Neural field model of receptive field restructuring in primary visual cortex.

Receptive fields (RF) in the visual cortex can change their size depending on the state of the individual. This reflects a changing visual resolution according to different demands on information processing during drowsiness. So far, however, the possible mechanisms that underlie these size changes have not been tested rigorously. Only qualitatively has it been suggested that state-dependent lateral geniculate nucleus (LGN) firing patterns (burst versus tonic firing) are mainly responsible for the observed cortical receptive field restructuring. Here, we employ a neural field approach to describe the changes of cortical RF properties analytically. Expressions to describe the spatiotemporal receptive fields are given for pure feedforward networks. The model predicts that visual latencies increase nonlinearly with the distance of the stimulus location from the RF center. RF restructuring effects are faithfully reproduced. Despite the changing RF sizes, the model demonstrates that the width of the spatial membrane potential profile (as measured by the variance sigma of a gaussian) remains constant in cortex. In contrast, it is shown for recurrent networks that both the RF width and the width of the membrane potential profile generically depend on time and can even increase if lateral cortical excitatory connections extend further than fibers from LGN to cortex. In order to differentiate between a feedforward and a recurrent mechanism causing the experimental RF changes, we fitted the data to the analytically derived point-spread functions. Results of the fits provide estimates for model parameters consistent with the literature data and support the hypothesis that the observed RF sharpening is indeed mainly driven by input from LGN, not by recurrent intracortical connections.

The control of low-level information flow in the visual system.

Visual information processing needs to be error free and efficient. Our visual system tries to achieve the first goal by accommodating a wide variety of visual algorithms for the extraction of the relevant features in the scene, while at the same time the second goal is addressed by controlling the amount of visual information flow in the network employing selective attention. Attentional or pre-attentional mechanisms are found throughout many visual areas and these processes may start as early as in the visual thalamus (lateral geniculate nucleus, LGN). In this review we pay particular attention to experimental and theoretical findings which indicate that even low-level structures, such as LGN and V1, can play a major role in the flow-control of visual information.

The dynamic spatio-temporal behavior of visual responses in thalamus and cortex.

Due to eye and object movements the visual world changes on a rather fast time scale and the neuronal network of the primary visual pathway has to immediately react to these changes. Accordingly the neuronal activity patterns in the visual thalamus and cortex show a pronounced dynamic behavior which reenters the circuitry such that the actual cell responses are also guided by the activation history of the network. Thus, spatial and temporal aspects of visual receptive fields change not only by means of the actual visual stimulation hut also as a consequence of the state of the network. In this short review we summarize the different aspects which can influence the temporal firing patterns of cells in the visual thalamus (lateral geniculate nucleus, LGN) mainly by demonstrating how their inter-spike interval distributions will change. We then show that these firing patterns are able to change the spatial shape of receptive fields in the visual cortex (see Fig. 12 for a summary diagram). Finally, by means of a biophysical model, we will argue that the observed changes could serve to adjust the temporal and spatial resolution within the primary visual pathway to the different demands for information processing in an attentive as compared to a non-attentive state.

State-dependent receptive-field restructuring in the visual cortex.

To extract important information from the environment on a useful timescale, the visual system must be able to adapt rapidly to constantly changing scenes. This requires dynamic control of visual resolution, possibly at the level of the responses of single neurons. Individual cells in the visual cortex respond to light stimuli on particular locations (receptive fields) on the retina, and the structure of these receptive fields can change in different contexts. Here we show experimentally that the shape of receptive fields in the primary visual cortex of anaesthetized cats undergoes significant modifications, which are correlated with the general state of the brain as assessed by electroencephalography: receptive fields are wider during synchronized states and smaller during non-synchronized states. We also show that cortical receptive fields shrink over time when stimulated with flashing light spots. Finally, by using a network model we account for the changing size of the cortical receptive fields by dynamically rescaling the levels of excitation and inhibition in the visual thalamus and cortex. The observed dynamic changes in the sizes of the cortical receptive field could be a reflection of a process that adapts the spatial resolution within the primary visual pathway to different states of excitability.

One-dimensional, nonlinear determinism characterizes heart rate pattern during paced respiration.

This study focuses on the dynamic pattern of heart rate variability in the frequency range of respiration, the so-called respiratory sinus arrhythmia. Forty experimental time series of heart rate data from four healthy adult volunteers undergoing a paced respiration protocol were used as an empirical basis. For pacing-cycle lengths >8 s, the heartbeat intervals are shown to obey a rule that can be expressed by a one-dimensional circle map (next-angle map). Circle maps are introduced as a new type of model for time series analyses to characterize the nonlinear dynamic pattern underlying the respiratory sinus arrhythmia during voluntary paced respiration. Although these maps are not chaotic, the dynamic pattern shows typical imprints of nonlinearity. By starting from a piecewise linear model, which describes the different circle maps obtained from the empirical time series for various pacing frequencies, time invariant measures can be introduced that characterize the dynamic pattern of heart rate variability during voluntary slow-paced respiration.