Receptive field

About: Receptive field is a research topic. Over the lifetime, 8537 publications have been published within this topic receiving 596428 citations.

Bilateral hand representation in the postcentral somatosensory cortex

Abstract: In accordance with its important role in prehensile activity, a large cortical area is devoted to representation of the digits. Within this large cortical zone in the macaque somatosensory cortex, the complexity of neuronal receptive field characteristics increases from area 3b to areas 1 and 2 (refs 1-7). This increase in complexity continues into the upper bank of the intraparietal sulcus, where the somatosensory cortex adjoins the parietal association cortex. In this bank, callosal connections are much denser than in the more anterior part of this cortical zone. We have now discovered a substantial number of neurons with receptive fields on the bilateral hands. It was previously thought that neuronal receptive fields were restricted to the contralateral side in this cortical zone. Neurons with bilateral receptive fields were not found after lesioning the postcentral gyrus in the contralateral hemisphere. The majority of these neurons had receptive fields of the most complex types, representing multiple digits, indicating that the interhemispheric transfer of information occurs at higher levels of the hierarchical processing in each hemisphere.

Somatotopographic organization in the second somatosensory area of M. fascicularis.

Abstract: The second somatosensory area (SII) of awake, untrained cynomolgus monkeys was surveyed with recordings from nearly 1,000 single neurons. A detailed somatotopographic organization could be demonstrated in SII because the majority of these neurons had contralateral, moderate to well-defined receptive fields of < 10 cm2, and because neighboring neurons possessed receptive fields that were only slightly displaced from one another. Different body regions were represented in successive anterior to posterior strips that were oriented across the parietal operculum with an anterolateral to posteromedial slant. Neurons with trigeminal receptive fields were found in the anterior portion of SII; these neurons were the only ones in SII with predominantly bilateral receptive fields (r.f.'s.). Neurons with digit or hand r.f'.s form the largest component of the map, and were located posterior to those with face r.f.s. Most of these neurons had only contralateral activation. The hand and digit region was followed in turn by the arm, the upper and lower trunk, and the hindlimb regions. Although the overall SII orientation was along an anterior-posterior gradient, recordings at individual coronal planes often demonstrated isolated sequences of receptive fields that exhibited a medial-lateral progression. The principle example of this latter gradient was seen in the forelimb region where digits one through five were represented in an overlapping sequence across the parietal operculum. Except for portions of the digit representation, neighboring sequences of neurons in SII do not form a precise topologic map of the body that is comparable to the somatotopic maps observed in areas 3b and 1. The present findings contrast with previous physiological studies of SII in the primate. These discrepancies are discussed in relation to methodological differences and in terms of distinctions used to define the boundaries of SII.

The inner plexiform layer of the vertebrate retina: A quantitative and comparative electron microscopic analysis

Abstract: The inner plexiform layer of human, monkey, cat, rat, rabbit, ground squirrel, frog and pigeon retinas was studied by electron microscopy. All showed the same qualitative synaptic arrangements: bipolar cells made dyad ribbon synapses onto amacrine and ganglion cells; amacrine cells made conventional synaptic contacts onto bipolar, ganglion cells; amacrine cells montage of electron micrographs through the full thickness of the inner plexiform layer were made for each species and were scored for synaptic contacts. Both absolute and relative quantitative differences were found between species. The ratio of amacrine cell (conventional) synapses to bipolar cell (ribbon) synapses, the absolute number of amacrine cell synapses and the number of inter-amacrine cell synapses were all found to be higher in those animals which are known to have relatively complex retinal ganglion cell receptive field properties. It is suggested that the amacrine cell is involved in mediating complex visual transformations in certain vertebrate retinas.

Linearity of Cortical Receptive Fields Measured with Natural Sounds

Abstract: How do cortical neurons represent the acoustic environment? This question is often addressed by probing with simple stimuli such as clicks or tone pips. Such stimuli have the advantage of yielding easily interpreted answers, but have the disadvantage that they may fail to uncover complex or higher-order neuronal response properties. Here, we adopt an alternative approach, probing neuronal responses with complex acoustic stimuli, including animal vocalizations. We used in vivo whole-cell methods in the rat auditory cortex to record subthreshold membrane potential fluctuations elicited by these stimuli. Most neurons responded robustly and reliably to the complex stimuli in our ensemble. Using regularization techniques, we estimated the linear component, the spectrotemporal receptive field (STRF), of the transformation from the sound (as represented by its time-varying spectrogram) to the membrane potential of the neuron. We find that the STRF has a rich dynamical structure, including excitatory regions positioned in general accord with the prediction of the classical tuning curve. However, whereas the STRF successfully predicts the responses to some of the natural stimuli, it surprisingly fails completely to predict the responses to others; on average, only 11% of the response power could be predicted by the STRF. Therefore, most of the response of the neuron cannot be predicted by the linear component, although the response is deterministically related to the stimulus. Analysis of the systematic errors of the STRF model shows that this failure cannot be attributed to simple nonlinearities such as adaptation to mean intensity, rectification, or saturation. Rather, the highly nonlinear response properties of auditory cortical neurons must be attributable to nonlinear interactions between sound frequencies and time-varying properties of the neural encoder.

Why Neurons Have Thousands of Synapses, a Theory of Sequence Memory in Neocortex.

Abstract: Pyramidal neurons represent the majority of excitatory neurons in the neocortex. Each pyramidal neuron receives input from thousands of excitatory synapses that are segregated onto dendritic branches. The dendrites themselves are segregated into apical, basal, and proximal integration zones, which have different properties. It is a mystery how pyramidal neurons integrate the input from thousands of synapses, what role the different dendrites play in this integration, and what kind of network behavior this enables in cortical tissue. It has been previously proposed that non-linear properties of dendrites enable cortical neurons to recognize multiple independent patterns. In this paper we extend this idea in multiple ways. First we show that a neuron with several thousand synapses segregated on active dendrites can recognize hundreds of independent patterns of cellular activity even in the presence of large amounts of noise and pattern variation. We then propose a neuron model where patterns detected on proximal dendrites lead to action potentials, defining the classic receptive field of the neuron, and patterns detected on basal and apical dendrites act as predictions by slightly depolarizing the neuron without generating an action potential. By this mechanism, a neuron can predict its activation in hundreds of independent contexts. We then present a network model based on neurons with these properties that learns time-based sequences. The network relies on fast local inhibition to preferentially activate neurons that are slightly depolarized. Through simulation we show that the network scales well and operates robustly over a wide range of parameters as long as the network uses a sparse distributed code of cellular activations. We contrast the properties of the new network model with several other neural network models to illustrate the relative capabilities of each. We conclude that pyramidal neurons with thousands of synapses, active dendrites, and multiple integration zones create a robust and powerful sequence memory. Given the prevalence and similarity of excitatory neurons throughout the neocortex and the importance of sequence memory in inference and behavior, we propose that this form of sequence memory may be a universal property of neocortical tissue.