Responses Of Single Neurons Research Articles

Taste-Odor Association Learning Alters the Dynamics of Intraoral Odor Responses in the Posterior Piriform Cortex of Awake Rats.

How an odor is perceived is to a large extent dependent on the context in which that odor is (or has been) experienced. For example, experiencing an odor in mixture with taste during consumption can instill taste qualities in the percept of that odor (e.g., vanilla, an odor, has a gustatory quality: sweet). How associative features of odors are encoded in the brain remains unknown, but previous work suggests an important role for ongoing interactions between piriform cortex and extraolfactory systems. Here, we tested the hypothesis that piriform cortex dynamically encodes taste associations of odors. Rats were trained to associate one of two odors with saccharin; the other odor remained neutral. Before and after training, we tested preferences for the saccharin-associated odor versus the neutral odor, and recorded spiking responses from ensembles of neurons in posterior piriform cortex (pPC) to intraoral delivery of small drops of the same odor solutions. The results show that animals successfully learned taste-odor associations. At the neural level, single pPC neuron responses to the saccharin-paired odor were selectively altered following conditioning. Altered response patterns appeared after 1 s following stimulus delivery, and successfully discriminated between the two odors. However, firing rate patterns in the late epoch appeared different from firing rates early in the early epoch (<1 s following stimulus delivery). That is, in different response epoch, neurons used different codes to represent the difference between the two odors. The same dynamic coding scheme was observed at the ensemble level.

Neural activity during monkey vehicular wayfinding

Navigation gets us from place to place, creating a path to arrive at a goal. We trained a monkey to steer a motorized cart in a large room, beginning at its trial-by-trial start location and ending at a trial-by-trial cued goal location. While the monkey steered its autonomously chosen path to its goal, we recorded neural activity simultaneously in both the hippocampus (HPC) and medial superior temporal (MST) cortex.Local field potentials (LFPs) in these sites show similar patterns of activity with the 15–30 Hz band highlighting specific room locations. In contrast, 30–100 Hz LFPs support a unified map of the behaviorally relevant start and goal locations. The single neuron responses (SNRs) do not substantially contribute to room or start-goal maps. Rather, the SNRs form a continuum from neurons that are most active when the monkey is moving on a path toward the goal, versus other neurons that are most active when the monkey deviates from paths toward the goal.Granger analyses suggest that HPC firing precedes MST firing during cueing at the trial start location, mainly mediated by off-path neurons. In contrast, MST precedes HPC firing during steering, mainly mediated by on-path neurons. Interactions between MST and HPC are mediated by the parallel activation of on-path and off-path neurons, selectively activated across stages of this wayfinding task.

Learning-Induced Odor Modulation of Neuronal Activity in Auditory Cortex.

Sensory cortices, even of primary regions, are not purely unisensory. Rather, cortical neurons in sensory cortex show various forms of multisensory interactions. While some multisensory interactions naturally co-occur, the combination of others will co-occur through experience. In real life, learning and experience will result in conjunction with seemingly disparate sensory information that ultimately becomes behaviorally relevant, impacting perception, cognition, and action. Here we describe a novel auditory discrimination task in mice, designed to manipulate the expectation of upcoming trials using olfactory cues. We show that, after learning, female mice display a transient period of several days during which they exploit odor-mediated expectations for making correct decisions. Using two-photon calcium imaging of single neurons in auditory cortex (ACx) during behavior, we found that the behavioral effects of odor-mediated expectations are accompanied by an odor-induced modulation of neuronal activity. Further, we find that these effects are manifested differentially, based on the response preference of individual cells. A significant portion of effects, but not all, are consistent with a predictive coding framework. Our data show that learning novel odor-sound associations evoke changes in ACx. We suggest that behaviorally relevant multisensory environments mediate contextual effects as early as ACx.SIGNIFICANCE STATEMENT Natural environments are composed of multisensory objects. It remains unclear whether and how animals learn the regularities of congruent multisensory associations and how these may impact behavior and neural activity. We tested how learned odor-sound associations affected single-neuron responses in auditory cortex. We introduce a novel auditory discrimination task for mice in which odors set different contexts of expectation to upcoming trials. We show that, although the task can be solved purely by sounds, odor-mediated expectation impacts performance. We further show that odors cause a modulation of neuronal activity in auditory cortex, which is correlated with behavior. These results suggest that learning prompts an interaction of odor and sound information as early as sensory cortex.

Correlation between neural responses and human perception in figure-ground segregation.

Segmentation of a natural scene into objects (figures) and background (ground) is one of crucial functions for object recognition and scene understanding. Recent studies have investigated neural mechanisms underlying figure-ground (FG) segregation and reported neural modulation to FG in the intermediate-level visual area, V4, of macaque monkeys (FG neurons). However, whether FG neurons contribute to the perception of FG segregation has not been clarified. To examine the contribution of FG neurons, we examined the correlations between perceptual consistency (PC), which quantified perceptual ambiguity in FG determination, and the reliability of neural signals in response to FG. First, we evaluated PCs for the images that were used in the previous neural recording in V4; specifically, we measured how consistently FG can be determined across trials and participants for each stimulus. The PCs were widely distributed, so that we identified the ambiguity in FG segregation for each stimulus. Next, we analyzed the correlation between the PCs and the reliability of neural modulation to FG. We found that the stimuli with higher PCs evoked more consistent and greater modulation in the responses of single neurons than those with lower PCs. Since perception is expected to show a greater correlation with responses of neural population compared to those of single neurons, we examined the correlation between the PCs and the consistency of the population responses in FG determination. Stimuli with higher PCs evoked higher population consistency than those with lower PCs. Finally, we analyzed the correlation between the PCs and neural latencies in FG modulation. We found that the stimuli with higher PCs showed shorter reaction times in FG perception and evoked shorter modulation latencies in FG neurons. These results indicate that the responses of FG neurons recorded from macaque monkeys show significant correlations with human FG perception, suggesting that V4 neurons with FG-dependent responses contribute to the perception of FG segregation.

Neonatal Deafening Selectively Degrades the Sensitivity to Interaural Time Differences of Electrical Stimuli in Low-Frequency Pathways in Rats.

We examined the effect of neonatal deafening on frequency-specific pathways for processing of interaural time differences (ITDs) in cochlear-implant stimuli. Animal studies have demonstrated differences in neural ITD sensitivity in the inferior colliculus (IC) depending on the intracochlear location of intracochlear stimulating electrodes. We used neonatally deafened (ND) rats of both sexes and recorded the responses of single neurons in the IC to electrical stimuli with ITDs delivered to the apical or basal cochlea and compared them with acutely deafened (AD) rats of both sexes with normal hearing (NH) during development. We found that neonatal deafness significantly impacted the ITD sensitivity and the ITD tuning patterns restricted to apically driven IC neurons. In ND rats, the ITD sensitivity of apically driven neurons is reduced to values similar to basally driven neurons. The prevalence of ITD-sensitive apical neurons with a peak-shaped ITD tuning curve, which may reflect predominant input from the medial superior olivary (MSO) complex, in ND rats was diminished compared with that in AD rats (67%, AD vs 40%, ND). Conversely, monotonic-type responses rarely occurred in AD rats (14%) but were approximately equally as prevalent as peak-type tuning curves in ND rats (42%). Nevertheless, in ND rats, the ITD at the maximum slope of the ITD tuning curve was still more concentrated within the physiological ITD range in apically driven than in basally driven neurons. These results indicate that the development of high ITD sensitivity processed by low-frequency pathways depends on normal auditory experience and associated biases in ITD tuning strategies.

Multiset neurons

The present work addresses the adoption of multiset similarity, more specifically the real-valued Jaccard and coincidence indices, as an element underlying artificial neurons. The overlap/interiority index, cosine similarity, and Euclidean distance are also considered. Special attention is given to the features adopted to characterize the patterns provided as input to neurons. After presenting the basic concepts related to real-valued multisets and the coincidence similarity index, including the generalization of the real-valued Jaccard and coincidence indices to higher orders, as well as a description of their special properties, we introduce multiset neurons with and without a non-linearity stage. Important issues related to the selectivity and sensitivity of the implemented comparisons, as well as the effect of localized perturbations of individual features are then addressed. We proceed to study the response of single linear multiset neurons respectively to the detection of gaussian two-dimensional stimulus in presence of displacement, magnification, intensity variation, noise and interference from additional patterns. It is shown that the real-valued Jaccard and coincidence approaches tend to be more robust and effective than the interiority index and the cross-correlation. The coincidence-based neurons are shown to have enhanced overall performance respectively to the considered type of data and perturbations. The potential of multiset neurons is further illustrated with respect to the challenging problem of color and gray-level image segmentation. The reported concepts, methods, and results have several implications not only for pattern recognition and machine learning, but also regarding several other related areas including signal processing, image analysis, and neuroscience.

Oscillatory discharges in the auditory midbrain of the big brown bat contribute to coding of echo delay.

Subsequent to his breakthrough discovery of delay-tuned neurons in the bat's auditory midbrain and cortex, Albert Feng proposed that neural computations for echo delay involve intrinsic oscillatory discharges generated in the inferior colliculus (IC). To explore further the presence of these neural oscillations, we recorded multiple unit activity with a novel annular low impedance electrode from the IC of anesthetized big brown bats and Seba's short-tailed fruit bats. In both species, responses to tones, noise bursts, and FM sweeps contain long latency components, extending up to 60ms post-stimulus onset, organized in periodic, oscillatory-like patterns at frequencies of 360-740Hz. Latencies of this oscillatory activity resemble the wide distributions of single neuron response latencies in the IC. In big brown bats, oscillations lasting up to 30ms after pulse onset emerge in response to single FM pulse-echo pairs, at particular pulse-echo delays. Oscillatory responses to pulses and evoked responses to echoes overlap extensively at short echo delays (5-7ms), creating interference-like patterns. At longer echo delays, responses are separately evident to both pulses and echoes, with less overlap. These results extend Feng's reports of IC oscillations, and point to different processing mechanisms underlying perception of short vs long echo delays.

Feature representation under crowding in macaque V1 and V4 neuronal populations

Visual perception depends strongly on spatial context. A profound example is visual crowding, whereby the presence of nearby stimuli impairs the discriminability of object features. Despite extensive work on perceptual crowding and the spatial integrative properties of visual cortical neurons, the link between these two aspects of visual processing remains unclear. To understand better the neural basis of crowding, we recorded activity simultaneously from neuronal populations in V1 and V4 of fixating macaque monkeys. We assessed the information available from the measured responses about the orientation of a visual target both for targets presented in isolation and amid distractors. Both single neuron and population responses had less information about target orientation when distractors were present. Information loss was moderate in V1 and more substantial in V4. Information loss could be traced to systematic divisive and additive changes in neuronal tuning. Additive and multiplicative changes in tuning were more severe in V4; in addition, tuning exhibited other, non-affine transformations that were greater in V4, further restricting the ability of a fixed sensory readout strategy to extract accurate feature information across displays. Our results provide a direct test of crowding effects at different stages of the visual hierarchy. They reveal how crowded visual environments alter the spiking activity of cortical populations by which sensory stimuli are encoded and connect these changes to established mechanisms of neuronal spatial integration.

New insights on single-neuron selectivity in the era of population-level approaches.

In the past, neuroscience was focused on individual neurons seen as the functional units of the nervous system, but this approach fell short over time to account for new experimental evidence, especially for what concerns associative and motor cortices. For this reason and thanks to great technological advances, a part of modern research has shifted the focus from the responses of single neurons to the activity of neural ensembles, now considered the real functional units of the system. However, on a microscale, individual neurons remain the computational components of these networks, thus the study of population dynamics cannot prescind from studying also individual neurons which represent their natural substrate. In this new framework, ideas such as the capability of single cells to encode a specific stimulus (neural selectivity) may become obsolete and need to be profoundly revised. One step in this direction was made by introducing the concept of “mixed selectivity,” the capacity of single cells to integrate multiple variables in a flexible way, allowing individual neurons to participate in different networks. In this review, we outline the most important features of mixed selectivity and we also present recent works demonstrating its presence in the associative areas of the posterior parietal cortex. Finally, in discussing these findings, we present some open questions that could be addressed by future studies.

A Simple Model of Nonspiking Neurons.

Due to the ubiquity of spiking neurons in neuronal processes, various simple spiking neuron models have been proposed as an alternative to conductance-based models (a.k.a. Hodgkin-Huxley-type models), known to be computationally expensive and difficult to treat mathematically. However, to the best of our knowledge, there is no equivalent in the literature of a simple and lightweight model for describing the voltage behavior of nonspiking neurons, which also are ubiquitous in a large variety of nervous tissues in both vertebrate and invertebrate species and play a central role in information processing. This letter proposes a simple model that reproduces the experimental qualitative behavior of known types of nonspiking neurons. The proposed model, which differs fundamentally from classic simple spiking models unable to characterize nonspiking dynamics due to their intrinsic structure, is derived from the bifurcation study of conductance-based models of nonspiking neurons. Since such neurons display a high sensitivity to noise, the model aims at capturing the experimental distribution of single-neuron responses rather than perfectly replicating a single given experimental voltage trace. We show that such a model can be used as a building block for realistic simulations of large nonspiking neuronal networks and is endowed with generalization capabilities, granted by design.

Encoding of Race Categories by Single Neurons in the Human Brain.

Previous research has suggested that race-specific features are automatically processed during face perception, often with out-group faces treated categorically. Functional imaging has illuminated the hemodynamic correlates of this process, with fewer studies examining single-neuron responses. In the present experiment, epilepsy patients undergoing microwire recordings in preparation for surgical treatment were shown realistic computer-generated human faces, which they classified according to the emotional expression shown. Racial categories of the stimulus faces varied independently of the emotion shown, being irrelevant to the patients' primary task. Nevertheless, we observed race-driven changes in neural firing rates in the amygdala, anterior cingulate cortex, and hippocampus. These responses were broadly distributed, with the firing rates of 28% of recorded neurons in the amygdala and 45% in the anterior cingulate cortex predicting one or more racial categories. Nearly equal proportions of neurons responded to White and Black faces (24% vs. 22% in the amygdala and 26% vs. 28% in the anterior cingulate cortex). A smaller fraction (12%) of race-responsive neurons in the hippocampus predicted only White faces. Our results imply a distributed representation of race in brain areas involved in affective judgments, decision making, and memory. They also support the hypothesis that race-specific cues are perceptually coded even when those cues are task-irrelevant.

Motor cortex activity across movement speeds is predicted by network-level strategies for generating muscle activity.

Learned movements can be skillfully performed at different paces. What neural strategies produce this flexibility? Can they be predicted and understood by network modeling? We trained monkeys to perform a cycling task at different speeds, and trained artificial recurrent networks to generate the empirical muscle-activity patterns. Network solutions reflected the principle that smooth well-behaved dynamics require low trajectory tangling. Network solutions had a consistent form, which yielded quantitative and qualitative predictions. To evaluate predictions, we analyzed motor cortex activity recorded during the same task. Responses supported the hypothesis that the dominant neural signals reflect not muscle activity, but network-level strategies for generating muscle activity. Single-neuron responses were better accounted for by network activity than by muscle activity. Similarly, neural population trajectories shared their organization not with muscle trajectories, but with network solutions. Thus, cortical activity could be understood based on the need to generate muscle activity via dynamics that allow smooth, robust control over movement speed.

Neural Substrates of Homing Pigeon Spatial Navigation: Results From Electrophysiology Studies

Over many centuries, the homing pigeon has been selectively bred for returning home from a distant location. As a result of this strong selective pressure, homing pigeons have developed an excellent spatial navigation system. This system passes through the hippocampal formation (HF), which shares many striking similarities to the mammalian hippocampus; there are a host of shared neuropeptides, interconnections, and its role in the storage and manipulation of spatial maps. There are some notable differences as well: there are unique connectivity patterns and spatial encoding strategies. This review summarizes the comparisons between the avian and mammalian hippocampal systems, and the responses of single neurons in several general categories: (1) location and place cells responding in specific areas, (2) path and goal cells responding between goal locations, (3) context-dependent cells that respond before or during a task, and (4) pattern, grid, and boundary cells that increase firing at stable intervals. Head-direction cells, responding to a specific compass direction, are found in mammals and other birds but not to date in pigeons. By studying an animal that evolved under significant adaptive pressure to quickly develop a complex and efficient spatial memory system, we may better understand the comparative neurology of neurospatial systems, and plot new and potentially fruitful avenues of comparative research in the future.

Measuring ongoing delay with reverse noise

Counts of spike coincidences provide a powerful means to compare responses to different stimuli or of different neurons, particularly regarding temporal factors. A drawback is that these methods do not provide an absolute measure of latency, i.e., the temporal interval between stimulus and response. It is desirable to obtain such a measurement within the same analysis framework of coincidence counting. Single neuron responses were obtained from several fiber tracts (nerve, trapezoid body, lateral lemniscus) in two species to a broadband noise and its polarity-inverted version. The spike trains in response to these stimuli are the “forward noise” responses. The same stimuli were also played time-reversed. The resulting spike trains are then again time-reversed: these are the “reverse noise” responses. The forward and reverse responses were then analyzed with the coincidence count methods we have introduced earlier. Latency measurements derived from correlograms between forward and reverse noise responses show maxima at twice the latency value measured with other methods, as expected. Correlograms were often asymmetric but, at low-characteristic frequencies, were well-predicted by crosscorrelation of the reverse-correlation function and its reverse. We conclude that reverse noise provides an easy and reliable means to estimate latency of auditory nerve and brainstem neurons.

Cortical taste processing evolves through benign taste exposures.

Experience impacts learning and perception. Familiarity with stimuli that later become the conditioned stimulus (CS) in a learning paradigm, for instance, reduces the strength of that learning-a fact well documented in studies of conditioned taste aversion (CTA; De la Casa & Lubow, 1995; Lubow, 1973; Lubow & Moore, 1959). Recently, we have demonstrated that even experience with "incidental" (i.e., non-CS) stimuli influences CTA learning: Long Evans rats pre-exposed to salty and/or sour tastes later learn unusually strong aversions to novel sucrose (Flores et al., 2016), and exhibit enhanced sucrose-responsiveness after learning in gustatory cortex (GC; Flores et al., 2018). These findings suggest that incidental taste exposure (TE) may change spiking responses that have been shown to underlie the processing of tastes in GC. Here, we test this hypothesis, evaluating whether GC neuron spiking responses change across 3 days of taste exposure. Our results demonstrate that the discriminability of GC ensemble taste responses increases with this familiarization. Analysis of single-neuron responses recorded across multiple sessions reveals that taste exposure not only enriches identity and palatability information in taste-evoked activity but also enhances the discriminability of even novel tastes. These findings demonstrate that "mere" familiarization with incidental episodes of tasting changes the neural spiking responses of taste processing and provides specific insight into how such TE may impact later learning. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Reduced neural activity but improved coding in rodent higher-order visual cortex during locomotion

Running profoundly alters stimulus-response properties in mouse primary visual cortex (V1), but its effect in higher-order visual cortex is under-explored. Here we systematically investigate how visual responses vary with locomotive state across six visual areas and three cortical layers using a massive dataset from the Allen Brain Institute. Although previous work has shown running speed to be positively correlated with neural activity in V1, here we show that the sign of correlations between speed and neural activity varies across extra-striate cortex, and is even negative in anterior extra-striate cortex. Nevertheless, across all visual cortices, neural responses can be decoded more accurately during running than during stationary periods. We show that this effect is not attributable to changes in population activity structure, and propose that it instead arises from an increase in reliability of single-neuron responses during locomotion.

Non-Gaussian noise and autapse-induced inverse stochastic resonance in bistable Izhikevich neural system under electromagnetic induction

Inverse stochastic resonance refers to the phenomenon that the average firing rate of a neuron is inhibited by noise, of which mechanism is widely used in a variety of biological cells and economic phenomena. In this paper, a bistable Izhikevich neural model and triple-neuron feed-forward loop Izhikevich neural network motifs under the effects of electromagnetic induction are constructed to investigate the phenomenon of inverse stochastic resonance induced by Non-Gaussian colored noise and electrical autapse. It is found that there exists a minimum value of the average firing rate curve caused by intensity of non-Gaussian colored noise, which is the phenomenon of inverse stochastic resonance. Obtained results also show that the inverse stochastic resonance induced by electrical autapse shows a decaying oscillation process with respect to synaptic delay time, and further research indicates that average firing rate has several minimums as a function of time delay of electrical autapse, which is called multiple inverse stochastic resonance. Furthermore, the inverse stochastic resonance in triple-neuron feed-forward loop Izhikevich neural network motifs are also examined, and it is confirmed that the responses of single Izhikevich neuron and neural network motifs to different parameters show consistency under same conditions, but also show some differences. Finally, the effects of electromagnetic induction on inverse stochastic resonance are checked both in single Izhikevich neural model and feed-forward loop network motifs. Electromagnetic induction feedback gain coefficient k1 should not be too large under certain conditions, otherwise it may cause FFL network motifs to loss the function of suppressing the discharge activity. No matter how the values of electromagnetic induction parameter k2 and magnetic flux leakage coefficient k3 change, they basically cannot affect the ISR in the feed-forward loop neural network motifs The conclusions of this paper may help researchers understanding how using unique mechanism of inverse stochastic resonance to find advantages and avoid disadvantages in biomedical field and many interdisciplinary research.

Distorted Tonotopy Severely Degrades Neural Representations of Connected Speech in Noise following Acoustic Trauma.

Listeners with sensorineural hearing loss (SNHL) struggle to understand speech, especially in noise, despite audibility compensation. These real-world suprathreshold deficits are hypothesized to arise from degraded frequency tuning and reduced temporal-coding precision; however, peripheral neurophysiological studies testing these hypotheses have been largely limited to in-quiet artificial vowels. Here, we measured single auditory-nerve-fiber responses to a connected speech sentence in noise from anesthetized male chinchillas with normal hearing (NH) or noise-induced hearing loss (NIHL). Our results demonstrated that temporal precision was not degraded following acoustic trauma, and furthermore that sharpness of cochlear frequency tuning was not the major factor affecting impaired peripheral coding of connected speech in noise. Rather, the loss of cochlear tonotopy, a hallmark of NH, contributed the most to both consonant-coding and vowel-coding degradations. Because distorted tonotopy varies in degree across etiologies (e.g., noise exposure, age), these results have important implications for understanding and treating individual differences in speech perception for people suffering from SNHL.SIGNIFICANCE STATEMENT Difficulty understanding speech in noise is the primary complaint in audiology clinics and can leave people with sensorineural hearing loss (SNHL) suffering from communication difficulties that affect their professional, social, and family lives, as well as their mental health. We measured single-neuron responses from a preclinical SNHL animal model to characterize salient neural-coding deficits for naturally spoken speech in noise. We found the major mechanism affecting neural coding was not a commonly assumed factor, but rather a disruption of tonotopicity, the systematic mapping of acoustic frequency to cochlear place that is a hallmark of normal hearing. Because the degree of distorted tonotopy varies across hearing-loss etiologies, these results have important implications for precision audiology approaches to diagnosis and treatment of SNHL.

Effect of Biphasic kHz Field Stimulation on CA1 Pyramidal Neurons in Slices

Electrical stimulation in the kilohertz-frequency range has been successfully used for treatment of various neurological disorders. Nevertheless, the mechanisms underlying this stimulation are poorly understood. To study the effect of kilohertz-frequency electric fields on neuronal membrane biophysics we developed a reliable experimental method to measure responses of single neurons to kilohertz field stimulation in brain slice preparations. In the submerged brain slice pyramidal neurons of the CA1 subfield were recorded in the whole-cell configuration before, during and after stimulation with a biphasic charge balanced electric field at 2kHz, 5kHz or 10 kHz. The slice was placed in a 1 mm gap between two parallel 2 mm long platinum-iridium 0.1 mm diameter wire electrodes. Whole-cell recordings lasted for an hour or longer. Typically, a few 5-10 min long sessions of kHz-field stimulation (kHz-FS) with various frequency/amplitude combinations were applied to assess the type of neuronal response and possible changes of its membrane characteristics. It was found that kHz-field stimulation at all frequencies elicited reproducible excitatory neuronal responses lasting throughout stimulation period, but not after cessation of kHz-FS. During kHz-FS the rheobase usually decreased. In addition, spontaneous firing might be initiated in some silent neurons or became more intense in previously spontaneously active neurons. Response thresholds were in the range of 0.5-2 mA and were higher at higher frequencies. Blockade of glutamatergic synaptic transmission did not alter the magnitude of responses. Inhibitory synaptic input was not changed by kilohertz field stimulation. We conclude that kHz-frequency current applied in brain tissue has an excitatory effect on pyramidal neurons during stimulation. This effect is more prominent and occurs at a lower stimulus intensity at a frequency of 2kHz as compared to 5kHz and 10kHz.

Neural Mechanisms of Innate Recognition of Facial Stimuli in Primates

Primates can recognize or respond to specific stimuli that are important for survival, such as faces, predators, prey animals, and foods, even if they have not experienced those stimuli previously (innate recognition). Throughout vertebrates, including primates, the extrageniculate visual system (subcortical visual pathway) comprising the retina, superior colliculus, pulvinar, and amygdala is thought to be genetically hard-wired and involved in innate recognition of these stimuli. To investigate neural mechanisms of innate recognition in primates, we analyzed single neuronal responses to facial images in the monkey pulvinar and superior colliculus. The results indicated that the pulvinar and superior collicular neurons responded preferentially to facial images in short latency and showed gamma oscillations during stimulus presentation. Furthermore, the population activity of these neurons discriminated head direction, sex, and identity of facial images. Based on these findings, we discussed neural mechanisms underlying the innate and automatic (unconscious) detection of facial stimuli in the extrageniculate visual system.