for voluntary arm movements by simultaneous juxtacellular and multiunit recordings from behaving rats. The results suggest an interesting difference in the spectrum of functional activity between the superficial and deep layers. Furthermore, the task-related activities recorded from various layers exhibited power law distributions of inter-spike intervals (ISIs), in contrast to a general belief that ISIs obey Poisson or Gamma distributions in cortical neurons. We present a theoretical argument that this power law of in vivo neurons may represent the maximization of the entropy of firing rate with limited energy consumption of spike generation. Though further studies are required to fully clarify the functional implications of this coding principle, it may shed new light on information representations by neurons and circuits in motor cortex.
Frontiers in Neural Circuits 01/2013; 7:85. · 5.10 Impact Factor
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ABSTRACT: The mammalian habenula is involved in regulating the activities of serotonergic and dopaminergic neurons. It consists of the medial and lateral habenulae, with each subregion having distinct neural connectivity. Despite the functional significance, manipulating neural activity in a subset of habenular pathways remains difficult because of the poor availability of molecular markers that delineate the subnuclear structures. Thus, we examined the molecular nature of neurons in the habenular subnuclei by analyzing the gene expressions of neurotransmitter markers. The results showed that different subregions of the medial habenula (MHb) use different combinations of neurotransmitter systems and could be categorized as either exclusively glutamatergic (superior part of MHb), both substance P-ergic and glutamatergic (dorsal region of central part of MHb), or both cholinergic and glutamatergic (inferior part, ventral region of central part, and lateral part of MHb). The superior part of the MHb strongly expressed interleukin-18 and was innervated by noradrenergic fibers. In contrast, the inferior part, ventral region of the central part, and lateral part of the MHb were peculiar in that acetylcholine and glutamate were cotransmitted from the axonal terminals. In contrast, neurons in the lateral habenula (LHb) were almost uniformly glutamatergic. Finally, the expressions of Htr2c and Drd2 seemed complementary in the medial LHb division, whereas they coincided in the lateral division, suggesting that the medial and lateral divisions of LHb show strong heterogeneity with respect to monoamine receptor expression. These analyses clarify molecular differences between subnuclei in the mammalian habenula that support their respective functional implications. J. Comp. Neurol. 520:4051-4066, 2012. © 2012 Wiley Periodicals, Inc.
The Journal of Comparative Neurology 12/2012; 520(18):Spc1. · 3.81 Impact Factor
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ABSTRACT: Spike-timing-dependent plasticity (STDP) has been observed in many brain areas such as sensory cortices, where it is hypothesized to structure synaptic connections between neurons. Previous studies have demonstrated how STDP can capture spiking information at short timescales using specific input configurations, such as coincident spiking, spike patterns and oscillatory spike trains. However, the corresponding computation in the case of arbitrary input signals is still unclear. This paper provides an overarching picture of the algorithm inherent to STDP, tying together many previous results for commonly used models of pairwise STDP. For a single neuron with plastic excitatory synapses, we show how STDP performs a spectral analysis on the temporal cross-correlograms between its afferent spike trains. The postsynaptic responses and STDP learning window determine kernel functions that specify how the neuron "sees" the input correlations. We thus denote this unsupervised learning scheme as 'kernel spectral component analysis' (kSCA). In particular, the whole input correlation structure must be considered since all plastic synapses compete with each other. We find that kSCA is enhanced when weight-dependent STDP induces gradual synaptic competition. For a spiking neuron with a "linear" response and pairwise STDP alone, we find that kSCA resembles principal component analysis (PCA). However, plain STDP does not isolate correlation sources in general, e.g., when they are mixed among the input spike trains. In other words, it does not perform independent component analysis (ICA). Tuning the neuron to a single correlation source can be achieved when STDP is paired with a homeostatic mechanism that reinforces the competition between synaptic inputs. Our results suggest that neuronal networks equipped with STDP can process signals encoded in the transient spiking activity at the timescales of tens of milliseconds for usual STDP.
PLoS Computational Biology 07/2012; 8(7):e1002584. · 5.22 Impact Factor
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ABSTRACT: The mammalian habenula is involved in regulating the activities of serotonergic and dopaminergic neurons. It consists of the medial and lateral habenulae, with each subregion having distinct neural connectivity. Despite the functional significance, manipulating neural activity in a subset of habenular pathways remains difficult because of the poor availability of molecular markers that delineate the subnuclear structures. Thus, we examined the molecular nature of neurons in the habenular subnuclei by analyzing the gene expressions of neurotransmitter markers. The results showed that different subregions of the medial habenula (MHb) use different combinations of neurotransmitter systems and could be categorized as either exclusively glutamatergic (superior part of MHb), both substance P-ergic and glutamatergic (dorsal region of central part of MHb), or both cholinergic and glutamatergic (inferior part, ventral region of central part, and lateral part of MHb). The superior part of the MHb strongly expressed interleukin-18 and was innervated by noradrenergic fibers. In contrast, the inferior part, ventral region of the central part, and lateral part of the MHb were peculiar in that acetylcholine and glutamate were cotransmitted from the axonal terminals. In contrast, neurons in the lateral habenula (LHb) were almost uniformly glutamatergic. Finally, the expressions of Htr2c and Drd2 seemed complementary in the medial LHb division, whereas they coincided in the lateral division, suggesting that the medial and lateral divisions of LHb show strong heterogeneity with respect to monoamine receptor expression. These analyses clarify molecular differences between subnuclei in the mammalian habenula that support their respective functional implications. J. Comp. Neurol. 520:4051-4066, 2012. © 2012 Wiley Periodicals, Inc.
The Journal of Comparative Neurology 06/2012; 520(18):4051-66. · 3.81 Impact Factor
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ABSTRACT: The brain is considered to use a relatively small amount of energy for its efficient information processing. Under a severe restriction on the energy consumption, the maximization of mutual information (MMI), which is adequate for designing artificial processing machines, may not suit for the brain. The MMI attempts to send information as accurate as possible and this usually requires a sufficient energy supply for establishing clearly discretized communication bands. Here, we derive an alternative hypothesis for neural code from the neuronal activities recorded juxtacellularly in the sensorimotor cortex of behaving rats. Our hypothesis states that in vivo cortical neurons maximize the entropy of neuronal firing under two constraints, one limiting the energy consumption (as assumed previously) and one restricting the uncertainty in output spike sequences at given firing rate. Thus, the conditional maximization of firing-rate entropy (CMFE) solves a tradeoff between the energy cost and noise in neuronal response. In short, the CMFE sends a rich variety of information through broader communication bands (i.e., widely distributed firing rates) at the cost of accuracy. We demonstrate that the CMFE is reflected in the long-tailed, typically power law, distributions of inter-spike intervals obtained for the majority of recorded neurons. In other words, the power-law tails are more consistent with the CMFE rather than the MMI. Thus, we propose the mathematical principle by which cortical neurons may represent information about synaptic input into their output spike trains.
PLoS Computational Biology 04/2012; 8(4):e1002461. · 5.22 Impact Factor
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ABSTRACT: The connectivity of complex networks and functional implications has been attracting much interest in many physical, biological and social systems. However, the significance of the weight distributions of network links remains largely unknown except for uniformly- or Gaussian-weighted links. Here, we show analytically and numerically, that recurrent neural networks can robustly generate internal noise optimal for spike transmission between neurons with the help of a long-tailed distribution in the weights of recurrent connections. The structure of spontaneous activity in such networks involves weak-dense connections that redistribute excitatory activity over the network as noise sources to optimally enhance the responses of individual neurons to input at sparse-strong connections, thus opening multiple signal transmission pathways. Electrophysiological experiments confirm the importance of a highly broad connectivity spectrum supported by the model. Our results identify a simple network mechanism for internal noise generation by highly inhomogeneous connection strengths supporting both stability and optimal communication.
Scientific Reports 01/2012; 2:485.
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ABSTRACT: The postsynaptic potentials of pyramidal neurons have a non-Gaussian amplitude distribution with a heavy tail in both hippocampus and neocortex. Such distributions of synaptic weights were recently shown to generate spontaneous internal noise optimal for spike propagation in recurrent cortical circuits. However, whether this internal noise generation by heavy-tailed weight distributions is possible for and beneficial to other computational functions remains unknown. To clarify this point, we construct an associative memory (AM) network model of spiking neurons that stores multiple memory patterns in a connection matrix with a lognormal weight distribution. In AM networks, non-retrieved memory patterns generate a cross-talk noise that severely disturbs memory recall. We demonstrate that neurons encoding a retrieved memory pattern and those encoding non-retrieved memory patterns have different subthreshold membrane-potential distributions in our model. Consequently, the probability of responding to inputs at strong synapses increases for the encoding neurons, whereas it decreases for the non-encoding neurons. Our results imply that heavy-tailed distributions of connection weights can generate noise useful for AM recall.
Frontiers in Computational Neuroscience 01/2012; 6:102. · 2.15 Impact Factor
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ABSTRACT: This study introduces a new spike sorting method that classifies spike waveforms from multiunit recordings into spike trains of individual neurons. In particular, we develop a method to sort a spike mixture generated by a heterogeneous neural population. Such a spike sorting has a significant practical value, but was previously difficult. The method combines a feature extraction method, which we may term "multimodality-weighted principal component analysis" (mPCA), and a clustering method by variational Bayes for Student's t mixture model (SVB). The performance of the proposed method was compared with that of other conventional methods for simulated and experimental data sets. We found that the mPCA efficiently extracts highly informative features as clusters clearly separable in a relatively low-dimensional feature space. The SVB was implemented explicitly without relying on Maximum-A-Posterior (MAP) inference for the "degree of freedom" parameters. The explicit SVB is faster than the conventional SVB derived with MAP inference and works more reliably over various data sets that include spiking patterns difficult to sort. For instance, spikes of a single bursting neuron may be separated incorrectly into multiple clusters, whereas those of a sparsely firing neuron tend to be merged into clusters for other neurons. Our method showed significantly improved performance in spike sorting of these "difficult" neurons. A parallelized implementation of the proposed algorithm (EToS version 3) is available as open-source code at http://etos.sourceforge.net/.
Frontiers in Neuroinformatics 01/2012; 6:5.
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ABSTRACT: The activity patterns of the globus pallidus (GPe) and subthalamic nucleus (STN) are closely associated with motor function and dysfunction in the basal ganglia. In the pathological state caused by dopamine depletion, the STN-GPe network exhibits rhythmic synchronous activity accompanied by rebound bursts in the STN. Therefore, the mechanism of activity transition is a key to understand basal ganglia functions. As synchronization in GPe neurons could induce pathological STN rebound bursts, it is important to study how synchrony is generated in the GPe. To clarify this issue, we applied the phase-reduction technique to a conductance-based GPe neuronal model in order to derive the phase response curve (PRC) and interaction function between coupled GPe neurons. Using the PRC and interaction function, we studied how the steady-state activity of the GPe network depends on intrinsic membrane properties, varying ionic conductances on the membrane. We noted that a change in persistent sodium current, fast delayed rectifier Kv3 potassium current, M-type potassium current and small conductance calcium-dependent potassium current influenced the PRC shape and the steady state. The effect of those currents on the PRC shape could be attributed to extension of the firing period and reduction of the phase response immediately after an action potential. In particular, the slow potassium current arising from the M-type potassium and the SK current was responsible for the reduction of the phase response. These results suggest that the membrane property modulation controls synchronization/asynchronization in the GPe and the pathological pattern of STN-GPe activity.
Journal of Computational Neuroscience 10/2011; 32(3):539-53. · 2.51 Impact Factor
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ABSTRACT: Neural circuitry is a self-organizing arithmetic device that converts input to output and thereby remodels its computational algorithm to produce more desired output; however, experimental evidence regarding the mechanism by which information is modified and stored while propagating across polysynaptic networks is sparse. We used functional multineuron calcium imaging to monitor the spike outputs from thousands of CA1 neurons in response to the stimulation of two independent sites of the dentate gyrus in rat hippocampal networks ex vivo. Only pyramidal cells were analyzed based on post hoc immunostaining. Some CA1 pyramidal cells were observed to fire action potentials only when both sites were simultaneously stimulated (AND-like neurons), whereas other neurons fired in response to either site of stimulation but not to concurrent stimulation (XOR-like neurons). Both types of neurons were interlaced in the same network and altered their logical operation depending on the timing of paired stimulation. Repetitive paired stimulation for brief periods induced a persistent reorganization of AND and XOR operators, suggesting a flexibility in parallel distributed processing. We simulated these network functions in silico and found that synaptic modification of the CA3 recurrent excitation is pivotal to the shaping of logic plasticity. This work provides new insights into how microscopic synaptic properties are associated with the mesoscopic dynamics of complex microcircuits.
Journal of Neuroscience 09/2011; 31(37):13168-79. · 7.11 Impact Factor
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ABSTRACT: Several areas of the brain are known to participate in temporal processing. Neurons in the prefrontal cortex (PFC) are thought to contribute to perception of time intervals. However, it remains unclear whether the PFC itself can generate time intervals independently of external stimuli. Here we describe a group of PFC neurons in area 9 that became active when monkeys recognized a particular elapsed time within the range of 1-7 seconds. Another group of area 9 neurons became active only when subjects reproduced a specific interval without external cues. Both types of neurons were individually tuned to recognize or reproduce particular intervals. Moreover, the injection of muscimol, a GABA agonist, into this area bilaterally resulted in an increase in the error rate during time interval reproduction. These results suggest that area 9 may process multi-second intervals not only in perceptual recognition, but also in internal generation of time intervals.
PLoS ONE 01/2011; 6(4):e19168. · 4.09 Impact Factor
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ABSTRACT: Spike-timing-dependent plasticity (STDP) modifies the weight (or strength) of synaptic connections between neurons and is considered to be crucial for generating network structure. It has been observed in physiology that, in addition to spike timing, the weight update also depends on the current value of the weight. The functional implications of this feature are still largely unclear. Additive STDP gives rise to strong competition among synapses, but due to the absence of weight dependence, it requires hard boundaries to secure the stability of weight dynamics. Multiplicative STDP with linear weight dependence for depression ensures stability, but it lacks sufficiently strong competition required to obtain a clear synaptic specialization. A solution to this stability-versus-function dilemma can be found with an intermediate parametrization between additive and multiplicative STDP. Here we propose a novel solution to the dilemma, named log-STDP, whose key feature is a sublinear weight dependence for depression. Due to its specific weight dependence, this new model can produce significantly broad weight distributions with no hard upper bound, similar to those recently observed in experiments. Log-STDP induces graded competition between synapses, such that synapses receiving stronger input correlations are pushed further in the tail of (very) large weights. Strong weights are functionally important to enhance the neuronal response to synchronous spike volleys. Depending on the input configuration, multiple groups of correlated synaptic inputs exhibit either winner-share-all or winner-take-all behavior. When the configuration of input correlations changes, individual synapses quickly and robustly readapt to represent the new configuration. We also demonstrate the advantages of log-STDP for generating a stable structure of strong weights in a recurrently connected network. These properties of log-STDP are compared with those of previous models. Through long-tail weight distributions, log-STDP achieves both stable dynamics for and robust competition of synapses, which are crucial for spike-based information processing.
PLoS ONE 01/2011; 6(10):e25339. · 4.09 Impact Factor
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ABSTRACT: A variety of epileptic seizure models have shown that activation of glutamatergic pyramidal cells is usually required for rhythm generation and/or synchronization in hippocampal seizure-like oscillations in vitro. However, it still remains unclear whether GABAergic interneurons may be able to drive the seizure-like oscillations without glutamatergic transmission. Here, we found that electrical stimulation in rat hippocampal CA1 slices induced a putative prototype of seizure-like oscillations ("prototypic afterdischarge," 1.8-3.8 Hz) in mature pyramidal cells and interneurons in the presence of ionotropic glutamate receptor antagonists. The prototypic afterdischarge was abolished by GABA(A) receptor antagonists or gap junction blockers, but not by a metabotropic glutamate receptor antagonist or a GABA(B) receptor antagonist. Gramicidin-perforated patch-clamp and voltage-clamp recordings revealed that pyramidal cells were depolarized and frequently excited directly through excitatory GABAergic transmissions in each cycle of the prototypic afterdischarge. Interneurons that were actively spiking during the prototypic afterdischarge were mostly fast-spiking (FS) interneurons located in the strata oriens and pyramidale. Morphologically, these interneurons that might be "potential seizure drivers" included basket, chandelier, and bistratified cells. Furthermore, they received direct excitatory GABAergic input during the prototypic afterdischarge. The O-LM cells and most of the interneurons in the strata radiatum and lacunosum moleculare were not essential for the generation of prototypic afterdischarge. The GABA-mediated prototypic afterdischarge was observed later than the third postnatal week in the rat hippocampus. Our results suggest that an FS interneuron network alone can drive the prototypic form of electrically induced seizure-like oscillations through their excitatory GABAergic transmissions and presumably through gap junction-mediated communications.
Journal of Neuroscience 10/2010; 30(41):13679-89. · 7.11 Impact Factor
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Neural networks: the official journal of the International Neural Network Society 08/2010; 23(6):667-8. · 1.88 Impact Factor
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ABSTRACT: Phase response curve (PRC) of an oscillatory neuron describes the response of the neuron to external perturbation. The PRC is useful to predict synchronized dynamics of neurons; hence, its measurement from experimental data attracts increasing interest in neural science. This paper introduces a Bayesian method for estimating PRCs from data, which allows for the correlation of errors in explanatory and response variables of the PRC. The method is implemented with a replica exchange Monte Carlo technique; this avoids local minima and enables efficient calculation of posterior averages. A test with artificial data generated by the noisy Morris-Lecar equation shows that the proposed method outperforms conventional regression that ignores errors in the explanatory variable. Experimental data from the pyramidal cells in the rat motor cortex is also analyzed with the method; a case is found where the result with the proposed method is considerably different from that obtained by conventional regression.
Neural networks: the official journal of the International Neural Network Society 04/2010; 23(6):752-63. · 1.88 Impact Factor
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ABSTRACT: A neuron embedded in an intact brain, unlike an isolated neuron, participates in network activity at various spatial resolutions. Such multiple scale spatial dynamics is potentially reflected in multiple time scales of temporal dynamics. We identify such multiple dynamical time scales of the inter-spike interval (ISI) fluctuations of neurons of waking/sleeping rats by means of multiscale analysis. The time scale of large non-Gaussianity in the ISI fluctuations, measured with the Castaing method, ranges up to several minutes, markedly escaping the low-pass filtering characteristics of neurons. A comparison between neural activity during waking and sleeping reveals that non-Gaussianity is stronger during waking than sleeping throughout the entire range of scales observed. We find a remarkable property of near scale independence of the magnitude correlations as the primary cause of persistent non-Gaussianity. Such scale-invariance of correlations is characteristic of multiplicative cascade processes and raises the possibility of the existence of a scale independent memory preserving mechanism.
PLoS ONE 01/2010; 5(9). · 4.09 Impact Factor
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BMC Neuroscience. 01/2010;
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ABSTRACT: Simultaneous recordings with multi-channel electrodes are widely used for studying how multiple neurons are recruited for information processing. The recorded signals contain the spike events of a number of adjacent or distant neurons and must be sorted correctly into spike trains of individual neurons. Several mathematical methods have been proposed for spike sorting but the process is difficult in practice, as extracellularly recorded signals are corrupted by biological noise. Moreover, spike sorting is often time-consuming, as it usually requires corrections by human operators. Methods are needed to obtain reliable spike clusters without heavy manual operation. Here, we introduce several methods of spike sorting and compare the accuracy and robustness of their performance by using publicized data of simultaneous extracellular and intracellular recordings of neuronal activity. The best and excellent performance was obtained when a newly proposed filter for spike detection was combined with the wavelet transform and variational Bayes for a finite mixture of Student's t-distributions, namely, robust variational Bayes. Wavelet transform extracts features that are characteristic of the detected spike waveforms and the robust variational Bayes categorizes the extracted features into clusters corresponding to spikes of the individual neurons. The use of Student's t-distributions makes this categorization robust against noisy data points. Some other new methods also exhibited reasonably good performance. We implemented all of the proposed methods in a C++ code named 'EToS' (Efficient Technology of Spike sorting), which is freely available on the Internet.
European Journal of Neuroscience 01/2010; 31(2):263-72. · 3.63 Impact Factor
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ABSTRACT: Motor cortex neurons are activated at different times during self-initiated voluntary movement. However, the manner in which excitatory and inhibitory neurons in distinct cortical layers help to organize voluntary movement is poorly understood. We carried out juxtacellular and multiunit recordings from actively behaving rats and found temporally and functionally distinct activations of excitatory pyramidal cells and inhibitory fast-spiking interneurons. Across cortical layers, pyramidal cells were activated diversely for sequential motor phases (for example, preparation, initiation and execution). In contrast, fast-spiking interneurons, including parvalbumin-positive basket cells, were recruited predominantly for motor execution, with pyramidal cells producing a command-like activity. Thus, fast-spiking interneurons may underlie command shaping by balanced inhibition or recurrent inhibition, rather than command gating by temporally alternating excitation and inhibition. Furthermore, initiation-associated pyramidal cells excited similar and different functional classes of neurons through putative monosynaptic connections. This suggests that these cells may temporally integrate information to initiate and coordinate voluntary movement.
Nature Neuroscience 11/2009; 12(12):1586-93. · 15.53 Impact Factor
