A new Mathematical Framework to Understand Single Neuron Computations

An important feature of the nervous system is its ability to adapt to new stimuli. This adaptation allows for optimal encoding of the incoming information by dynamically changing the coding strategy based upon the incoming inputs to the neuron. At the level of single cells, this widespread phenomena is often referred to as spike-frequency adaptation, since it manifests as a history-dependent modulation of the neurons firing frequency. In this thesis I focus on how a neuron is able to adapt its activity to a specific input as well as on the function of such adaptive mechanisms. To study these adaptive processes different approaches have been used, from empirical observations of neural activities to detailed modeling of single cells. Here, I approach these problems by using simplified threshold models. In particular, I introduced a new generalization of the integrate-and-fire model (GIF) along with a convex fitting method allowing for efficient estimation of model parameters. Despite its relative simplicity I show that this neuron model is able to reproduce neuron behaviors with a high degree of accuracy. Moreover, using this method I was able to show that cortical neurons are equipped with two distinct adaptation mechanisms. First, a spike-triggered current that captures the complex influx of ions generated after the emission of a spike. While the second is a movement of the firing threshold, which possibly reflects the slow inactivation of sodium channels induced by the spiking activity. The precise dynamics of these adaptation processes is cell-type specific, explaining the difference of firing activity reported in different neuron types. Consequently, neuronal types can be classified based on model parameters. In Pyramidal neurons spike-dependent adaptation lasts for seconds and follows a scale-free dynamics, which is optimally tuned to encodes the natural inputs that pyramidal neurons receive in vivo. Finally using an extended version of the GIF model, I show that adaptation is not only a spike-dependent phenomenon, but also acts at the subthreshold level. In Pyramidal neurons the dynamics of the firing threshold is influenced by the subthreshold membrane potential. Spike-dependent and voltage-dependent adaptation interact in an activity-dependent way to ultimately shape the filtering properties of the membrane on the input statistics. Equipped with such a mechanism, Pyramidal neurons behave as integrators at low inputs and as a coincidence detectors at high inputs, maintaining sensitivity to input fluctuations across all regimes

Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons

The way in which single neurons transform input into output spike trains has fundamental consequences for network coding. Theories and modeling studies based on standard Integrate-and-Fire models implicitly assume that, in response to increasingly strong inputs, neurons modify their coding strategy by progressively reducing their selective sensitivity to rapid input fluctuations. Combining mathematical modeling with in vitro experiments, we demonstrate that, in L5 pyramidal neurons, the firing threshold dynamics adaptively adjust the effective timescale of somatic integration in order to preserve sensitivity to rapid signals over a broad range of input statistics. For that, a new Generalized Integrate-and-Fire model featuring nonlinear firing threshold dynamics and conductance-based adaptation is introduced that outperforms state-of-the-art neuron models in predicting the spiking activity of neurons responding to a variety of in vivo-like fluctuating currents. Our model allows for efficient parameter extraction and can be analytically mapped to a Generalized Linear Model in which both the input filter—describing somatic integration—and the spike-history filter—accounting for spike-frequency adaptation—dynamically adapt to the input statistics, as experimentally observed. Overall, our results provide new insights on the computational role of different biophysical processes known to underlie adaptive coding in single neurons and support previous theoretical findings indicating that the nonlinear dynamics of the firing threshold due to Na+-channel inactivation regulate the sensitivity to rapid input fluctuations

Temporal whitening by power-law adaptation in neocortical neurons

Spike-frequency adaptation (SFA) is widespread in the CNS, but its function remains unclear. In neocortical pyramidal neurons, adaptation manifests itself by an increase in the firing threshold and by adaptation currents triggered after each spike. Combining electrophysiological recordings in mice with modeling, we found that these adaptation processes lasted for more than 20 s and decayed over multiple timescales according to a power law. The power-law decay associated with adaptation mirrored and canceled the temporal correlations of input current received in vivo at the somata of layer 2/3 somatosensory pyramidal neurons. These findings suggest that, in the cortex, SFA causes temporal decorrelation of output spikes (temporal whitening), an energy-efficient coding procedure that, at high signal-to-noise ratio, improves the information transfer

Karakteristik Nanoemulsi Minyak Sawit Merah Yang Diperkaya Beta Karoten

Red palm oil (RPO) and β-carotene are insoluble in water. It makescan be used to improve RPO and βThis research is aimed to produce stable RPO nanoemulsion enriched withβ-carotene. The research was conducted in the SEAFAST CENTERLaboratory, Bogor Agriculture University from January to Septemberfollowing steps, i.e. enrichment of RPO with βusing a high pressure homogenizer at a pressure of 34.5 MPa in 10 cycles.The ratio of RPO and water in the mixture were 5 : 95; 7.5 : 92.5; and 10 :10% (w/w) of the total emulsions. In the second stage, nanoemulsionswere prepared on various RPO percentage of 2, 4, and 6% (w/w) andhad a droplet size from 115.1 to 145.2 nm and stable. Nanoemulsions wereresulting from the second stage had droplet size from 94.9 to 125.5 nm,and β-carotene content were 47.6 to 130.9 mg/l. Droplet size ofnanoemulsions is less than 125 nm. It can be produced with RPO an

Pembuatan Ethanol Dari Jerami Padi Dengan Proses Hidrolisis Dan Fermentasi

Jerami Padi banyak mengandung Pati, Selulosa dan Glukosa yang cukup tinggi. Alkohol dapat dihasilkan dari tanaman yang banyak mengandung senyawa selulosa dengan menggunakan bantuan aktivitas mikroba salah satu jenis tanamannya adalah jerami padi Tujuan penelitian ini yaitu untuk mendapatkan kadar ethanol yang terbaik pada jerami padi dengan menggunakan proses hidrolisis dan fermentasi. Kondisi yang ditetapkan larutan Hidrolisis sebanyak 2500 ml, pH hidrolisis 3, waktu hidrolisis 2 hari, dan pH fermentasi sebesar 4,5, sedangkan peubah yang dijalankan adalah waktu fermentasi (2,3,4,5,6,7 (hari)), berat jerami padi (40,50,60 (gram)), dan volume stater yang ditambahkan (8%, 10%, 12%, kali volume cairan fermentasi). Hasil penelitian menunjukkan bahwa kondisi terbaik pada berat jerami 50 gram dengan volume stater yang ditambahkan sebanyak 12% volume cairan fermentasi yang difermentasi selama 7 hari yang menghasilkan kadar ethanol sebesar 12,89%. Jerami padi dapat digunakan sebagai bahan baku alternatif pembuatan bioethanol

Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models

Single-neuron models are useful not only for studying the emergent properties of neural circuits in large-scale simulations, but also for extracting and summarizing in a principled way the information contained in electrophysiological recordings. Here we demonstrate that, using a convex optimization procedure we previously introduced, a Generalized Integrate-and-Fire model can be accurately fitted with a limited amount of data. The model is capable of predicting both the spiking activity and the subthreshold dynamics of different cell types, and can be used for online characterization of neuronal properties. A protocol is proposed that, combined with emergent technologies for automatic patch-clamp recordings, permits automated, in vitro high-throughput characterization of single neurons

Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms

Mensi S, Naud R, Pozzorini C, Avermann M, Petersen CCH, Gerstner W. Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms. J Neurophysiol 107: 1756-1775, 2012. First published December 7, 2011; doi:10.1152/jn.00408.2011.-Cortical information processing originates from the exchange of action potentials between many cell types. To capture the essence of these interactions, it is of critical importance to build mathematical models that reflect the characteristic features of spike generation in individual neurons. We propose a framework to automatically extract such features from current-clamp experiments, in particular the passive properties of a neuron (i.e., membrane time constant, reversal potential, and capacitance), the spike-triggered adaptation currents, as well as the dynamics of the action potential threshold. The stochastic model that results from our maximum likelihood approach accurately predicts the spike times, the subthreshold voltage, the firing patterns, and the type of frequency-current curve. Extracting the model parameters for three cortical cell types revealed that cell types show highly significant differences in the time course of the spike-triggered currents and moving threshold, that is, in their adaptation and refractory properties but not in their passive properties. In particular, GABAergic fast-spiking neurons mediate weak adaptation through spike-triggered currents only, whereas regular spiking excitatory neurons mediate adaptation with both moving threshold and spike-triggered currents. GABAergic nonfast-spiking neurons combine the two distinct adaptation mechanisms with reduced strength. Differences between cell types are large enough to enable automatic classification of neurons into three different classes. Parameter extraction is performed for individual neurons so that we find not only the mean parameter values for each neuron type but also the spread of parameters within a group of neurons, which will be useful for future large-scale computer simulations