In this paper we investigate the rate coding capabilities of neurons whose input signal are alterations of the base state of balanced inhibitory and excitatory synaptic currents. We consider different regimes of excitation-inhibition relationship and an established conductance-based leaky integrator model with adaptive threshold and parameter sets recreating biologically relevant spiking regimes. We find that given mean post-synaptic firing rate, counter-intuitively, increased ratio of inhibition to excitation generally leads to higher signal to noise ratio (SNR). On the other hand, the inhibitory input significantly reduces the dynamic coding range of the neuron. We quantify the joint effect of SNR and dynamic coding range by computing the metabolic efficiency-the maximal amount of information per one ATP molecule expended (in bits/ATP). Moreover, by calculating the metabolic efficiency we are able to predict the shapes of the post-synaptic firing rate histograms that may be tested on experimental data. Likewise, optimal stimulus input distributions are predicted, however, we show that the optimum can essentially be reached with a broad range of input distributions. Finally, we examine which parameters of the used neuronal model are the most important for the metabolically efficient information transfer.
- MeSH
- Adenosine Triphosphate metabolism MeSH
- Action Potentials physiology MeSH
- Excitatory Postsynaptic Potentials physiology MeSH
- Membrane Potentials physiology MeSH
- Models, Neurological * MeSH
- Neural Conduction physiology MeSH
- Synaptic Transmission physiology MeSH
- Neural Inhibition physiology MeSH
- Neurons physiology MeSH
- Computer Simulation MeSH
- Signal-To-Noise Ratio MeSH
- Computational Biology MeSH
- Animals MeSH
- Check Tag
- Animals MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Names of Substances
- Adenosine Triphosphate MeSH
This paper presents a rate-code model of binaural interaction inspired by recent neurophysiological findings. The model consists of a peripheral part and a binaural part. The binaural part is composed of models of the medial superior olive (MSO) and the lateral superior olive (LSO), which are parts of the auditory brainstem. The MSO and LSO model outputs are preprocessed in the interaural time difference (ITD) and interaural level difference (ILD) central stages, respectively, which give absolute values of the predicted lateralization at their outputs, allowing a direct comparison with psychophysical data. The predictions obtained with the MSO and LSO models are compared with subjective data on the lateralization of pure tones and narrowband noises, discrimination of the ITD and ILD, and discrimination of the phase warp. The lateralization and discrimination experiments show good agreement with the subjective data. In the case of the phase-warp experiment, the models agree qualitatively with the subjective data. The results demonstrate that rate-code models of MSO and LSO can be used to explain psychophysical data considering lateralization and discrimination based on binaural cues.
- MeSH
- Discrimination, Psychological MeSH
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Sound Localization * MeSH
- Models, Neurological * MeSH
- Brain Stem physiology MeSH
- Evoked Potentials, Auditory, Brain Stem MeSH
- Ear physiology MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Recent studies on the theoretical performance of latency and rate code in single neurons have revealed that the ultimate accuracy is affected in a nontrivial way by aspects such as the level of spontaneous activity of presynaptic neurons, amount of neuronal noise or the duration of the time window used to determine the firing rate. This study explores how the optimal decoding performance and the corresponding conditions change when the energy expenditure of a neuron in order to spike and maintain the resting membrane potential is accounted for. It is shown that a nonzero amount of spontaneous activity remains essential for both the latency and the rate coding. Moreover, the optimal level of spontaneous activity does not change so much with respect to the intensity of the applied stimulus. Furthermore, the efficiency of the temporal and the rate code converge to an identical finite value if the neuronal activity is observed for an unlimited period of time.
- Keywords
- Fisher information, Metabolic cost, Rate coding, Temporal coding,
- MeSH
- Time Factors MeSH
- Energy Metabolism * MeSH
- Humans MeSH
- Membrane Potentials MeSH
- Models, Neurological * MeSH
- Nerve Net cytology physiology MeSH
- Neural Networks, Computer * MeSH
- Neurons physiology MeSH
- Computer Simulation MeSH
- Computational Biology MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- MeSH
- Adult MeSH
- Information Theory * MeSH
- Humans MeSH
- Methods MeSH
- Reaction Time MeSH
- Heart Rate * MeSH
- Man-Machine Systems MeSH
- Data Display MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article 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 way the human brain represents speech in memory is still unknown. An obvious characteristic of speech is its evolvement over time. During speech processing, neural oscillations are modulated by the temporal properties of the acoustic speech signal, but also acquired knowledge on the temporal structure of language influences speech perception-related brain activity. This suggests that speech could be represented in the temporal domain, a form of representation that the brain also uses to encode autobiographic memories. Empirical evidence for such a memory code is lacking. We investigated the nature of speech memory representations using direct cortical recordings in the left perisylvian cortex during delayed sentence reproduction in female and male patients undergoing awake tumor surgery. Our results reveal that the brain endogenously represents speech in the temporal domain. Temporal pattern similarity analyses revealed that the phase of frontotemporal low-frequency oscillations, primarily in the beta range, represents sentence identity in working memory. The positive relationship between beta power during working memory and task performance suggests that working memory representations benefit from increased phase separation.SIGNIFICANCE STATEMENT Memory is an endogenous source of information based on experience. While neural oscillations encode autobiographic memories in the temporal domain, little is known on their contribution to memory representations of human speech. Our electrocortical recordings in participants who maintain sentences in memory identify the phase of left frontotemporal beta oscillations as the most prominent information carrier of sentence identity. These observations provide evidence for a theoretical model on speech memory representations and explain why interfering with beta oscillations in the left inferior frontal cortex diminishes verbal working memory capacity. The lack of sentence identity coding at the syllabic rate suggests that sentences are represented in memory in a more abstract form compared with speech coding during speech perception and production.
- Keywords
- electrocorticography, memory representations, sentence repetition, speech perception, speech production, temporal pattern similarity,
- MeSH
- Adult MeSH
- Electrocorticography MeSH
- Memory, Short-Term physiology MeSH
- Middle Aged MeSH
- Humans MeSH
- Young Adult MeSH
- Brain physiology MeSH
- Speech Perception physiology MeSH
- Speech physiology MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
BACKGROUND: The LUCAS (Lund University Cardiopulmonary Assist System; Physio-Control Inc./Jolife AB, Lund, Sweden) was developed for automatic chest compressions during cardiopulmonary resuscitation (CPR). Evidence on the use of this device in out-of-hospital cardiac arrest (OHCA) suggests that it should not be used routinely because it has no superior effects. OBJECTIVE: The aim of this study was to compare the effect of CPR for OHCA with and without LUCAS via a regional nonurban emergency medical service (EMS) physician-present prehospital medical system. METHODS: We analyzed a prospective registry of all consecutive OHCA patients in four EMS stations. Two of them used a LUCAS device in all CPR, and the EMS crews in the other two stations used manual CPR. Individuals with contraindication to LUCAS or with EMS-witnessed arrest were excluded. RESULTS: Data from 278 patients were included in the analysis, 144 with LUCAS and 134 with manual CPR. There were more witnessed arrests in the LUCAS group (79.17% vs. 64.18%; p = 0.0074) and patients in the LUCAS group were older (p = 0.03). We found no significant difference in return of spontaneous circulation (30.6% in non-LUCAS vs. 25% in LUCAS; p = 0.35). In the LUCAS group, we observed significantly more conversions from nonshockable to shockable rhythm (20.7% vs. 10.10%; p = 0.04). The 30-day survival rate was significantly lower in the LUCAS group (5.07% vs. 16.31% in the non-LUCAS group; p = 0.044). At 180-day follow-up, we observed no significant difference (5.45% in non-LUCAS vs. 9.42% in LUCAS; p = 0.25). CONCLUSIONS: Use of the LUCAS system decreased survival rate in OHCA patients. Significantly higher 30-day mortality was seen in LUCAS-treated patients.
- Keywords
- CPR, LUCAS 2, prehospital care,
- MeSH
- Thorax MeSH
- Cardiopulmonary Resuscitation * MeSH
- Humans MeSH
- Survival Rate MeSH
- Emergency Medical Services * MeSH
- Out-of-Hospital Cardiac Arrest * therapy MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
Neuronal firing rate is traditionally defined as the number of spikes per time window. The concept is essential for the rate coding hypothesis, which is still the most commonly investigated scenario in neuronal activity analysis. The estimation of dynamically changing firing rate from neural data can be challenging due to the variability of spike times, even under identical external conditions; hence a wide range of statistical measures have been employed to solve this particular problem. In this paper, we review established firing rate estimation methods, briefly summarize the technical aspects of each approach and discuss their practical applications.
- Keywords
- Bayesian rule, Firing rate, Kernel smoothing, Spike train, Time histogram,
- MeSH
- Action Potentials * MeSH
- Algorithms MeSH
- Bayes Theorem MeSH
- Data Interpretation, Statistical MeSH
- Humans MeSH
- Models, Neurological MeSH
- Neurons physiology MeSH
- Probability MeSH
- Stochastic Processes MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Review MeSH
The rate coding hypothesis is the oldest and still one of the most accepted hypotheses of neural coding. Consequently, many approaches have been devised for the firing rate estimation, ranging from simple binning of the time axis to advanced statistical methods. Nonetheless the concept of firing rate, while informally understood, can be mathematically defined in several distinct ways. These definitions may yield mutually incompatible results unless implemented properly. Recently it has been shown that the notions of the instantaneous and the classical firing rates can be made compatible, at least in terms of their averages, by carefully discerning the time instant at which the neuronal activity is observed. In this paper we revisit the properties of instantaneous interspike intervals in order to derive several novel firing rate estimators, which are free of additional assumptions or parameters and their temporal resolution is 'locally self-adaptive'. The estimators are simple to implement and are numerically efficient even for very large sets of data.
- Keywords
- Big data, Estimator, Firing rate, Spike train,
- MeSH
- Action Potentials * physiology MeSH
- Humans MeSH
- Models, Neurological * MeSH
- Neurons * physiology MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
We define an optimal signal in parametric neuronal models on the basis of interspike interval data and rate coding schema. Under the classical approach the optimal signal is located where the frequency transfer function is steepest. Its position coincides with the inflection point of this curve. This concept is extended here by using Fisher information which is the inverse asymptotic variance of the best estimator and its dependence on the parameter value indicates accuracy of estimation. We compare the signal producing maximal Fisher information with the inflection point of the sigmoidal frequency transfer function.
