Dataset Information

Effects of visual stimulation on LFPs, spikes, and LFP-spike relations in PRR.

ABSTRACT: Local field potentials (LFPs) have shown diverse relations to the spikes across different brain areas and stimulus features, suggesting that LFP-spike relationships are highly specific to the underlying connectivity of a local network. If so, the LFP-spike relationship may vary even within one brain area under the same task condition if neurons have heterogeneous connectivity with the active input sources during the task. Here, we tested this hypothesis in the parietal reach region (PRR), which includes two distinct classes of motor goal planning neurons with different connectivity to the visual input, i.e., visuomotor neurons receive stronger visual input than motor neurons. We predicted that the visual stimulation would render both the spike response and the LFP-spike relationship different between the two neuronal subpopulations. Thus we examined how visual stimulations affect spikes, LFPs, and LFP-spike relationships in PRR by comparing their planning (delay) period activity between two conditions: with or without a visual stimulus at the reach target. Neurons were classified as visuomotor if the visual stimulation increased their firing rate, or as motor otherwise. We found that the visual stimulation increased LFP power in gamma bands >40 Hz for both classes. Moreover, confirming our prediction, the correlation between the LFP gamma power and the firing rate became higher for the visuomotor than motor neurons in the presence of visual stimulation. We conclude that LFPs vary with the stimulation condition and that the LFP-spike relationship depends on a given neuron's connectivity to the dominant input sources in a particular stimulation condition.

SUBMITTER: Hwang EJ

PROVIDER: S-EPMC3075288 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature

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Publications

Effects of visual stimulation on LFPs, spikes, and LFP-spike relations in PRR.

Hwang Eun Jung EJ Andersen Richard A RA

Journal of neurophysiology 20110209 4

Local field potentials (LFPs) have shown diverse relations to the spikes across different brain areas and stimulus features, suggesting that LFP-spike relationships are highly specific to the underlying connectivity of a local network. If so, the LFP-spike relationship may vary even within one brain area under the same task condition if neurons have heterogeneous connectivity with the active input sources during the task. Here, we tested this hypothesis in the parietal reach region (PRR), which ...[more]

PMID: 21307325

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Project description:Local Field Potentials (LFPs) integrate multiple neuronal events like synaptic inputs and intracellular potentials. LFP spatiotemporal features are particularly relevant in view of their applications both in research (e.g. for understanding brain rhythms, inter-areal neural communication and neuronal coding) and in the clinics (e.g. for improving invasive Brain-Machine Interface devices). However the relation between LFPs and spikes is complex and not fully understood. As spikes represent the fundamental currency of neuronal communication this gap in knowledge strongly limits our comprehension of neuronal phenomena underlying LFPs. We investigated the LFP-spike relation during tactile stimulation in primary somatosensory (S-I) cortex in the rat. First we quantified how reliably LFPs and spikes code for a stimulus occurrence. Then we used the information obtained from our analyses to design a predictive model for spike occurrence based on LFP inputs. The model was endowed with a flexible meta-structure whose exact form, both in parameters and structure, was estimated by using a multi-objective optimization strategy. Our method provided a set of nonlinear simple equations that maximized the match between models and true neurons in terms of spike timings and Peri Stimulus Time Histograms. We found that both LFPs and spikes can code for stimulus occurrence with millisecond precision, showing, however, high variability. Spike patterns were predicted significantly above chance for 75% of the neurons analysed. Crucially, the level of prediction accuracy depended on the reliability in coding for the stimulus occurrence. The best predictions were obtained when both spikes and LFPs were highly responsive to the stimuli. Spike reliability is known to depend on neuron intrinsic properties (i.e. on channel noise) and on spontaneous local network fluctuations. Our results suggest that the latter, measured through the LFP response variability, play a dominant role.

Dataset Information

Effects of visual stimulation on LFPs, spikes, and LFP-spike relations in PRR.

Publications

Effects of visual stimulation on LFPs, spikes, and LFP-spike relations in PRR.

Similar Datasets

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