Chun-I Yeh

Temporal precision in the neural code and the timescales of natural vision.

The timing of action potentials relative to sensory stimuli can be precise down to milliseconds in the visual system, even though the relevant timescales of natural vision are much slower. The existence of such precision contributes to a fundamental debate over the basis of the neural code and, specifically, what timescales are important for neural computation. Using recordings in the lateral geniculate nucleus, here we demonstrate that the relevant timescale of neuronal spike trains depends on the frequency content of the visual stimulus, and that ‘relative’, not absolute, precision is maintained both during spatially uniform white-noise visual stimuli and naturalistic movies. Using information-theoretic techniques, we demonstrate a clear role of relative precision, and show that the experimentally observed temporal structure in the neuronal response is necessary to represent accurately the more slowly changing visual world. By establishing a functional role of precision, we link visual neuron function on slow timescales to temporal structure in the response at faster timescales, and uncover a straightforward purpose of fine-timescale features of neuronal spike trains.

Spatial Spread of the Local Field Potential and its Laminar Variation in Visual Cortex

We developed a new method to estimate the spatial extent of summation, the cortical spread, of the local field potential (LFP) throughout all layers of macaque primary visual cortex V1 by taking advantage of the V1 retinotopic map. We mapped multi-unit activity and LFP visual responses with sparse-noise at several cortical sites simultaneously. The cortical magnification factor near the recording sites was precisely estimated by track reconstruction. The new method combined experimental measurements together with a model of signal summation to obtain the cortical spread of the LFP. This new method could be extended to cortical areas that have topographic maps such as S1 or A1, and to cortical areas without functional columnar maps, such as rodent visual cortex. In macaque V1, the LFP was the sum of signals from a very local region, the radius of which was on average 250 μm. The LFPs cortical spread varied across cortical layers, reaching a minimum value of 120 μm in layer 4B. An important functional consequence of the small cortical spread of the LFP is that the visual field maps of LFP and MUA recorded at a single electrode site were very similar. The similar spatial scale of the visual responses, the restricted cortical spread, and their laminar variation led to new insights about the sources and possible applications of the LFP.

Laminar analysis of visually evoked activity in the primary visual cortex

Studying the laminar pattern of neural activity is crucial for understanding the processing of neural signals in the cerebral cortex. We measured neural population activity [multiunit spike activity (MUA) and local field potential, LFP] in Macaque primary visual cortex (V1) in response to drifting grating stimuli. Sustained visually driven MUA was at an approximately constant level across cortical depth in V1. However, sustained, visually driven, local field potential power, which was concentrated in the γ-band (20–60 Hz), was greatest at the cortical depth corresponding to cortico-cortical output layers 2, 3, and 4B. γ-band power also tends to be more sustained in the output layers. Overall, cortico-cortical output layers accounted for 67% of total γ-band activity in V1, whereas 56% of total spikes evoked by drifting gratings were from layers 2, 3, and 4B. The high-resolution layer specificity of γ-band power, the laminar distribution of MUA and γ-band activity, and their dynamics imply that neural activity in V1 is generated by laminar-specific mechanisms. In particular, visual responses of MUA and γ-band activity in cortico-cortical output layers 2, 3, and 4B seem to be strongly influenced by laminar-specific recurrent circuitry and/or feedback.

Adaptation to stimulus contrast and correlations during natural visual stimulation

In this study, we characterize the adaptation of neurons in the cat lateral geniculate nucleus to changes in stimulus contrast and correlations. By comparing responses to high- and low-contrast natural scene movie and white noise stimuli, we show that an increase in contrast or correlations results in receptive fields with faster temporal dynamics and stronger antagonistic surrounds, as well as decreases in gain and selectivity. We also observe contrast- and correlation-induced changes in the reliability and sparseness of neural responses. We find that reliability is determined primarily by processing in the receptive field (the effective contrast of the stimulus), while sparseness is determined by the interactions between several functional properties. These results reveal a number of adaptive phenomena and suggest that adaptation to stimulus contrast and correlations may play an important role in visual coding in a dynamic natural environment.

“Black” Responses Dominate Macaque Primary Visual Cortex V1

Achromatic visual information is transferred from the retina to the brain through two parallel channels: ON-center cells carry “white” information and OFF-center cells “black” information (Nelson et al., 1978; Schiller, 1982; Schiller et al., 1986). Responses of ON and OFF retinal and thalamic neurons are approximately equal in magnitude (Krüger and Fischer, 1975; Kremers et al., 1993), but psychophysical studies have shown that humans detect light decrements (black) better and faster than increments (white) (Blackwell, 1946; Short, 1966; Krauskopf, 1980; Whittle, 1986; Bowen et al., 1989; Chan and Tyler, 1992; Kontsevich and Tyler, 1999; Chubb and Nam, 2000; Dannemiller and Stephens, 2001). From recordings of single-cell activity in the macaque monkeys primary visual cortex (V1), we found that black-dominant neurons substantially outnumbered white-dominant neurons in the corticocortical output layers 2/3, but the numbers of black- and white-dominant neurons were nearly equal in the thalamocortical input layer 4c. These results strongly suggest that the black-over-white preference is generated or greatly amplified in V1. The predominance of OFF neurons in layers 2/3 of V1, which provide visual input to higher cortical areas, may explain why human subjects detect black more easily than white. Furthermore, our results agree with human EEG and fMRI findings that V1 responses to decrements are stronger than to increments, though the OFF/ON imbalance we found in layers 2/3 of macaque V1 is much larger than in the whole V1 population in the human V1 experiments (Zemon et al., 1988, 1995; Olman et al., 2008).

Dynamic encoding of natural luminance sequences by LGN bursts

In the lateral geniculate nucleus (LGN) of the thalamus, visual stimulation produces two distinct types of responses known as tonic and burst. Due to the dynamics of the T-type Ca 2+ channels involved in burst generation, the type of response evoked by a particular stimulus depends on the resting membrane potential, which is controlled by a network of modulatory connections from other brain areas. In this study, we use simulated responses to natural scene movies to describe how modulatory and stimulus-driven changes in LGN membrane potential interact to determine the luminance sequences that trigger burst responses. We find that at low resting potentials, when the T channels are de-inactivated and bursts are relatively frequent, an excitatory stimulus transient alone is sufficient to evoke a burst. However, to evoke a burst at high resting potentials, when the T channels are inactivated and bursts are relatively rare, prolonged inhibitory stimulation followed by an excitatory transient is required. We also observe evidence of these effects in vivo, where analysis of experimental recordings demonstrates that the luminance sequences that trigger bursts can vary dramatically with the overall burst percentage of the response. To characterize the functional consequences of the effects of resting potential on burst generation, we simulate LGN responses to different luminance sequences at a range of resting potentials with and without a mechanism for generating bursts. Using analysis based on signal detection theory, we show that bursts enhance detection of specific luminance sequences, ranging from the onset of excitatory sequences at low resting potentials to the offset of inhibitory sequences at high resting potentials. These results suggest a dynamic role for burst responses during visual processing that may change according to behavioral state.

Stochastic Generation of Gamma-Band Activity in Primary Visual Cortex of Awake and Anesthetized Monkeys

Oscillatory neural activity within the gamma band (25–90 Hz) is generally thought to be able to provide a timing signal for harmonizing neural computations across different brain regions. Using time–frequency analyses of the dynamics of gamma-band activity in the local field potentials recorded from monkey primary visual cortex, we found identical temporal characteristics of gamma activity in both awake and anesthetized brain states, including large variability of peak frequency, brief oscillatory epochs (<100 ms on average), and stochastic statistics of the incidence and duration of oscillatory events. These findings indicate that gamma-band activity is temporally unstructured and is inherently a stochastic signal generated by neural networks. This idea was corroborated further by our neural-network simulations. Our results suggest that gamma-band activity is too random to serve as a clock signal for synchronizing neuronal responses in awake as in anesthetized monkeys. Instead, gamma-band activity is more likely to be filtered neuronal network noise. Its mean frequency changes with global state and is reduced under anesthesia.

Generation of Black-Dominant Responses in V1 Cortex

Consistent with human perceptual data, we found many more black-dominant than white-dominant responses in layer 2/3 neurons of the macaque primary visual cortex (V1). Seeking the mechanism of this black dominance of layer 2/3 neurons, we measured the laminar pattern of population responses (multiunit activity and local field potential) and found that a small preference for black is observable in early responses in layer 4Cβ, the parvocellular-input layer, but not in the magnocellular-input layer 4Cα. Surprisingly, further analysis of the dynamics of black-white responses in layers 4Cβ and 2/3 suggested that black-dominant responses in layer 2/3 were not generated simply because of the weak black-dominant inputs from 4Cβ. Instead, our results indicated the neural circuitry in V1 is wired with a preference to strengthen black responses. We hypothesize that this selective wiring could be due to (1) feedforward connectivity from black-dominant neurons in layer 4C to cells in layer 2/3 or (2) recurrent interactions between black-dominant neurons in layer 2/3, or a combination of both.

Stimulus ensemble and cortical layer determine V1 spatial receptive fields

The concept of receptive field is a linear, feed-forward view of visual signal processing. Frequently used models of V1 neurons, like the dynamic linear filter static nonlinearity Poisson spike encoder model, predict that receptive fields measured with different stimulus ensembles should be similar. Here, we tested this concept by comparing spatiotemporal maps of V1 neurons derived from two very different, but commonly used, stimulus ensembles: sparse noise and Hartley subspace stimuli. We found maps from the two methods agreed for neurons in input layer 4C but were very different for neurons in superficial layers of V1. Many layer 2/3 cells have receptive fields with multiple elongated subregions when mapped with Hartley stimuli, but their spatial maps collapse to only a single, less-elongated subregion when mapped with sparse noise. Moreover, for upper layer V1 neurons, the preferred orientation for Hartley maps is much closer to the preferred orientation measured with drifting gratings than is the orientation preference of sparse-noise maps. These results challenge the concept of a stimulus-invariant receptive field and imply that intracortical interactions shape fundamental properties of layer 2/3 neurons.

Functional Consequences of Neuronal Divergence Within the Retinogeniculate Pathway

The neuronal connections from the retina to the dorsal lateral geniculate nucleus (dLGN) are characterized by a high specificity. Each retinal ganglion cell diverges to connect to a small group of geniculate cells and each geniculate cell receives input from a small number of retinal ganglion cells. Consistent with the high specificity of the connections, geniculate cells sharing input from the same retinal afferent are thought to have very similar receptive fields. However, the magnitude of the receptive-field mismatches, which has not been systematically measured across the different cell types in dLGN, seems to be in contradiction with the functional anatomy of the Y visual pathway: Y retinal afferents in the cat diverge into two geniculate layers (A and C) that have Y geniculate cells (Y(A) and Y(C)) with different receptive-field sizes, response latencies, nonlinearity of spatial summation, and contrast sensitivity. To better understand the functional consequences of retinogeniculate divergence, we recorded from pairs of geniculate cells that shared input from a common retinal afferent across layers and within the same layer in dLGN. We found that nearly all cell pairs that shared retinal input across layers had Y-type receptive fields of the same sign (i.e., both on-center) that overlapped by >70%, but frequently differed in size and response latency. The receptive-field mismatches were relatively small in value (receptive-field size ratio <5; difference in peak response <5 ms), but were robustly correlated with the strength of the synchronous firing generated by the shared retinal connections (R(2) = 0.75). On average, the percentage of geniculate spikes that could be attributed to shared retinal inputs was about 10% for all cell-pair combinations studied. These results are used to provide new estimates of retinogeniculate divergence for different cell classes.