Integration of Vibrotactile Signals for Whisker-Related Perception in Rats Is Governed by Short Time Constants: Comparison of Neurometric and Psychometric Detection Performance

Stüttgen, Maik C.; Schwarz, Cornelius

doi:10.1523/jneurosci.3943-09.2010

Cited by 60 publications

(63 citation statements)

References 42 publications

(58 reference statements)

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“…However, shorter integration times may suffice to produce fast and accurate detection. Previous findings in humans and rats (25,26) suggest that monkeys may be capable of detecting a stimulus that consists of a single sinusoidal pulse. If so, it will be interesting to test whether their accuracy drops relative to that with 10-pulse stimuli, as predicted from the neurometric curves obtained here.…”

Section: Discussionmentioning

confidence: 97%

Transformation of the neural code for tactile detection from thalamus to cortex

Vázquez

Salinas

Romo

2013

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

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To understand how sensory-driven neural activity gives rise to perception, it is essential to characterize how various relay stations in the brain encode stimulus presence. Neurons in the ventral posterior lateral (VPL) nucleus of the somatosensory thalamus and in primary somatosensory cortex (S1) respond to vibrotactile stimulation with relatively slow modulations (∼100 ms) of their firing rate. In addition, faster modulations (∼10 ms) time-locked to the stimulus waveform are observed in both areas, but their contribution to stimulus detection is unknown. Furthermore, it is unclear whether VPL and S1 neurons encode stimulus presence with similar accuracy and via the same response features. To address these questions, we recorded single neurons while trained monkeys judged the presence or absence of a vibrotactile stimulus of variable amplitude, and their activity was analyzed with a unique decoding method that is sensitive to the time scale of the firing rate fluctuations. We found that the maximum detection accuracy of single neurons is similar in VPL and S1. However, VPL relies more heavily on fast rate modulations than S1, and as a consequence, the neural code in S1 is more tolerant: its performance degrades less when the readout method or the time scale of integration is suboptimal. Therefore, S1 neurons implement a more robust code, one less sensitive to the temporal integration window used to infer stimulus presence downstream. The differences between VPL and S1 responses signaling the appearance of a stimulus suggest a transformation of the neural code from thalamus to cortex. (1-4), and as the signal is relayed from one layer to the next, its representation in spatiotemporal patterns of activity may change (1). Previous studies have shown that, when a tactile vibratory stimulus is presented, neurons in the ventral posterior lateral (VPL) nucleus in the somatosensory thalamus and primary somatosensory cortex (S1, areas 3b and 1) respond by modulating their firing rates computed over a time scale of hundreds of milliseconds (5-9). Because these slow firing rate modulations occurring within relatively long time windows depend on amplitude and vibration frequency, they encode these key stimulus features. In addition, to varying degrees, evoked spikes in both areas also tend to be synchronized to the waveform of the applied mechanical stimulus, which, for frequencies <40 Hz, varies on a time scale of tens of milliseconds. This entrainment, which corresponds to fast firing rate modulations, also depends on stimulus amplitude (9), and so it too carries information about stimulus presence. However, it is unknown whether both the slow (∼100 ms) and fast (∼10 ms) modulations in firing rate contribute to perceptual performance during stimulus detection. Moreover, it is not clear whether the two time scales are equally important in VPL and S1, nor whether the capacity to signal stimulus presence changes from one area to the next.To investigate these issues, we recorded activity from single neurons in the VPL nucle...

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Section: Discussionmentioning

confidence: 97%

Transformation of the neural code for tactile detection from thalamus to cortex

Vázquez

Salinas

Romo

2013

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

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“…On the one hand, visual cortical neurons can show sustained sensory responses, but more transient responses, implying a stronger reliance on timing-based codes, have been observed in other primary sensory areas such as rodent somatosensory (barrel) and auditory cortex (51)(52)(53). However, in these other areas temporal integration is possible [e.g, when sensory stimuli are combined (51) and direct electrical stimuli are used (54)].…”

Section: Discussion Linear Population Codes Can Arise From Weak Inputmentioning

confidence: 99%

Cortical neural populations can guide behavior by integrating inputs linearly, independent of synchrony

Histed

Maunsell

2013

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

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Neurons are sensitive to the relative timing of inputs, both because several inputs must coincide to reach spike threshold and because active dendritic mechanisms can amplify synchronous inputs. To determine if input synchrony can influence behavior, we trained mice to report activation of excitatory neurons in visual cortex using channelrhodopsin-2. We used light pulses that varied in duration from a few milliseconds to 100 ms and measured neuronal responses and animals' detection ability. We found detection performance was well predicted by the total amount of light delivered. Short pulses provided no behavioral advantage, even when they concentrated evoked spikes into an interval a few milliseconds long. Arranging pulses into trains of varying frequency from beta to gamma also produced no behavioral advantage. Light intensities required to drive behavior were low (at low intensities, channelrhodopsin-2 conductance varies linearly with intensity), and the accompanying changes in firing rate were small (over 100 ms, average change: 1.1 spikes per s). Firing rate changes varied linearly with pulse intensity and duration, and behavior was predicted by total spike count independent of temporal arrangement. Thus, animals' detection performance reflected the linear integration of total input over 100 ms. This behavioral linearity despite neurons' nonlinearities can be explained by a population code using noisy neurons. Ongoing background activity creates probabilistic spiking, allowing weak inputs to change spike probability linearly, with little amplification of coincident input. Summing across a population then yields a total spike count that weights inputs equally, regardless of their arrival time.neuronal circuits | population coding | optogenetics | mouse M any neurons in the brain receive thousands of inputs spread over their dendritic trees, and several of those inputs need to be active simultaneously to generate a spike reliably (1, 2). In this way, coincident synaptic inputs can be amplified relative to asynchronous inputs. In addition to this nonlinearity caused by spike threshold, other active processes such as dendritic calcium spikes (3-5) can preferentially amplify synchronous inputs. A variety of ways that timing could impact network function have been explored, including oscillatory synchronization (6, 7), strong cascading effects of individual neurons or synapses (8-11), and information encoding via temporal patterns (12). In the songbird, for example, song neurons receive precisely timed coincident input that recruits active calcium conductances, generating strong, reliable spike bursts that control the song (13, 14). However, synchrony might not always be critical for neuronal processing. Several types of models show that neuronal networks can be insensitive to precise spike timing even though individual neurons are highly sensitive. Most of these models rely on strong (15) or numerous (16-18) inputs and amplification of small perturbations, leading to chaotic network dynamics and noisy single neu...

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“…5). To incorporate the fact that neuronal signals are not integrated indefinitely, but rather over tens or hundreds of milliseconds [36, 37], in making a perceptual decision, we used an exponentially decaying time window, w n , whose time course was a parameter. …”

Section: Methodsmentioning

confidence: 99%

A computational model that predicts behavioral sensitivity to intracortical microstimulation

2016

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Objective Intracortical microstimulation (ICMS) is a powerful tool to investigate the neural mechanisms of perception and can be used to restore sensation for patients who have lost it. While sensitivity to ICMS has previously been characterized, no systematic framework has been developed to summarize the detectability of individual ICMS pulse trains or the discriminability of pairs of pulse trains. Approach We develop a simple simulation that describes the responses of a population of neurons to a train of electrical pulses delivered through a microelectrode. We then perform an ideal observer analysis on the simulated population responses to predict the behavioral performance of non-human primates in ICMS detection and discrimination tasks. Main results Our computational model can predict behavioral performance across a wide range of stimulation conditions with high accuracy (R2 = 0.97) and generalizes to novel ICMS pulse trains that were not used to fit its parameters. Furthermore, the model provides a theoretical basis for the finding that amplitude discrimination based on ICMS violates Weber's law. Significance The model can be used to characterize the sensitivity to ICMS across the range of perceptible and safe stimulation regimes. As such, it will be a useful tool for both neuroscience and neuroprosthetics.

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Integration of Vibrotactile Signals for Whisker-Related Perception in Rats Is Governed by Short Time Constants: Comparison of Neurometric and Psychometric Detection Performance

Cited by 60 publications

References 42 publications

Transformation of the neural code for tactile detection from thalamus to cortex

Transformation of the neural code for tactile detection from thalamus to cortex

Cortical neural populations can guide behavior by integrating inputs linearly, independent of synchrony

A computational model that predicts behavioral sensitivity to intracortical microstimulation

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