19
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Lognormal firing rate distribution reveals prominent fluctuation–driven regime in spinal motor networks

      research-article
      1 , 1 , *
      eLife
      eLife Sciences Publications, Ltd
      network, lognormal, spinal cord, motor control, neuronal ensemble, CPG, Other

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          When spinal circuits generate rhythmic movements it is important that the neuronal activity remains within stable bounds to avoid saturation and to preserve responsiveness. Here, we simultaneously record from hundreds of neurons in lumbar spinal circuits of turtles and establish the neuronal fraction that operates within either a ‘mean-driven’ or a ‘fluctuation–driven’ regime. Fluctuation-driven neurons have a ‘supralinear’ input-output curve, which enhances sensitivity, whereas the mean-driven regime reduces sensitivity. We find a rich diversity of firing rates across the neuronal population as reflected in a lognormal distribution and demonstrate that half of the neurons spend at least 50 % of the time in the ‘fluctuation–driven’ regime regardless of behavior. Because of the disparity in input–output properties for these two regimes, this fraction may reflect a fine trade–off between stability and sensitivity in order to maintain flexibility across behaviors.

          DOI: http://dx.doi.org/10.7554/eLife.18805.001

          eLife digest

          Where and how are rhythmic movements, such as walking, produced? Many neurons, primarily in the spinal cord, are responsible for the movements, but it is not known how the activity is distributed across this group of cells and what type of activity the neurons use. Some neurons produce regular patterns of “spiking” activity, while others produce spikes at more irregular intervals. These two types of activity have different origins and represent different states of the neural network. It is not clear whether they participate equally in a movement, or if there is a hierarchy among the neurons, such that some neurons have more influence than others.

          Petersen and Berg studied neurons in the lower spines of turtles during rhythmic movements. The experiments show that during rhythmic scratching some neurons are very active while most aren’t particularly active at all. This is known as a lognormal distribution and is seen in many other situations, such as the levels of income of people in a society.

          Petersen and Berg also found that neurons can move between two regimes of activity, called the mean-driven and fluctuation-driven spiking regimes. During rhythmic scratching, the neurons are almost equally divided between the two regimes, and this division is also found in other types of rhythmic movement. This even division between the two regimes is likely to be important for maintaining a balance between the sensitivity and stability of the neural network. The next steps following on from this work are to reveal the mechanisms behind the two regimes and to find out what causes these differences in activity.

          DOI: http://dx.doi.org/10.7554/eLife.18805.002

          Related collections

          Most cited references97

          • Record: found
          • Abstract: found
          • Article: not found

          Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering.

          This study introduces a new method for detecting and sorting spikes from multiunit recordings. The method combines the wavelet transform, which localizes distinctive spike features, with superparamagnetic clustering, which allows automatic classification of the data without assumptions such as low variance or gaussian distributions. Moreover, an improved method for setting amplitude thresholds for spike detection is proposed. We describe several criteria for implementation that render the algorithm unsupervised and fast. The algorithm is compared to other conventional methods using several simulated data sets whose characteristics closely resemble those of in vivo recordings. For these data sets, we found that the proposed algorithm outperformed conventional methods.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            The variable discharge of cortical neurons: implications for connectivity, computation, and information coding.

            Cortical neurons exhibit tremendous variability in the number and temporal distribution of spikes in their discharge patterns. Furthermore, this variability appears to be conserved over large regions of the cerebral cortex, suggesting that it is neither reduced nor expanded from stage to stage within a processing pathway. To investigate the principles underlying such statistical homogeneity, we have analyzed a model of synaptic integration incorporating a highly simplified integrate and fire mechanism with decay. We analyzed a "high-input regime" in which neurons receive hundreds of excitatory synaptic inputs during each interspike interval. To produce a graded response in this regime, the neuron must balance excitation with inhibition. We find that a simple integrate and fire mechanism with balanced excitation and inhibition produces a highly variable interspike interval, consistent with experimental data. Detailed information about the temporal pattern of synaptic inputs cannot be recovered from the pattern of output spikes, and we infer that cortical neurons are unlikely to transmit information in the temporal pattern of spike discharge. Rather, we suggest that quantities are represented as rate codes in ensembles of 50-100 neurons. These column-like ensembles tolerate large fractions of common synaptic input and yet covary only weakly in their spike discharge. We find that an ensemble of 100 neurons provides a reliable estimate of rate in just one interspike interval (10-50 msec). Finally, we derived an expression for the variance of the neural spike count that leads to a stable propagation of signal and noise in networks of neurons-that is, conditions that do not impose an accumulation or diminution of noise. The solution implies that single neurons perform simple algebra resembling averaging, and that more sophisticated computations arise by virtue of the anatomical convergence of novel combinations of inputs to the cortical column from external sources.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Generating coherent patterns of activity from chaotic neural networks.

              Neural circuits display complex activity patterns both spontaneously and when responding to a stimulus or generating a motor output. How are these two forms of activity related? We develop a procedure called FORCE learning for modifying synaptic strengths either external to or within a model neural network to change chaotic spontaneous activity into a wide variety of desired activity patterns. FORCE learning works even though the networks we train are spontaneously chaotic and we leave feedback loops intact and unclamped during learning. Using this approach, we construct networks that produce a wide variety of complex output patterns, input-output transformations that require memory, multiple outputs that can be switched by control inputs, and motor patterns matching human motion capture data. Our results reproduce data on premovement activity in motor and premotor cortex, and suggest that synaptic plasticity may be a more rapid and powerful modulator of network activity than generally appreciated.
                Bookmark

                Author and article information

                Contributors
                Role: Reviewing editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                26 October 2016
                2016
                : 5
                : e18805
                Affiliations
                [1 ]deptDepartment of Neuroscience and Pharmacology, Faculty of Health and Medical Sciences , University of Copenhagen , Copenhagen, Denmark
                [2]Seattle Children's Research Institute and University of Washington , United States
                [3]Seattle Children's Research Institute and University of Washington , United States
                Author notes
                Author information
                http://orcid.org/0000-0002-2092-4791
                http://orcid.org/0000-0001-6376-9368
                Article
                18805
                10.7554/eLife.18805
                5135395
                27782883
                27d4e0bb-9875-4e82-943a-73f752c92314
                © 2016, Petersen et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 14 June 2016
                : 25 October 2016
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100008392, Sundhed og Sygdom, Det Frie Forskningsråd;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100004191, Novo Nordisk;
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Neuroscience
                Research Article
                Custom metadata
                2.5
                Neuronal participation in generation of motor patterns in the spinal circuits is lognormal, which is an indication of a rich diversity of activity within the mean-driven as well as the fluctuation-driven regimes.

                Life sciences
                network,lognormal,spinal cord,motor control,neuronal ensemble,cpg,other
                Life sciences
                network, lognormal, spinal cord, motor control, neuronal ensemble, cpg, other

                Comments

                Comment on this article