Processing of Temporal Information in the Brain

Carr, Catherine E.

doi:10.1146/annurev.ne.16.030193.001255

Cited by 298 publications

(182 citation statements)

References 76 publications

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“…In the domain of temporal processing, behavioural studies have revealed that animals can use extremely small time intervals in computations (Simmons 1979;Rose & Heiligenberg 1985;Carr 1993). Thus, the relative slowness of neural processing does not seem to be a necessary consequence of a general limitation of neurobiological processes.…”

Section: Introductionmentioning

confidence: 99%

Compensating time delays with neural predictions: are predictions sensory or motor?

Nijhawan

2009

Phil. Trans. R. Soc. A.

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Neural delays are a general property of computations carried out by neural circuits. Delays are a natural consequence of temporal summation and coding used by the nervous system to integrate information from multiple resources. For adaptive behaviour, however, these delays must be compensated. In order to sense and interact with moving objects, for example, the visual system must predict the future position of the object to compensate for delays. In this paper, we address two critical questions concerning the implementation of the compensation mechanisms in the brain, namely, where does compensation occur and how is it realized. We present evidence showing that compensation can happen in both the motor and sensory systems, and that compensation using 'diagonal neural pathways' is a suitable strategy for implementing compensation in the visual system. In this strategy, neural signals in the early stage of information processing are sent to the future cortical positions that correspond to the distance the object will travel in the period of transmission delay. We propose a computational model to elucidate this using the retinal visual information pathway.

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Section: Introductionmentioning

confidence: 99%

Compensating time delays with neural predictions: are predictions sensory or motor?

Nijhawan

2009

Phil. Trans. R. Soc. A.

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“…Envelopes are also found in natural visual images and are presumably used by the brain in order to distinguish contrast-based visual contours (Grosof et al, 1993;Mareschal and Baker, 1998;Tanaka and Ohzawa, 2006). Another example of a nonlinear code consists of temporal coding where the neural response displays precision at time scales that are smaller than those contained in the stimulus waveform and has been observed in a variety of systems (Carr and Konishi, 1990;Carr, 1993;Joseph and Hyson, 1993;Johansson and Birznieks, 2004;Jones et al, 2004). The mechanisms that underlie the generation of nonlinear codes are poorly understood in general and this is particularly the case for the coding of envelopes.…”

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confidence: 99%

Neural heterogeneities influence envelope and temporal coding at the sensory periphery

Savard¹,

Krahe²,

Chacron³

2011

Neuroscience

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Peripheral sensory neurons respond to stimuli containing a wide range of spatio-temporal frequencies. We investigated electroreceptor neuron coding in the gymnotiform wave-type weakly electric fish Apteronotus leptorhynchus. Previous studies used low to mid temporal frequencies (<256 Hz) and showed that electroreceptor neuron responses to sensory stimuli could be almost exclusively accounted for by linear models, thereby implying a rate code. We instead used temporal frequencies up to 425 Hz, which is in the upper behaviorally relevant range for this species. We show that electroreceptors can: (A) respond up to the highest frequencies tested and (B) display strong nonlinearities in their responses to such stimuli. These nonlinearities were manifested by the fact that the responses to repeated presentations of the same stimulus were coherent at temporal frequencies outside of those contained in the stimulus waveform. Specifically, these consisted of low frequencies corresponding to the time varying contrast or envelope of the stimulus as well as higher harmonics of the frequencies contained in the stimulus. Heterogeneities in the afferent population influenced nonlinear coding as afferents with the lowest baseline firing rates tended to display the strongest nonlinear responses. To understand the link between afferent heterogeneity and nonlinear responsiveness, we used a phenomenological mathematical model of electrosensory afferents. Varying a single parameter in the model was sufficient to account for the variability seen in our experimental data and yielded a prediction: nonlinear responses to the envelope and at higher harmonics are both due to afferents with lower baseline firing rates displaying greater degrees of rectification in their responses. This prediction was verified experimentally as we found that the coherence between the half-wave rectified stimulus and the response resembled the coherence between the responses to repeated presentations of the stimulus in our dataset. This result shows that rectification cannot only give rise to responses to low frequency envelopes but also at frequencies that are higher than those contained in the stimulus. The latter result implies that information is contained in the fine temporal structure of electroreceptor afferent spike trains. Our results show that heterogeneities in peripheral neuronal populations can have dramatic consequences on the nature of the neural code.

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“…Information is conveyed via phase-locked spikes for frequencies as high as 9 kHz. The existence of an oscillatory field potential, the ''neurophonic'' that arises at the NL borders (12), suggests the almost coincident arrival of volleys of phase-locked spikes in thousands of NM axons (10,13,23). In other words, the average conduction times from the inner ear to the NL border (called ''NL delays'' here) are almost equal.…”

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confidence: 99%

“…Many results point to an ontogenetic development that is critically driven by sensory experience (3). Here we focus on the development of maps of exclusively temporal features (4)(5)(6)(7)(8)(9)(10), where the time course of stimuli carries relevant information, in contrast to previous studies that have shown the emergence of orderly representations of spatial or spatiotemporal features. A prominent example is the map of interaural time differences (ITDs) that is used for sound localization (11)(12)(13).…”

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Formation of temporal-feature maps by axonal propagation of synaptic learning

Kempter

Leibold

Wagner

et al. 2001

Proc. Natl. Acad. Sci. U.S.A.

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Computational maps are of central importance to a neuronal representation of the outside world. In a map, neighboring neurons respond to similar sensory features. A well studied example is the computational map of interaural time differences (ITDs), which is essential to sound localization in a variety of species and allows resolution of ITDs of the order of 10 s. Nevertheless, it is unclear how such an orderly representation of temporal features arises. We address this problem by modeling the ontogenetic development of an ITD map in the laminar nucleus of the barn owl. We show how the owl's ITD map can emerge from a combined action of homosynaptic spike-based Hebbian learning and its propagation along the presynaptic axon. In spike-based Hebbian learning, synaptic strengths are modified according to the timing of pre-and postsynaptic action potentials. In unspecific axonal learning, a synapse's modification gives rise to a factor that propagates along the presynaptic axon and affects the properties of synapses at neighboring neurons. Our results indicate that both Hebbian learning and its presynaptic propagation are necessary for map formation in the laminar nucleus, but the latter can be orders of magnitude weaker than the former. We argue that the algorithm is important for the formation of computational maps, when, in particular, time plays a key role.C omputational maps (1, 2) transform information about the outside world into topographic neuronal representations. They are a key concept in information processing by the nervous system. Many results point to an ontogenetic development that is critically driven by sensory experience (3). Here we focus on the development of maps of exclusively temporal features (4-10), where the time course of stimuli carries relevant information, in contrast to previous studies that have shown the emergence of orderly representations of spatial or spatiotemporal features. A prominent example is the map of interaural time differences (ITDs) that is used for sound localization (11-13).Sound localization is important to the survival of many animal species-in particular, to those that hunt in the dark. ITDs often are used as a spatial cue. Because the range of available ITDs depends on the size of the head, ITDs are normally well below 1 ms; they can be measured with a precision of up to a few microseconds. In this article, we address the question of how temporal information from both ears can be transmitted to a site of comparison where neurons are tuned to ITDs, and how those ITD-tuned neurons can be organized in a map. A first step toward a solution was made by Jeffress (1) as early as 1948. He proposed a scheme (Fig. 1A) that combines axonal delay lines from both ears and neuronal coincidence detectors to convert ITDs into a place code where a spatial pattern of neuronal activity codes the ITD. His scheme has had a profound impact on understanding sound localization. Neurons tuned to ITDs and maps resembling the circuit envisioned by Jeffress have been observed in many animals...

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Processing of Temporal Information in the Brain

Cited by 298 publications

References 76 publications

Compensating time delays with neural predictions: are predictions sensory or motor?

Compensating time delays with neural predictions: are predictions sensory or motor?

Neural heterogeneities influence envelope and temporal coding at the sensory periphery

Formation of temporal-feature maps by axonal propagation of synaptic learning

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