Representation Of Stimuli Research Articles

Decomposing Neural Circuit Function into Information Processing Primitives.

It is challenging to measure how specific aspects of coordinated neural dynamics translate into operations of information processing and, ultimately, cognitive functions. An obstacle is that simple circuit mechanisms-such as self-sustained or propagating activity and nonlinear summation of inputs-do not directly give rise to high-level functions. Nevertheless, they already implement simple the information carried by neural activity. Here, we propose that distinct functions, such as stimulus representation, working memory, or selective attention, stem from different combinations and types of low-level manipulations of information or information processing primitives. To test this hypothesis, we combine approaches from information theory with simulations of multi-scale neural circuits involving interacting brain regions that emulate well-defined cognitive functions. Specifically, we track the information dynamics emergent from patterns of neural dynamics, using quantitative metrics to detect where and when information is actively buffered, transferred or nonlinearly merged, as possible modes of low-level processing (storage, transfer and modification). We find that neuronal subsets maintaining representations in working memory or performing attentional gain modulation are signaled by their boosted involvement in operations of information storage or modification, respectively. Thus, information dynamic metrics, beyond detecting which network units participate in cognitive processing, also promise to specify how and when they do it, that is, through which type of primitive computation, a capability that may be exploited for the analysis of experimental recordings.

Stochastic resonance in sparse neuronal network: functional role of ongoing activity to detect weak sensory input in awake auditory cortex of rat.

The awake cortex is characterized by a higher level of ongoing spontaneous activity, but it has a better detectability of weak sensory inputs than the anesthetized cortex. However, the computational mechanism underlying this paradoxical nature of awake neuronal activity remains to be elucidated. Here, we propose a hypothetical stochastic resonance, which improves the signal-to-noise ratio (SNR) of weak sensory inputs through nonlinear relations between ongoing spontaneous activities and sensory-evoked activities. Prestimulus and tone-evoked activities were investigated via in vivo extracellular recording with a dense microelectrode array covering the entire auditory cortex in rats in both awake and anesthetized states. We found that tone-evoked activities increased supralinearly with the prestimulus activity level in the awake state and that the SNR of weak stimulus representation was optimized at an intermediate level of prestimulus ongoing activity. Furthermore, the temporally intermittent firing pattern, but not the trial-by-trial reliability or the fluctuation of local field potential, was identified as a relevant factor for SNR improvement. Since ongoing activity differs among neurons, hypothetical stochastic resonance or "sparse network stochastic resonance" might offer beneficial SNR improvement at the single-neuron level, which is compatible with the sparse representation in the sensory cortex.

Feature-Specific Salience Maps in Human Cortex.

Priority map theory is a leading framework for understanding how various aspects of stimulus displays and task demands guide visual attention. Per this theory, the visual system computes a priority map, which is a representation of visual space indexing the relative importance, or priority, of locations in the environment. Priority is computed based on both salience, defined based on image-computable properties; and relevance, defined by an individual's current goals, and is used to direct attention to the highest-priority locations for further processing. Computational theories suggest that priority maps identify salient locations based on individual feature dimensions (e.g., color, motion), which are integrated into an aggregate priority map. While widely accepted, a core assumption of this framework, the existence of independent feature dimension maps in visual cortex, remains untested. Here, we tested the hypothesis that retinotopic regions selective for specific feature dimensions (color or motion) in human cortex act as neural feature dimension maps, indexing salient locations based on their preferred feature. We used fMRI activation patterns to reconstruct spatial maps while male and female human participants viewed stimuli with salient regions defined by relative color or motion direction. Activation in reconstructed spatial maps was localized to the salient stimulus position in the display. Moreover, the strength of the stimulus representation was strongest in the ROI selective for the salience-defining feature. Together, these results suggest that feature-selective extrastriate visual regions highlight salient locations based on local feature contrast within their preferred feature dimensions, supporting their role as neural feature dimension maps.SIGNIFICANCE STATEMENT Identifying salient information is important for navigating the world. For example, it is critical to detect a quickly approaching car when crossing the street. Leading models of computer vision and visual search rely on compartmentalized salience computations based on individual features; however, there has been no direct empirical demonstration identifying neural regions as responsible for performing these dissociable operations. Here, we provide evidence of a critical double dissociation that neural activation patterns from color-selective regions prioritize the location of color-defined salience while minimally representing motion-defined salience, whereas motion-selective regions show the complementary result. These findings reveal that specialized cortical regions act as neural "feature dimension maps" that are used to index salient locations based on specific features to guide attention.

Temporal association activates projections from the perirhinal cortex and ventral CA1 to the prelimbic cortex and from the prelimbic cortex to the basolateral amygdala.

In trace fear conditioning, the prelimbic cortex exhibits persistent activity during the interval between the conditioned and unconditioned stimuli, which maintains a conditioned stimulus representation. Regions cooperating for this function or encoding the conditioned stimulus before the interval could send inputs to the prelimbic cortex, supporting learning. The basolateral amygdala has conditioned stimulus- and unconditioned stimulus-responsive neurons, convergently activated. The prelimbic cortex could directly project to the basolateral amygdala to associate the transient memory of the conditioned stimulus with the unconditioned stimulus. We investigated the neuronal circuit supporting temporal associations using contextual fear conditioning with a 5-s interval, in which 5s separates the contextual conditioned stimulus from the unconditioned stimulus. Injecting retrobeads, we quantified c-Fos in prelimbic cortex- or basolateral amygdala-projecting neurons from 9 regions after contextual fear conditioning with a 5-s interval or contextual fear conditioning, in which the conditioned and unconditioned stimuli overlap. The contextual fear conditioning with a 5-s interval activated ventral CA1 and perirhinal cortex neurons projecting to the prelimbic cortex and prelimbic cortex neurons projecting to basolateral amygdala. Both fear conditioning activated ventral CA1 and lateral entorhinal cortex neurons projecting to basolateral amygdala and basolateral amygdala neurons projecting to prelimbic cortex. The perirhinal cortex→prelimbic cortex and ventral CA1→prelimbic cortex connections are the first identified prelimbic cortex afferent projections participating in temporal associations. These results help to understand time-linked memories, a process required in episodic and working memories.

Mental representations mediate aversive learning in humans.

Mental representations of stimuli that are not physically present are critical for a range of cognitive capacities, including perception, memory, and learning. Overly robust mental representations, however, can contribute to hallucinations in healthy individuals and those diagnosed with psychotic illness. Measuring the strength of mental representations can thus provide insight into how the contents of the mind influence both adaptive and maladaptive behaviors. In rodents, the robustness of mental representations has been tested using the representation-mediated learning (RML) task, in which animals respond less to a cue after a stimulus that has previously been associated with this cue has been paired with illness. This suggests that the mental representation of the cue enters into a negative association during aversive learning, even though the cue is not physically present. Here, we developed a human version of the RML task in which participants initially learned associations between two visual symbols and two different appetitive food odors. Preference for the food odors was then tested immediately before and after a session in which one symbol was paired with an aversive noise. We observed that mediated learning, in the form of selective decrease in preference for the odor previously paired with the noise-predicting symbol, was directly proportional to direct aversive learning for the symbols themselves. These findings suggest that a mental representation of the odor entered into a negative association with the sound and pave the way for future studies aimed at characterizing the neural circuits of mediated learning in the human brain. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Age-Specific Effects of Visual Feature Binding.

Temporary binding of visual features enables objects to be stored and maintained in the visual working memory as a singular structure, irrespective of its inherent complexity. Although working memory capacity is reduced in aging, previous behavioral studies suggest that binding is preserved. Using event-related brain potentials (ERPs), we tested whether stimulus encoding is different in younger (N = 26, mean age = 28.5) and older (N = 22; mean age = 67.4) participants in a change detection task. The processing costs of binding were defined by the difference between feature-alone (color or shape) and feature-binding (color-shape) conditions. The behavioral data revealed that discrimination ability was reduced in the feature-binding condition, and that this effect was more attenuated in older participants. A corresponding ERP effect was not found in early components related to visual feature detection and processing (posterior N1 and frontal P2). However, the late positive complex (LPC) was more often expressed in the feature-binding condition, and the increase in amplitude was more pronounced in older participants. The LPC can be related to attentional allocation processes which might support the maintenance of the more complex stimulus representation in the binding task. However, the selective neural overactivation in the encoding phase observed in older participants does not prevent swap errors in the subsequent retrieval phase.

Predictive learning by a burst-dependent learning rule

Humans and other animals are able to quickly generalize latent dynamics of spatiotemporal sequences, often from a minimal number of previous experiences. Additionally, internal representations of external stimuli must remain stable, even in the presence of sensory noise, in order to be useful for informing behavior. In contrast, typical machine learning approaches require many thousands of samples, and generalize poorly to unexperienced examples, or fail completely to predict at long timescales. Here, we propose a novel neural network module which incorporates hierarchy and recurrent feedback terms, constituting a simplified model of neocortical microcircuits. This microcircuit predicts spatiotemporal trajectories at the input layer using a temporal error minimization algorithm. We show that this module is able to predict with higher accuracy into the future compared to traditional models. Investigating this model we find that successive predictive models learn representations which are increasingly removed from the raw sensory space, namely as successive temporal derivatives of the positional information. Next, we introduce a spiking neural network model which implements the rate-model through the use of a recently proposed biological learning rule utilizing dual-compartment neurons. We show that this network performs well on the same tasks as the mean-field models, by developing intrinsic dynamics that follow the dynamics of the external stimulus, while coordinating transmission of higher-order dynamics. Taken as a whole, these findings suggest that hierarchical temporal abstraction of sequences, rather than feed-forward reconstruction, may be responsible for the ability of neural systems to quickly adapt to novel situations.

Invited Session IV: Studies of the visual cortex with sub-millimeter resolution: Toward an all-optical bi-directional interrogation of topographic population codes in primate cortex.

The representation of visual stimuli in primate V1 is widely distributed and topographic. This raises the possibility that in some visual tasks, downstream areas that decode V1 signals in order to mediate perception could combine V1 signals at a relevant topographic scale-e.g., at the scale of orientation columns. If this were the case, then the fundamental unit of information would be individual columns rather than single neurons, and to account for the subject's behavior in a perceptual task, it would be necessary and sufficient to consider the summed activity of the thousands of neurons within each column. In this presentation I will discuss our initial attempts to test this topographic-code hypothesis using our optical-genetic toolbox for "reading" and "writing" neural population codes at the spatial scales of topographic maps in V1 of behaving macaques.

Overlapping representations of food and social stimuli in mouse VTA dopamine neurons

Dopamine neurons of the ventral tegmental area (VTADA) respond to food and social stimuli and contribute to both forms of motivation. However, it is unclear whether the same or different VTADA neurons encode these different stimuli. To address this question, we performed two-photon calcium imaging in mice presented with food and conspecifics and found statistically significant overlap in the populations responsive to both stimuli. Both hunger and opposite-sex social experience further increased the proportion of neurons that respond to both stimuli, implying that increasing motivation for one stimulus increases overlap. In addition, single-nucleus RNA sequencing revealed significant co-expression of feeding- and social-hormone-related genes in individual VTADA neurons. Taken together, our functional and transcriptional data suggest overlapping VTADA populations underlie food and social motivation.

Do choice tasks and rating scales elicit the same judgments?

Discrete choice (DCE) and rating scale experiments (RSE) are commonly applied procedures for eliciting preference judgments in a plethora of applied settings such as consumer choices, health care, and transport economics. An almost universal assumption is that actual “ground truth” preferences do not depend on which elicitation procedure is used. It is usually not possible to test this assumption, because typical studies feature response options for which there is no objectively correct response. To make progress on testing this assumption, we conducted a perceptual discrimination experiment where response options varied on a single attribute – stimulus saturation level – with a known objectively correct response. We had the same participants complete both a choice task (CT) and rating scale (RS) version of the experiment, allowing a direct examination of the assumption of a common representation. Our CT featured many characteristics that define a DCE, however, in order to have a known objectively correct response, it also differed in a few important ways. To test the assumption of a common representation, we developed a cognitive model with a response mechanism for both CT and RS. This enabled us to compare a model version that featured one shared latent stimulus representation across CT and RS versus a version which featured separate representations. Our results support the assumption that a single internal state supports both CT and RS responses, and also suggest that the CT method might provide more sensitive measurement of internal states than the RS method.

Propagation of activity through the cortical hierarchy and perception are determined by neural variability

Brains are composed of anatomically and functionally distinct regions performing specialized tasks, but regions do not operate in isolation. Orchestration of complex behaviors requires communication between brain regions, but how neural dynamics are organized to facilitate reliable transmission is not well understood. Here we studied this process directly by generating neural activity that propagates between brain regions and drives behavior, assessing how neural populations in sensory cortex cooperate to transmit information. We achieved this by imaging two densely interconnected regions—the primary and secondary somatosensory cortex (S1 and S2)—in mice while performing two-photon photostimulation of S1 neurons and assigning behavioral salience to the photostimulation. We found that the probability of perception is determined not only by the strength of the photostimulation but also by the variability of S1 neural activity. Therefore, maximizing the signal-to-noise ratio of the stimulus representation in cortex relative to the noise or variability is critical to facilitate activity propagation and perception.

Enhancement of dynamic visual acuity using transcranial alternating current stimulation with gamma burst entrained on alpha wave troughs

BackgroundCross-frequency phase-amplitude coupling (PAC) of cortical oscillations is observed within and across cortical regions during higher-order cognitive processes. Particularly, the PAC of alpha and gamma waves in the occipital cortex is closely associated with visual perception. In theory, gamma oscillation is a neuronal representation of visual stimuli, which drives the duty cycle of visual perception together with alpha oscillation. Therefore, it is believed that the timing of entrainment in alpha-gamma PAC may play a critical role in the performance of visual perception. We hypothesized that transcranial alternating current stimulation (tACS) with gamma waves entrained at the troughs of alpha waves would enhance the dynamic visual acuity (DVA).MethodWe attempted to modulate the performance of DVA by using tACS. The waveforms of the tACS were tailored to target PAC over the occipital cortex. The waveforms contained gamma (80 Hz) waves oscillating at either the peaks or troughs of alpha (10 Hz) waves. Participants performed computerized DVA task before, immediately after, and 10 min after each stimulation sessions. EEG and EOG were recorded during the DVA task to assess inter-trial phase coherence (ITPC), the alpha-gamma PAC at occipital site and the eye movements.ResultstACS with gamma waves entrained at alpha troughs effectively enhanced DVA, while the tACS with gamma waves entrained at alpha peaks did not affect DVA performance. Importantly, analyses of EEG and EOG showed that the enhancement of DVA performance originated solely from the neuromodulatory effects, and was not related to the modulation of saccadic eye movements. Consequently, DVA, one of the higher-order cognitive abilities, was successfully modulated using tACS with a tailored waveform.ConclusionsOur experimental results demonstrated that DVA performances were enhanced when tACS with gamma bursts entrained on alpha wave troughs were applied over the occipital cortex. Our findings suggest that using tACS with tailored waveforms, modulation of complex neuronal features could effectively enhance higher-order cognitive abilities such as DVA, which has never been modulated with conventional noninvasive brain stimulation methods.

Single-neuron and population measures of neuronal activity in working memory tasks.

Information represented in working memory is reflected in the firing rate of neurons in the prefrontal cortex and brain areas connected to it. In recent years, there has been an increased realization that population measures capture more accurately neural correlates of cognitive functions. We examined how single neuron firing in the prefrontal and posterior parietal cortex of two male monkeys compared with population measures in spatial working memory tasks. Persistent activity was observed in the dorsolateral prefrontal and posterior parietal cortex and firing rate predicted working memory behavior, particularly in the prefrontal cortex. These findings had equivalents in population measures, including trajectories in state space that became less separated in error trials. We additionally observed rotations of stimulus representations in the neuronal state space for different task conditions, which were not obvious in firing rate measures. These results suggest that population measures provide a richer view of how neuronal activity is associated with behavior, largely confirming that persistent activity is the core phenomenon that maintains visual-spatial information in working memory.NEW & NOTEWORTHY Recordings from large numbers of neurons led to a reevaluation of neural correlates of cognitive functions, which traditionally were defined based on responses of single neurons or averages of firing rates. Analysis of neuronal recordings from the dorsolateral prefrontal and posterior parietal cortex revealed that properties of neuronal firing captured in classical studies of persistent activity can account for population representations, though some population characteristics did not have clear correlates in single neuron activity.

Redundant representations are required to disambiguate simultaneously presented complex stimuli.

A pedestrian crossing a street during rush hour often looks and listens for potential danger. When they hear several different horns, they localize the cars that are honking and decide whether or not they need to modify their motor plan. How does the pedestrian use this auditory information to pick out the corresponding cars in visual space? The integration of distributed representations like these is called the assignment problem, and it must be solved to integrate distinct representations across but also within sensory modalities. Here, we identify and analyze a solution to the assignment problem: the representation of one or more common stimulus features in pairs of relevant brain regions-for example, estimates of the spatial position of cars are represented in both the visual and auditory systems. We characterize how the reliability of this solution depends on different features of the stimulus set (e.g., the size of the set and the complexity of the stimuli) and the details of the split representations (e.g., the precision of each stimulus representation and the amount of overlapping information). Next, we implement this solution in a biologically plausible receptive field code and show how constraints on the number of neurons and spikes used by the code force the brain to navigate a tradeoff between local and catastrophic errors. We show that, when many spikes and neurons are available, representing stimuli from a single sensory modality can be done more reliably across multiple brain regions, despite the risk of assignment errors. Finally, we show that a feedforward neural network can learn the optimal solution to the assignment problem, even when it receives inputs in two distinct representational formats. We also discuss relevant results on assignment errors from the human working memory literature and show that several key predictions of our theory already have support.

How do Autistic and Neurotypical Children’s Interests Influence their Accuracy During Novel Word Learning?

Word learning depends on attention – children must focus on the right things at the right times. However, autistic children often display restricted interests, limiting their intake of stimuli during word learning. This study investigates how category interests influence word learning in autism and neurotypical development. Autistic and neurotypical children matched on receptive vocabulary used a touch-screen computer to learn novel words associated with animals (high-interest stimuli) and objects (neutral-interest stimuli) via fast mapping. Response accuracy and speed were examined at referent selection, 5-minute retention, and 24-hour retention. Both groups identified meanings of novel words associated with unfamiliar animals and objects via mutual exclusivity with comparable accuracy. After 5 minutes, autistic children retained animal names with greater accuracy than neurotypical children. Autistic children showed a greater increase in their accuracy between 5-minute and 24-hour retention and outperformed neurotypical children across conditions after a night’s sleep. Across groups, 24-hour retention was predicted by number of target word repetitions heard at referent selection, indicating a relationship between fast mapping input and retention. However, autistic children were slower to respond correctly, particularly in the animal condition. For autistic children, superior word learning associated with high-interest stimuli was relatively short-term, as sleep appeared to consolidate their memory representations for neutral-interest stimuli. Although these results demonstrate that fundamental word learning mechanisms are not atypical in autism, slower response times may signal a speed-accuracy trade-off that could have implications for naturalistic language acquisition. Our findings also indicate favourable environmental conditions to scaffold word learning.

Effect of focused and distributed attention on stimulus representations in neural priority maps

Effect of focused and distributed attention on stimulus representations in neural priority maps

Neural representations of visual stimuli in primary visual cortex change as a function of threat and safety learning

Neural representations of visual stimuli in primary visual cortex change as a function of threat and safety learning

The role of awareness in shaping responses in human visual cortex

The visual cortex contains information about stimuli even when they are not consciously perceived. However, it remains unknown whether the visual system integrates local features into global objects without awareness. Here, we tested this by measuring brain activity in human observers viewing fragmented shapes that were either visible or rendered invisible by fast counterphase flicker. We then projected measured neural responses to these stimuli back into visual space. Visible stimuli caused robust responses reflecting the positions of their component fragments. Their neural representations also strongly resembled one another regardless of local features. By contrast, representations of invisible stimuli differed from one another and, crucially, also from visible stimuli. Our results demonstrate that even the early visual cortex encodes unconscious visual information differently from conscious information, presumably by only encoding local features. This could explain previous conflicting behavioural findings on unconscious visual processing.

Enhanced representation of visual stimuli near a suppressed distractor

Enhanced representation of visual stimuli near a suppressed distractor

Faster Convergence in Deep-Predictive-Coding Networks to Learn Deeper Representations.

Deep-predictive-coding networks (DPCNs) are hierarchical, generative models. They rely on feed-forward and feedback connections to modulate latent feature representations of stimuli in a dynamic and context-sensitive manner. A crucial element of DPCNs is a forward-backward inference procedure to uncover sparse, invariant features. However, this inference is a major computational bottleneck. It severely limits the network depth due to learning stagnation. Here, we prove why this bottleneck occurs. We then propose a new forward-inference strategy based on accelerated proximal gradients. This strategy has faster theoretical convergence guarantees than the one used for DPCNs. It overcomes learning stagnation. We also demonstrate that it permits constructing deep and wide predictive-coding networks. Such convolutional networks implement receptive fields that capture well the entire classes of objects on which the networks are trained. This improves the feature representations compared with our lab's previous nonconvolutional and convolutional DPCNs. It yields unsupervised object recognition that surpass convolutional autoencoders and is on par with convolutional networks trained in a supervised manner.