Triggered Correlation

Boer, E. de; Kuyper, P

doi:10.1109/tbme.1968.4502561

Cited by 427 publications

(278 citation statements)

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“…The matrix operations effected on the right side of this equation, X T r observed , are the operations of reverse correlation (de Boer and Kuyper, 1968;Jones and Palmer, 1987). The vector of 625 RF weights (plus a constant to account for the mean rate) that this operation produces is the sum of the stimulus snapshots (within the 10 ϫ 10 mm region around the RF) when action potentials occurred.…”

Section: Methodsmentioning

confidence: 99%

Velocity Invariance of Receptive Field Structure in Somatosensory Cortical Area 3b of the Alert Monkey

DiCarlo

Johnson

1999

J. Neurosci.

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This is the second in a series of studies of the neural representation of tactile spatial form in cortical area 3b of the alert monkey. We previously studied the spatial structure of 330 area 3b neuronal receptive fields (RFs) on the fingerpad with random dot patterns scanned at one velocity (40 mm/sec; DiCarlo et al., 1998). Here, we analyze the temporal structure of 84 neuronal RFs by studying their spatial structure at three scanning velocities (20, 40, and 80 mm/sec). As in the previous study, most RFs contained a single, central, excitatory region and one or more surrounding or flanking inhibitory regions. The mean time delay between skin stimulation and its excitatory effect was 15.5 msec. Except for differences in mean rate, each neuron's response and the spatial structure of its RF were essentially unaffected by scanning velocity. This is the expected outcome when excitatory and inhibitory effects are brief and synchronous. However, that interpretation is consistent neither with the reported timing of excitation and inhibition in somatosensory cortex nor with the third study in this series, which investigates the effect of scanning direction and shows that one component of inhibition lags behind excitation. We reconcile these observations by showing that overlapping (in-field) inhibition delayed relative to excitation can produce RF spatial structure that is unaffected by changes in scanning velocity. Regardless of the mechanisms, the velocity invariance of area 3b RF structure is consistent with the velocity invariance of tactile spatial perception (e.g., roughness estimation and form recognition). Key words: receptive field; somatosensory; cortex; area 3b; SI; tactile; velocity; monkey; reverse correlationThe study reported here concerns the temporal and spatial response properties of neurons in area 3b of primary somatosensory cortex. Previous studies of area 3b have shown that each point in a neuron's cutaneous receptive field (RF) may give rise to excitation, inhibition, or both (Mountcastle and Powell, 1959;Laskin and Spencer, 1979; Gardner and Costanzo, 1980b,c;DiCarlo et al., 1998), that there is a delay between the cutaneous stimulus and the response (Mountcastle and Powell, 1959;Laskin and Spencer, 1979;Gardner and Costanzo, 1980a), that the excitatory and inhibitory effects may persist for variable periods (Laskin and Spencer, 1979;Gardner and Costanzo, 1980b), and that the timing of excitation and inhibition arising from a single cutaneous site may be different (Laskin and Spencer, 1979;Gardner and Costanzo, 1980b). Thus, area 3b RFs have temporal, as well as spatial structure. Although the precise relationship between the spatial and temporal parameters of a neuron's response and the RF estimated with a scanned stimulus is complex (see Appendix A), the general effects of temporal delay between the stimulus and response are as follows. Because we do not initially know the delay between the stimulus and each response component, our RF estimation procedure assigns each response component to the...

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Section: Methodsmentioning

confidence: 99%

Velocity Invariance of Receptive Field Structure in Somatosensory Cortical Area 3b of the Alert Monkey

DiCarlo

Johnson

1999

J. Neurosci.

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“…A method in widespread use to characterize the responses of neurons is the spike-triggered average [4][5][6][7][8], also widely known as the "reverse correlation" method. In this method, the snippet of the stimulus preceding each spike is time-shifted (so that the spike corresponds to time 0) and then averaged.…”

Section: Spike-triggered Methodsmentioning

confidence: 99%

“…where the phase ϕ is given from the Gabor transform of the input x(t): (6) We shall abstract a neuronal unit to follow these rules: our model auditory neuron will fire whenever the ω ins estimate is within a certain range ω CF ± Δ for some ω in the range ω CF ± 1/ σ. When this happens, our neuron fires at time t ins , i.e., the time of firing is corrected by the local group delay of the stimulus.…”

Section: A Timing-based Auditory Modelmentioning

confidence: 99%

Decoding spike timing: The differential reverse-correlation method

Tkačik

Magnasco

2008

Biosystems

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It is widely acknowledged that detailed timing of action potentials is used to encode information, for example in auditory pathways; however the computational tools required to analyze encoding through timing are still in their infancy. We present a simple example of encoding, based on a recent model of time-frequency analysis, in which units fire action potentials when a certain condition is met, but the timing of the action potential depends also on other features of the stimulus. We show that, as a result, spike-triggered averages are smoothed so much they do not represent the true features of the encoding. Inspired by this example, we present a simple method, differential reverse correlations, that can separate an analysis of what causes a neuron to spike, and what controls its timing. We analyze with this method the leaky integrate-and-fire neuron and show the method accurately reconstructs the model's kernel.The receptive field of a sensory neuron is defined as an area in the stimulus parameter space in which the presence of a stimulus significantly affects the firing behavior of the neuron. For example, visual neurons may fire when a stimulus at a given position in space and with a given orientation or velocity is presented; in this case, the receptive field of that neuron will be a region in the (5-dimensional) space of position, orientation and velocity.Methods to reconstruct receptive fields are many, and the details of the receptive field thus reconstructed are often dependent on which method is used, underscoring that the definition of a receptive field given above is rather loose. For example, in primary visual cortex there is a distinction between the "classical" receptive field (namely, the location and orientation where a single small line segment on a uniform background will, when presented by itself, elicit firing) and various "non-classical" receptive fields, i.e. regions of stimulus space where the presentation of a single segment can alter sub-threshold electrical activity, or where the presentation of a second segment alters the firing rate elicited by a first segment located within the classical receptive field [1]. This distinction dates back to Kuffler, who in 1953 observed the effect of lateral inhibition by illuminating the retina with two spots of light instead of only one, and consequently stated that "not only the areas from which responses can actually be set up by retinal illumination may be included in a definition of the receptive field but also all areas which show a functional connection, by an inhibitory or excitatory effect on a ganglion cell" [2,3].In order to accelerate the characterization of receptive fields, various approaches are often employed in which stimuli are drawn at random from the stimulus parameter space and presented to a given neuron, whereupon all those that elicit firing are averaged together. This method is thus referred to as spike-triggered averaging of the stimuli [4]. While superficially

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“…The most common use of white noise analysis has been to analyze the response properties of single neurons [11,4,5,8,9,2,3,16,18,7,6]. Recently, researchers have begun to apply the techniques of white noise analysis to simultaneous measurements of multiple neurons [15,1,19], although without explicitly modeling neural connectivity.…”

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confidence: 99%

“…The integral is taken over the intersection of the two half planes x · u > a 4 Since stimulus is rotationally invariant, we can rotate axis so that h i 1 is parallel to the first axis and h i−k When the nonlinearity is an error function (Eq. (2.3)), we substitute into Eq.…”

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confidence: 99%

White Noise Analysis of Coupled Linear-Nonlinear Systems

Nykamp¹

2003

SIAM J. Appl. Math.

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Abstract. We present an asymptotic analysis of two coupled linear-nonlinear systems. Through measuring first and second input-output statistics of the systems in response to white noise input, one can completely characterize the systems and their coupling. The proposed model is similar to a widely used phenomenological model of neurons in response to sensory stimulation and may be used to help characterize neural circuitry in sensory brain regions.Key words. neural networks, correlations, Weiner analysis, white noise AMS subject classification. 92C201. Introduction. Most electrophysiology data from intact mammalian brains is recorded using an extracellular electrode which remains outside neurons. When the electrode is positioned near a neuron, it can record the neuron's output events, called spikes, because spike magnitudes are sufficiently large. The internal state of a neuron, including small fluctuations in response to its inputs, cannot be measured.When only output spikes are measurable, one cannot directly measure the effect of a connection from one neuron to another. If neuron 1 is connected to neuron 2, then an output spike of neuron 1 will perturb the internal state of neuron 2. If the internal state cannot be measured, this perturbation can be inferred only via its effect on the spike times of neuron 2. In general, the spike times of a neuron will be a function of many inputs coming from many other neurons. This complexity makes reliable inferences on the structure of neuron circuits from spike time data a formidable challenge.Explicit mathematical models may lead to tools that can address this challenge. Through model analysis, one may develop methods to infer aspects of network structure from spike times, subject to the validity of the underlying model. In this paper, we derive a method to reconstruct the connectivity between two isolated neurons based on a simple linear-nonlinear model (see below) of neural response to white noise. Although this model greatly simplifies the reality of the brain's neural networks, the results from this analysis can be used to analyze neurophysiology data provided that they are interpreted within the limitations of the model [12].Numerous researchers have used white noise analysis to describe the response of neurons to a stimulus. The most common use of white noise analysis has been to analyze the response properties of single neurons [11,4,5,8,9,2,3,16,18,7,6]. Recently, researchers have begun to apply the techniques of white noise analysis to simultaneous measurements of multiple neurons [15,1,19], although without explicitly modeling neural connectivity. In Ref.[12], we showed how, in white noise experiments, interpretation of spike time data is especially difficult because standard correlation measures confound stimulus and connectivity effects. We demonstrated correlation measures that remove the stimulus effects based on the linear-nonlinear model.In this paper, we present the asymptotic analysis of the linear-nonlinear model that underlies the correlation measur...

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Triggered Correlation

Cited by 427 publications

References 8 publications

Velocity Invariance of Receptive Field Structure in Somatosensory Cortical Area 3b of the Alert Monkey

Velocity Invariance of Receptive Field Structure in Somatosensory Cortical Area 3b of the Alert Monkey

Decoding spike timing: The differential reverse-correlation method

White Noise Analysis of Coupled Linear-Nonlinear Systems

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