Dataset Information

Using a compound gain field to compute a reach plan.

ABSTRACT: A gain field, the scaling of a tuned neuronal response by a postural signal, may help support neuronal computation. Here, we characterize eye and hand position gain fields in the parietal reach region (PRR). Eye and hand gain fields in individual PRR neurons are similar in magnitude but opposite in sign to one another. This systematic arrangement produces a compound gain field that is proportional to the distance between gaze location and initial hand position. As a result, the visual response to a target for an upcoming reach is scaled by the initial gaze-to-hand distance. Such a scaling is similar to what would be predicted in a neural network that mediates between eye- and hand-centered representations of target location. This systematic arrangement supports a role of PRR in visually guided reaching and provides strong evidence that gain fields are used for neural computations.

SUBMITTER: Chang SW

PROVIDER: S-EPMC2811884 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Using a compound gain field to compute a reach plan.

Chang Steve W C SW Papadimitriou Charalampos C Snyder Lawrence H LH

Neuron 20091201 5

A gain field, the scaling of a tuned neuronal response by a postural signal, may help support neuronal computation. Here, we characterize eye and hand position gain fields in the parietal reach region (PRR). Eye and hand gain fields in individual PRR neurons are similar in magnitude but opposite in sign to one another. This systematic arrangement produces a compound gain field that is proportional to the distance between gaze location and initial hand position. As a result, the visual response t ...[more]

PMID: 20005829

Similar Datasets

Project description:Despite its century-old use, the interpretation of local field potentials (LFPs), the low-frequency part of electrical signals recorded in the brain, is still debated. In cortex the LFP appears to mainly stem from transmembrane neuronal currents following synaptic input, and obvious questions regarding the 'locality' of the LFP are: What is the size of the signal-generating region, i.e., the spatial reach, around a recording contact? How far does the LFP signal extend outside a synaptically activated neuronal population? And how do the answers depend on the temporal frequency of the LFP signal? Experimental inquiries have given conflicting results, and we here pursue a modeling approach based on a well-established biophysical forward-modeling scheme incorporating detailed reconstructed neuronal morphologies in precise calculations of population LFPs including thousands of neurons. The two key factors determining the frequency dependence of LFP are the spatial decay of the single-neuron LFP contribution and the conversion of synaptic input correlations into correlations between single-neuron LFP contributions. Both factors are seen to give low-pass filtering of the LFP signal power. For uncorrelated input only the first factor is relevant, and here a modest reduction (<50%) in the spatial reach is observed for higher frequencies (>100 Hz) compared to the near-DC ([Formula: see text]) value of about [Formula: see text]. Much larger frequency-dependent effects are seen when populations of pyramidal neurons receive correlated and spatially asymmetric inputs: the low-frequency ([Formula: see text]) LFP power can here be an order of magnitude or more larger than at 60 Hz. Moreover, the low-frequency LFP components have larger spatial reach and extend further outside the active population than high-frequency components. Further, the spatial LFP profiles for such populations typically span the full vertical extent of the dendrites of neurons in the population. Our numerical findings are backed up by an intuitive simplified model for the generation of population LFP.

Dataset Information

Using a compound gain field to compute a reach plan.

Publications

Using a compound gain field to compute a reach plan.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure