Fast information transfer in neuronal systems rests on series of action potentials, the spike trains, conducted along axons. Methods that compare spike trains are crucial for characterizing different neuronal coding schemes. In this paper we review recent results on the notion of spiking randomness, and discuss its properties with respect to the rate and temporal coding schemes. This method is compared with other widely used characteristics of spiking activity, namely the variability of interspike intervals, and it is shown that randomness and variability provide two distinct views. We demonstrate that estimation of spiking randomness from simulated and experimental data is capable of capturing characteristics that would otherwise be difficult to obtain with conventional methods.

• MeSH
• Action Potentials physiology   MeSH
• Electrophysiology MeSH
• Models, Neurological * MeSH
• Synaptic Transmission physiology   MeSH
• Neurons physiology   MeSH
• Computer Simulation MeSH
• Animals MeSH
• Check Tag
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Review MeSH

The apparent stochastic nature of neuronal activity significantly affects the reliability of neuronal coding. To quantify the encountered fluctuations, both in neural data and simulations, the notions of variability and randomness of inter-spike intervals have been proposed and studied. In this article we focus on the concept of the instantaneous firing rate, which is also based on the spike timing. We use several classical statistical models of neuronal activity and we study the corresponding probability distributions of the instantaneous firing rate. To characterize the firing rate variability and randomness under different spiking regimes, we use different indices of statistical dispersion. We find that the relationship between the variability of interspike intervals and the instantaneous firing rate is not straightforward in general. Counter-intuitively, an increase in the randomness (based on entropy) of spike times may either decrease or increase the randomness of instantaneous firing rate, in dependence on the neuronal firing model. Finally, we apply our methods to experimental data, establishing that instantaneous rate analysis can indeed provide additional information about the spiking activity.

• Keywords
• entropy, firing rate, instantaneous firing rate, neural coding, randomness, rate coding, temporal coding, variability,
• Publication type
• Journal Article MeSH

The analysis of information coding in neurons requires methods that measure different properties of neuronal signals. In this paper we review the recently proposed measure of randomness and compare it to the coefficient of variation, which is the frequently employed measure of variability of spiking neuronal activity. We focus on the problem of the spontaneous activity of neurons, and we hypothesize that under defined conditions, spontaneous activity is more random than evoked activity. This hypothesis is supported by contrasting variability and randomness obtained from experimental recordings of olfactory receptor neurons in rats.

• MeSH
• Action Potentials physiology   MeSH
• Olfactory Receptor Neurons physiology   MeSH
• Models, Neurological MeSH
• Synaptic Transmission physiology   MeSH
• Animals MeSH
• Check Tag
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Review MeSH

The limits on maximum information that can be transferred by single neurons help us to understand how sensory and other information is being processed in the brain. In this paper we approximately calculate the information capacity of the perfect integrate-and-fire neuronal model in dependence on the stimulus range, assuming the simplest form of temporal coding scheme. We couple the information transfer with metabolic cost of neuronal activity and we find that the optimal information per metabolic cost ratios may occur for a relatively small stimulus range. This article is part of a Special Issue entitled Neural Coding.

• MeSH
• Action Potentials physiology   MeSH
• Time Factors MeSH
• Haplorhini MeSH
• Models, Neurological MeSH
• Random Allocation MeSH
• Neurons physiology   MeSH
• Animals MeSH
• Check Tag
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

It is widely accepted that neuronal firing rates contain a significant amount of information about the stimulus intensity. Nevertheless, theoretical studies on the coding accuracy inferred from the exact spike counting distributions are rare. We present an analysis based on the number of observed spikes assuming the stochastic perfect integrate-and-fire model with a change point, representing the stimulus onset, for which we calculate the corresponding Fisher information to investigate the accuracy of rate coding. We analyze the effect of changing the duration of the time window and the influence of several parameters of the model, in particular the level of the presynaptic spontaneous activity and the level of random fluctuation of the membrane potential, which can be interpreted as noise of the system. The results show that the Fisher information is nonmonotonic with respect to the length of the observation period. This counterintuitive result is caused by the discrete nature of the count of spikes. We observe also that the signal can be enhanced by noise, since the Fisher information is nonmonotonic with respect to the level of spontaneous activity and, in some cases, also with respect to the level of fluctuation of the membrane potential.

• Publication type
• Journal Article MeSH

To understand how anatomy and physiology allow an organism to perform its function, it is important to know how information that is transmitted by spikes in the brain is received and encoded. A natural question is whether the spike rate alone encodes the information about a stimulus (rate code), or additional information is contained in the temporal pattern of the spikes (temporal code). Here we address this question using data from the cat Lateral Geniculate Nucleus (LGN), which is the visual portion of the thalamus, through which visual information from the retina is communicated to the visual cortex. We analyzed the responses of LGN neurons to spatially homogeneous spots of various sizes with temporally random luminance modulation. We compared the Firing Rate with the Shannon Information Transmission Rate , which quantifies the information contained in the temporal relationships between spikes. We found that the behavior of these two rates can differ quantitatively. This suggests that the energy used for spiking does not translate directly into the information to be transmitted. We also compared Firing Rates with Information Rates for X-ON and X-OFF cells. We found that, for X-ON cells the Firing Rate and Information Rate often behave in a completely different way, while for X-OFF cells these rates are much more highly correlated. Our results suggest that for X-ON cells a more efficient "temporal code" is employed, while for X-OFF cells a straightforward "rate code" is used, which is more reliable and is correlated with energy consumption.

• Keywords
• Cat LGN, Entropy, Firing rate, Neural coding, ON–OFF cells, Shannon information theory,
• MeSH
• Action Potentials physiology   MeSH
• Mental Processes physiology   MeSH
• Cats MeSH
• Geniculate Bodies cytology   physiology   MeSH
• Neurons physiology   MeSH
• Photic Stimulation methods   MeSH
• Visual Pathways cytology   physiology   MeSH
• Visual Cortex cytology   physiology   MeSH
• Animals MeSH
• Check Tag
• Cats MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Research Support, N.I.H., Extramural MeSH
• Comparative Study MeSH

What is the neural substrate of our capability to properly react to changes in the environment? It can be hypothesized that the anterior cingulate cortex (ACC) manages repetitive stimuli in routine conditions and alerts the dorsolateral prefrontal cortex (PFC) when stimulation unexpectedly changes. To provide evidence in favor of this hypothesis, intracerebral stereoelectroencephalographic (SEEG) data were recorded from the anterior cingulate and dorsolateral PFC of eight epileptic patients in a standard visual oddball task during presurgical monitoring. Two types of stimuli (200 ms duration) such as the letters O (frequent stimuli; 80% of probability) and X (rare stimuli) were presented in random order, with an interstimulus interval between 2 and 5 s. Subjects had to mentally count the rare (target) stimuli and to press a button with their dominant hand as quickly and accurately as possible. EEG frequency bands of interest were theta (4-8 Hz), alpha (8-12 Hz), beta (14-30 Hz), and gamma (30-45 Hz). The directionality of the information flux within the EEG rhythms was indexed by a directed transfer function (DTF). The results showed that compared with the frequent stimuli, the target stimuli induced a statistically significant increase of DTF values from the anterior cingulate to the dorsolateral PFC at the theta rhythms (P < 0.01). These results provide support to the hypothesis that ACC directly or indirectly affects the oscillatory activity of dorsolateral PFC by a selective frequency code under typical oddball conditions.

• MeSH
• Action Potentials physiology   MeSH
• Biological Clocks physiology   MeSH
• Gyrus Cinguli anatomy & histology   physiology   MeSH
• Adult MeSH
• Mental Processes physiology   MeSH
• Electroencephalography methods   MeSH
• Evoked Potentials physiology   MeSH
• Data Interpretation, Statistical MeSH
• Cognition physiology   MeSH
• Humans MeSH
• Brain Mapping MeSH
• Young Adult MeSH
• Neural Pathways anatomy & histology   physiology   MeSH
• Neurons physiology   MeSH
• Neuropsychological Tests MeSH
• Signal Processing, Computer-Assisted MeSH
• Prefrontal Cortex anatomy & histology   physiology   MeSH
• Photic Stimulation MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Young Adult MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Nielsen, Volker Steuber O8 Analytic solution of cable energy function for cortical axons and dendrites Huiwen Ju, Jiao Yu, Michael L. Hines, Liang Chen, Yuguo Yu O9 C. elegans interactome: interactive visualization of Caenorhabditis elegans worm neuronal network Jimin Kim, Will Leahy, Eli Shlizerman O10 Is the model any good? Objective criteria for computational neuroscience model selection Justas Birgiolas, Richard C. Gerkin, Sharon M. Crook O11 Cooperation and competition of gamma oscillation mechanisms Atthaphon Viriyopase, Raoul-Martin Memmesheimer, Stan Gielen O12 A discrete structure of the brain waves Yuri Dabaghian, Justin DeVito, Luca Perotti O13 Direction-specific silencing of the Drosophila gaze stabilization system Anmo J. Kim, Lisa M. Fenk, Cheng Lyu, Gaby Maimon O14 What does the fruit fly think about values? A model of olfactory associative learning Chang Zhao, Yves Widmer, Simon Sprecher,Walter Senn O15 Effects of ionic diffusion on power spectra of local field potentials (LFP) Geir Halnes, Tuomo Mäki-Marttunen, Daniel Keller, Klas H. Pettersen,Ole A. Andreassen, Gaute T. Einevoll O16 Large-scale cortical models towards understanding relationship between brain structure abnormalities and cognitive deficits Yasunori Yamada O17 Spatial coarse-graining the brain: origin of minicolumns Moira L. Steyn-Ross, D. Alistair Steyn-Ross O18 Modeling large-scale cortical networks with laminar structure Jorge F. Mejias, John D. Murray, Henry Kennedy, Xiao-Jing Wang O19 Information filtering by partial synchronous spikes in a neural population Alexandra Kruscha, Jan Grewe, Jan Benda, Benjamin Lindner O20 Decoding context-dependent olfactory valence in Drosophila Laurent Badel, Kazumi Ohta, Yoshiko Tsuchimoto, Hokto Kazama P1 Neural network as a scale-free network: the role of a hub B. Kahng P2 Hemodynamic responses to emotions and decisions using near-infrared spectroscopy optical imaging Nicoladie D. Tam P3 Phase space analysis of hemodynamic responses to intentional movement directions using functional near-infrared spectroscopy (fNIRS) optical imaging technique Nicoladie D.Tam, Luca Pollonini, George Zouridakis P4 Modeling jamming avoidance of weakly electric fish Jaehyun Soh, DaeEun Kim P5 Synergy and redundancy of retinal ganglion cells in prediction Minsu Yoo, S. E. 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