scholarly journals Designing optimal stimuli to control neuronal spike timing

2011 ◽  
Vol 106 (2) ◽  
pp. 1038-1053 ◽  
Author(s):  
Yashar Ahmadian ◽  
Adam M. Packer ◽  
Rafael Yuste ◽  
Liam Paninski
Keyword(s):  
Spike Train ◽  
Laser Light ◽  
Pyramidal Cells ◽  
Electrical Current ◽  
Optimization Methods ◽  
Spike Trains ◽  
Spike Timing ◽  
Safety Measures ◽  
Physiological Constraints ◽  
Cortical Slices

Recent advances in experimental stimulation methods have raised the following important computational question: how can we choose a stimulus that will drive a neuron to output a target spike train with optimal precision, given physiological constraints? Here we adopt an approach based on models that describe how a stimulating agent (such as an injected electrical current or a laser light interacting with caged neurotransmitters or photosensitive ion channels) affects the spiking activity of neurons. Based on these models, we solve the reverse problem of finding the best time-dependent modulation of the input, subject to hardware limitations as well as physiologically inspired safety measures, that causes the neuron to emit a spike train that with highest probability will be close to a target spike train. We adopt fast convex constrained optimization methods to solve this problem. Our methods can potentially be implemented in real time and may also be generalized to the case of many cells, suitable for neural prosthesis applications. With the use of biologically sensible parameters and constraints, our method finds stimulation patterns that generate very precise spike trains in simulated experiments. We also tested the intracellular current injection method on pyramidal cells in mouse cortical slices, quantifying the dependence of spiking reliability and timing precision on constraints imposed on the applied currents.

scholarly journals Correlations Without Synchrony

1999 ◽  
Vol 11 (7) ◽  
pp. 1537-1551 ◽  
Author(s):  
Carlos D. Brody
Keyword(s):  
Spike Train ◽  
Response Latency ◽  
Quantitative Methods ◽  
Neuronal Excitability ◽  
Spike Trains ◽  
Spike Timing ◽  
Previous Article ◽  
Timing Synchronization

Peaks in spike train correlograms are usually taken as indicative of spike timing synchronization between neurons. Strictly speaking, however, a peak merely indicates that the two spike trains were not independent. Two biologically plausible ways of departing from independence that are capable of generating peaks very similar to spike timing peaks are described here: covariations over trials in response latency and covariations over trials in neuronal excitability. Since peaks due to these interactions can be similar to spike timing peaks, interpreting a correlogram may be a problem with ambiguous solutions. What peak shapes do latency or excitability interactions generate? When are they similar to spike timing peaks? When can they be ruled out from having caused an observed correlogram peak? These are the questions addressed here. The previous article in this issue proposes quantitative methods to tell cases apart when latency or excitability covariations cannot be ruled out.

Auditory Discrimination of Amplitude Modulations Based on Metric Distances of Spike Trains

2007 ◽  
Vol 97 (4) ◽  
pp. 3082-3092 ◽  
Author(s):  
Sandra Wohlgemuth ◽  
Bernhard Ronacher
Keyword(s):  
Spike Train ◽  
Temporal Resolution ◽  
Transfer Functions ◽  
Time Window ◽  
Discrimination Performance ◽  
Spike Trains ◽  
Spike Timing ◽  
Spike Count ◽  
Communication Signals ◽  
Metric Distances

Sound envelope cues play a crucial role for the recognition and discrimination of communication signals in diverse taxa, such as vertebrates and arthropods. Using a classification based on metric similarities of spike trains we investigate how well amplitude modulations (AMs) of sound signals can be distinguished at three levels of the locust's auditory pathway: receptors and local and ascending neurons. The spike train metric has the advantage of providing information about the necessary evaluation time window and about the optimal temporal resolution of processing, thereby yielding clues to possible coding principles. It further allows one to disentangle the respective contributions of spike count and spike timing to the fidelity of discrimination. These results are compared with the traditional paradigm using modulation transfer functions. Spike trains of receptors and two primary-like local interneurons enable an excellent discrimination of different AM frequencies, up to about 150 Hz. In these neurons discriminability depends almost completely on the timing of spikes, which must be evaluated with a temporal resolution of <5 ms. Even short spike-train segments of 150 ms, equivalent to five to eight spikes, suffice for a high (70%) discrimination performance. For the third level of processing, the ascending interneurons, the overall discrimination accuracy is reduced. Spike count differences become more important for the discrimination whereas the exact timing of spikes contributes less. This shift in temporal resolution does not primarily depend on the investigated stimulus space. Rather it appears to reflect a transformation of how amplitude modulations are represented at more central stages of processing.

scholarly journals Background Synaptic Conductance and Precision of EPSP-Spike Coupling at Pyramidal Cells

2005 ◽  
Vol 93 (6) ◽  
pp. 3248-3256 ◽  
Author(s):  
Veronika Zsiros ◽  
Shaul Hestrin
Keyword(s):  
Time Course ◽  
Pyramidal Cells ◽  
Spike Timing ◽  
Synaptic Activity ◽  
Synaptic Conductance ◽  
Temporal Precision ◽  
Voltage Dependent ◽  
Cortical Slices

The temporal precision of converting excitatory postsynaptic potentials (EPSPs) into spikes at pyramidal cells is critical for the coding of information in the cortex. Several in vitro studies have shown that voltage-dependent conductances in pyramidal cells can prolong the EPSP time course resulting in an imprecise EPSP-spike coupling. We have used dynamic-clamp techniques to mimic the in vivo background synaptic conductance in cortical slices and investigated how the ongoing synaptic activity may affect the EPSP time course near threshold and the EPSP spike coupling. We report here that background synaptic conductance dramatically diminished the depolarization related prolongation of the EPSPs in pyramidal cells and improved the precision of spike timing. Furthermore, we found that background synaptic conductance can affect the interaction among action potentials in a spike train. Thus the level of ongoing synaptic activity in the cortex may regulate the capacity of pyramidal cells to process temporal information.

Dendritic Modulation of Burst-Like Firing in Sensory Neurons

2001 ◽  
Vol 85 (1) ◽  
pp. 10-22 ◽  
Author(s):  
Joseph Bastian ◽  
Jerry Nguyenkim
Keyword(s):  
Spike Train ◽  
Sensory Neurons ◽  
Lateral Line ◽  
Interspike Interval ◽  
Pyramidal Cells ◽  
Spike Trains ◽  
Firing Frequency ◽  
Dendritic Morphology ◽  
Spontaneous Firing ◽  
Apical Dendrites

This report describes the variability of spontaneous firing characteristics of sensory neurons, electrosensory lateral line lobe (ELL) pyramidal cells, within the electrosensory lateral line lobe of weakly electric fish in vivo. We show that these cells' spontaneous firing frequency, measures of spike train regularity (interspike interval coefficient of variation), and the tendency of these cells to produce bursts of action potentials are correlated with the size of the cell's apical dendritic arbor. We also show that bursting behavior may be influenced or controlled by descending inputs from higher centers that provide excitatory and inhibitory inputs to the pyramidal cells' apical dendrites. Pyramidal cells were classified as “bursty” or “nonbursty” according to whether or not spike trains deviated significantly from the expected properties of random (Poisson) spike trains of the same average firing frequency, and, in the case of bursty cells, the maximum within-burst interspike interval characteristic of bursts was determined. Each cell's probability of producing bursts above the level expected for a Poisson spike train was determined and related to spontaneous firing frequency and dendritic morphology. Pyramidal cells with large apical dendritic arbors have lower rates of spontaneous activity and higher probabilities of producing bursts above the expected level, while cells with smaller apical dendrites fire at higher frequencies and are less bursty. The effect of blocking non- N-methyl-d-aspartate (non-NMDA) glutamatergic synaptic inputs to the apical dendrites of these cells, and to local inhibitory interneurons, significantly reduced the spontaneous occurrence of spike bursts and intracellular injection of hyperpolarizing current mimicked this effect. The results suggest that bursty firing of ELL pyramidal cells may be under descending control allowing activity in electrosensory feedback pathways to influence the firing properties of sensory neurons early in the processing hierarchy.

scholarly journals Action Potential Timing Precision in Dorsal Cochlear Nucleus Pyramidal Cells

2007 ◽  
Vol 97 (6) ◽  
pp. 4162-4172 ◽  
Author(s):  
Sarah E. Street ◽  
Paul B. Manis
Keyword(s):  
Firing Rate ◽  
Cochlear Nucleus ◽  
Pyramidal Neurons ◽  
Pyramidal Cells ◽  
Spike Trains ◽  
Spike Timing ◽  
Dorsal Cochlear Nucleus ◽  
Information Encoding ◽  
Temporal Precision ◽  
Noise Current

Many studies of the dorsal cochlear nucleus (DCN) have focused on the representation of acoustic stimuli in terms of average firing rate. However, recent studies have emphasized the role of spike timing in information encoding. We sought to ascertain whether DCN pyramidal cells might employ similar strategies and to what extent intrinsic excitability regulates spike timing. Gaussian distributed low-pass noise current was injected into pyramidal cells in a brain slice preparation. The shuffled autocorrelation-based analysis was used to compute a correlation index of spike times across trials. The noise causes the cells to fire with temporal precision (SD ≅ 1–2 ms) and high reproducibility. Increasing the coefficient of variation of the noise improved the reproducibility of the spike trains, whereas increasing the firing rate of the neuron decreased the neurons' ability to respond with predictable patterns of spikes. Simulated inhibitory postsynaptic potentials superimposed on the noise stimulus enhanced spike timing for >300 ms, although the enhancement was greatest during the first 100 ms. We also found that populations of pyramidal neurons respond to the same noise stimuli with correlated spike trains, suggesting that ensembles of neurons in the DCN receiving shared input can fire with similar timing. These results support the hypothesis that spike timing can be an important aspect of information coding in the DCN.

scholarly journals A Biologically Plausible Computational Model for Auditory Object Recognition

2009 ◽  
Vol 101 (1) ◽  
pp. 323-331 ◽  
Author(s):  
Eric Larson ◽  
Cyrus P. Billimoria ◽  
Kamal Sen
Keyword(s):  
Object Recognition ◽  
Spike Train ◽  
Nearest Neighbor ◽  
Primary Auditory Cortex ◽  
Spike Trains ◽  
Coincidence Detection ◽  
Model Based ◽  
Plausible Model ◽  
Field L ◽  
Song Discrimination

Object recognition is a task of fundamental importance for sensory systems. Although this problem has been intensively investigated in the visual system, relatively little is known about the recognition of complex auditory objects. Recent work has shown that spike trains from individual sensory neurons can be used to discriminate between and recognize stimuli. Multiple groups have developed spike similarity or dissimilarity metrics to quantify the differences between spike trains. Using a nearest-neighbor approach the spike similarity metrics can be used to classify the stimuli into groups used to evoke the spike trains. The nearest prototype spike train to the tested spike train can then be used to identify the stimulus. However, how biological circuits might perform such computations remains unclear. Elucidating this question would facilitate the experimental search for such circuits in biological systems, as well as the design of artificial circuits that can perform such computations. Here we present a biologically plausible model for discrimination inspired by a spike distance metric using a network of integrate-and-fire model neurons coupled to a decision network. We then apply this model to the birdsong system in the context of song discrimination and recognition. We show that the model circuit is effective at recognizing individual songs, based on experimental input data from field L, the avian primary auditory cortex analog. We also compare the performance and robustness of this model to two alternative models of song discrimination: a model based on coincidence detection and a model based on firing rate.

Postsynaptic Receptive Field Size and Spike Threshold Determine Encoding of High-Frequency Information Via Sensitivity to Synchronous Presynaptic Activity

2009 ◽  
Vol 101 (3) ◽  
pp. 1160-1170 ◽  
Author(s):  
Jason W. Middleton ◽  
André Longtin ◽  
Jan Benda ◽  
Leonard Maler
Keyword(s):  
High Frequency ◽  
Time Window ◽  
Pyramidal Cells ◽  
Spike Trains ◽  
Small Time ◽  
Field Size ◽  
Recent Experimental Data ◽  
Neuron Models ◽  
High Frequency Signal ◽  
Unit Spike

Parallel sensory streams carrying distinct information about various stimulus properties have been observed in several sensory systems, including the visual system. What remains unclear is why some of these streams differ in the size of their receptive fields (RFs). In the electrosensory system, neurons with large RFs have short-latency responses and are tuned to high-frequency inputs. Conversely, neurons with small RFs are low-frequency tuned and exhibit longer-latency responses. What principle underlies this organization? We show experimentally that synchronous electroreceptor afferent (P-unit) spike trains selectively encode high-frequency stimulus information from broadband signals. This finding relies on a comparison of stimulus-spike output coherence using output trains obtained by either summing pairs of recorded afferent spike trains or selecting synchronous spike trains based on coincidence within a small time window. We propose a physiologically realistic decoding mechanism, based on postsynaptic RF size and postsynaptic output rate normalization that tunes target pyramidal cells in different electrosensory maps to low- or high-frequency signal components. By driving realistic neuron models with experimentally obtained P-unit spike trains, we show that a small RF is matched with a postsynaptic integration regime leading to responses over a broad range of frequencies, and a large RF with a fluctuation-driven regime that requires synchronous presynaptic input and therefore selectively encodes higher frequencies, confirming recent experimental data. Thus our work reveals that the frequency content of a broadband stimulus extracted by pyramidal cells, from P-unit afferents, depends on the amount of feedforward convergence they receive.

Bursts and recurrences of bursts in the spike trains of spontaneously active striate cortex neurons

1985 ◽  
Vol 53 (4) ◽  
pp. 926-939 ◽  
Author(s):  
C. R. Legendy ◽  
M. Salcman
Keyword(s):  
Standard Deviation ◽  
Spike Train ◽  
Striate Cortex ◽  
Quantitative Measure ◽  
Spike Trains ◽  
Spike Rate ◽  
Occurrence Rate ◽  
Implanted Electrodes ◽  
Firing Rates ◽  
The Mean

Simultaneous recordings were made from small collections (2-7) of spontaneously active single units in the striate cortex of unanesthetized cats, by means of chronically implanted electrodes. The recorded spike trains were computer scanned for bursts of spikes, and the bursts were catalogued and studied. The firing rates of the neurons ranged from 0.16 to 32 spikes/s; the mean was 8.9 spikes/s, the standard deviation 7.0 spikes/s. Bursts of spikes were assigned a quantitative measure, termed Poisson surprise (S), defined as the negative logarithm of their probability in a random (Poisson) spike train. Only bursts having S greater than 10, corresponding to an occurrence rate of about 0.01 bursts/1,000 spikes in a random spike train, were considered to be of interest. Bursts having S greater than 10 occurred at a rate of about 5-15 bursts/1,000 spikes, or about 1-5 bursts/min. The rate slightly increased with spike rate; averaging about 2 bursts/min for neurons having 3 spikes/s and about 4.5 bursts/min for neurons having 30 spikes/s. About 21% of the recorded units emitted significantly fewer bursts than the rest (below 1 burst/1,000 spikes). The percentage of these neurons was independent of spike rate. The spike rate during bursts was found to be about 3-6 times the average spike rate; about the same for longer as for shorter bursts. Bursts typically contained 10-50 spikes and lasted 0.5-2.0 s. When the number of spikes in the successively emitted bursts was listed, it was found that in some neurons these numbers were not distributed at random but were clustered around one or more preferred values. In this sense, bursts occasionally "recurred" a few times in a few minutes. The finding suggests that neurons are highly reliable. When bursts of two or more simultaneously recorded neurons were compared, the bursts often appeared to be temporally close, especially between pairs of neurons recorded by the same electrode; but bursts seldom started and ended simultaneously on two channels. Recurring bursts emitted by one neuron were occasionally accompanied by time-locked recurring bursts by other neurons.

scholarly journals A New Multineuron Spike Train Metric

2008 ◽  
Vol 20 (6) ◽  
pp. 1495-1511 ◽  
Author(s):  
Conor Houghton ◽  
Kamal Sen
Keyword(s):  
Spike Train ◽  
Simulated Data ◽  
Spike Trains ◽  
The Other

The Victor-Purpura spike train metric has recently been extended to a family of multineuron metrics and used to analyze spike trains recorded simultaneously from pairs of proximate neurons. The metric is one of the two metrics commonly used for quantifying the distance between two spike trains; the other is the van Rossum metric. Here, we suggest an extension of the van Rossum metric to a multineuron metric. We believe this gives a metric that is both natural and easy to calculate. Both types of multineuron metric are applied to simulated data and are compared.

A Computational Model as Neurodecoder Based on Synchronous Oscillation in the Visual Cortex

2003 ◽  
Vol 15 (10) ◽  
pp. 2399-2418 ◽  
Author(s):  
Zhao Songnian ◽  
Xiong Xiaoyun ◽  
Yao Guozheng ◽  
Fu Zhi
Keyword(s):  
Visual Cortex ◽  
Computational Model ◽  
Visual Information ◽  
Spike Trains ◽  
Spike Timing ◽  
Contour Detection ◽  
System Level ◽  
Neuronal Spike ◽  
Neuronal Spike Trains ◽  
External Stimuli

Based on synchronized responses of neuronal populations in the visual cortex to external stimuli, we proposed a computational model consisting primarily of a neuronal phase-locked loop (NPLL) and multiscaled operator. The former reveals the function of synchronous oscillations in the visual cortex. Regardless of which of these patterns of the spike trains may be an average firing-rate code, a spike-timing code, or a rate-time code, the NPLL can decode original visual information from neuronal spike trains modulated with patterns of external stimuli, because a voltage-controlled oscillator (VCO), which is included in the NPLL, can precisely track neuronal spike trains and instantaneous variations, that is, VCO can make a copy of an external stimulus pattern. The latter, however, describes multi-scaled properties of visual information processing, but not merely edge and contour detection. In this study, in which we combined NPLL with a multiscaled operator and maximum likelihood estimation, we proved that the model, as a neurodecoder, implements optimum algorithm decoding visual information from neuronal spike trains at the system level. At the same time, the model also obtains increasingly important supports, which come from a series of experimental results of neurobiology on stimulus-specific neuronal oscillations or synchronized responses of the neuronal population in the visual cortex. In addition, the problem of how to describe visual acuity and multiresolution of vision by wavelet transform is also discussed. The results indicate that the model provides a deeper understanding of the role of synchronized responses in decoding visual information.

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