Over the last five decades, progress in neural recording techniques has allowed the number of simultaneously recorded neurons to double approximately every 7 years, mimicking Moore’s law. Such exponential growth motivates us to ask how data analysis techniques are affected by progressively larger numbers of recorded neurons. Traditionally, neurons are analyzed independently on the basis of their tuning to stimuli or movement. Although tuning curve approaches are unaffected by growing numbers of simultaneously recorded neurons, newly developed techniques that analyze interactions between neurons become more accurate and more complex as the number of recorded neurons increases. Emerging data analysis techniques should consider both the computational costs and the potential for more accurate models associated with this exponential growth of the number of recorded neurons.

Although neuronal spikes can be readily detected from extracellular recordings, synaptic and subthreshold activity remains undifferentiated within the local field potential (LFP). In the hippocampus, neurons discharge selectively when the rat is at certain locations, while LFPs at single anatomical sites exhibit no such place-tuning. Nonetheless, because the representation of position is sparse and distributed, we hypothesized that spatial information can be recovered from multiple-site LFP recordings. Using high-density sampling of LFP and computational methods, we show that the spatiotemporal structure of the theta rhythm can encode position as robustly as neuronal spiking populations. Because our approach exploits the rhythmicity and sparse structure of neural activity, features found in many brain regions, it is useful as a general tool for discovering distributed LFP codes.

SummaryA central question in neuroscience is how interactions between neurons give rise to behavior. In many electrophysiological experiments, the activity of a set of neurons is recorded while sensory stimuli or movement tasks are varied. Tools that aim to reveal underlying interactions between neurons from such data can be extremely useful. Traditionally, neuroscientists have studied these interactions using purely descriptive statistics (cross-correlograms or joint peri-stimulus time histograms). However, the interpretation of such data is often difficult, particularly as the number of recorded neurons grows. Recent research suggests that model-based, maximum likelihood methods can improve these analyses. In addition to estimating neural interactions, application of these © 2008 Elsevier Ltd. All rights reserved.Correspondence should be addressed to KPK (E-mail: kk@northwestern.edu). Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. W et al. (2005) Using data recorded from a population of neurons in primary motor cortex, the authors show that a GLM can improve decoding of hand kinematics. They also present several related models and use an important tool for model selection, not discussed in this review (KStests on the time-rescaled spike times).• • Pillow JW et al. (2008) Using data from a densely recorded population of retinal ganglion cells, the authors show that a GLM can improve decoding of visual stimuli. The inferred functional connections correspond closely with the known anatomy of the retina, where neighboring cells are highly correlated. Moreover, the authors found that trial-to-trial variation can largely be explained by these functional connections.• Kass RE et al (2005) The authors review some of the advanced methods for analyzing spike train data. They thoroughly explain model-based methods and tools to test their validity.• Pillow J (2007) The author reviews a number of likelihood-based models of neural coding. Functional connectivity is not the central theme, but maximum likelihood methods and tools for testing their validity (KS-tests on time-rescaled spike times and cross-validation) are thoroughly explained.• Schneidman E et al (2006) The authors use a popular alternative to the GLM, a maximum entropy model, to analyze data from a population of retinal ganglion cells. They show that most of the variance in spiking can be explained by pair-wise interactions alone. Higher-order correlations between triplets of neurons, for instance, play a smaller role. They also discuss the "curse of dimensionality," one of the difficulties in extending descriptive m...

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A fundamental challenge for the nervous system is to encode signals spanning many orders of magnitude with neurons of limited bandwidth. To meet this challenge, perceptual systems use gain control. However, whether the motor system uses an analogous mechanism is essentially unknown. Neuromodulators, such as serotonin, are prime candidates for gain control signals during force production. Serotonergic neurons project diffusely to motor pools, and, therefore, force production by one muscle should change the gain of others. Here we present behavioral and pharmaceutical evidence that serotonin modulates the input-output gain of motoneurons in humans. By selectively changing the efficacy of serotonin with drugs, we systematically modulated the amplitude of spinal reflexes. More importantly, force production in different limbs interacts systematically, as predicted by a spinal gain control mechanism. Psychophysics and pharmacology suggest that the motor system adopts gain control mechanisms, and serotonin is a primary driver for their implementation in force production.

Plasticity is a crucial component of normal brain function and a critical mechanism for recovery from injury. In vitro, associative pairing of presynaptic spiking and stimulus-induced postsynaptic depolarization causes changes in the synaptic efficacy of the presynaptic neuron, when activated by extrinsic stimulation. In vivo, such paradigms can alter the responses of whole groups of neurons to stimulation. Here, we used in vivo spike-triggered stimulation to drive plastic changes in rat forelimb sensorimotor cortex, which we monitored using a statistical measure of functional connectivity inferred from the spiking statistics of the neurons during normal, spontaneous behavior. These induced plastic changes in inferred functional connectivity depended on the latency between trigger spike and stimulation, and appear to reflect a robust reorganization of the network. Such targeted connectivity changes might provide a tool for rerouting the flow of information through a network, with implications for both rehabilitation and brain–machine interface applications.

Current multi-electrode techniques enable the simultaneous recording of spikes from hundreds of neurons. To study neural plasticity and network structure it is desirable to infer the underlying functional connectivity between the recorded neurons. Functional connectivity is defined by a large number of parameters, which characterize how each neuron influences the other neurons. A Bayesian approach that combines information from the recorded spikes (likelihood) with prior beliefs about functional connectivity (prior) can improve inference of these parameters and reduce overfitting. Recent studies have used likelihood functions based on the statistics of point-processes and a prior that captures the sparseness of neural connections. Here we include a prior that captures the empirical finding that interactions tend to vary smoothly in time. We show that this method can successfully infer connectivity patterns in simulated data and apply the algorithm to spike data recorded from primary motor (M1) and premotor (PMd) cortices of a monkey. Finally, we present a new approach to studying structure in inferred connections based on a Bayesian clustering algorithm. Groups of neurons in M1 and PMd show common patterns of input and output that may correspond to functional assemblies.

1 Parallel human and rat studies were carried out to confirm the previous suggestion of an increased sensitivity to warfarin in old age. 2 The anticoagulant response to warfarin was found to be greater in the elderly groups despite, in the case of the patient study, the elderly subjects being given a smaller weight‐related dose. 3 At the same plasma warfarin concentrations there was greater inhibition of vitamin K‐dependent clotting factor synthesis in the elderly. There was no difference in the rate of clotting factor degradation in the two age groups. 4 There was no appreciable difference in warfarin pharmacokinetics (plasma half‐life, apparent volume of distribution, plasma clearance, plasma protein binding or plasma warfarin alcohol levels) in the two age groups. 5 There appeared to be no major age‐ related differences in warfarin pharmacokinetics and the increased effect of warfarin in the elderly seemed to result from an increased intrinsic sensitivity to warfarin.