Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies

In motor-related brain regions, movement intention has been successfully decoded from in-vivo spike train by isolating a lower-dimension manifold that the high-dimensional spiking activity is constrained to. The mechanism enforcing this constraint remains unclear, although it has been hypothesized to be implemented by the connectivity of the sampled neurons. We test this idea and explore the interactions between local synaptic connectivity and its ability to encode information in a lower dimensional manifold through simulations of a detailed microcircuit model with realistic sources of noise. We confirm that even in isolation such a model can encode the identity of different stimuli in a lower-dimensional space. We then demonstrate that the reliability of the encoding depends on the connectivity between the sampled neurons by specifically sampling populations whose connectivity maximizes certain topological metrics. Finally, we developed an alternative method for determining stimulus identity from the activity of neurons by combining their spike trains with their recurrent connectivity. We found that this method performs better for sampled groups of neurons that perform worse under the classical approach, predicting the possibility of two separate encoding strategies in a single microcircuit.

Complementary population codes in the dorsal and ventral hippocampus during associative learning

Memories are multifaceted and layered, incorporating external stimuli and internal states, and at multiple levels of resolution. Although the hippocampus is essential for memory, it remains unclear if distinct aspects of experience are encoded within different hippocampal subnetworks during learning. By tracking the same dCA1 or vCA1 neurons across cue-outcome learning, we find detailed and externally based (stimulus identity) representations in dCA1, and broad and internally based (stimulus relevance) signals in vCA1 that emerge with learning. These dorsoventral differences were observed regardless of cue modality or outcome valence, and representations within each region were largely stable for days after learning. These results identify how the hippocampus encodes associative memories, and show that hippocampal ensembles not only link experiences, but also imbue relationships with meaning and highlight behaviorally relevant information. Together, these complementary dynamics across hippocampal subnetworks allow for rich, diverse representation of experiences.

Dissociating the neural correlates of subjective visibility from those of decision confidence

A key goal of consciousness science is identifying neural signatures of being aware vs. unaware of simple stimuli. This is often investigated in the context of near-threshold detection, with reports of stimulus awareness being linked to heightened activation in a frontoparietal network. However, due to the fact that reports of stimulus presence are also associated with higher confidence than reports of stimulus absence, these results could be explained by frontoparietal regions encoding stimulus visibility, decision confidence or both. Consistent with this view, previously we showed that prefrontal regions encode confidence in decisions about target presence (Mazor, Friston & Fleming, 2020). Here, we further ask if prefrontal cortex also encodes information about stimulus visibility over and above confidence. We first show that, whereas stimulus identity was best decoded from the visual cortex, stimulus visibility (presence vs. absence) was best decoded from prefrontal regions. To control for effects of confidence, we then selectively sampled trials prior to decoding to equalize the confidence distributions between absence and presence responses. This analysis revealed that posterior medial frontal cortex encoded stimulus visibility over and above decision confidence. We interpret our findings as providing support for a representation of stimulus visibility in specific higher-order cortical circuits, one that is dissociable from representations of both decision confidence and stimulus identity.

Behavioral and Neural Fusion of Expectation with Sensation

Humans perceive expected stimuli faster and more accurately. However, the mechanism behind the integration of expectations with sensory information during perception remains unclear. We investigated the hypothesis that such integration depends on “fusion”—the weighted averaging of different cues informative about stimulus identity. We first trained participants to map a range of tones onto faces spanning a male–female continuum via associative learning. These two features served as expectation and sensory cues to sex, respectively. We then tested specific predictions about the consequences of fusion by manipulating the congruence of these cues in psychophysical and fMRI experiments. Behavioral judgments and patterns of neural activity in auditory association regions revealed fusion of sensory and expectation cues, providing evidence for a precise computational account of how expectations influence perception.

Perceptually unidentifiable stimuli influence cortical processing and behavioral performance

Our daily behavior is dynamically influenced by conscious and unconscious processes. Although the neural bases of conscious experience have been extensively investigated over the past several decades, how unconscious information impacts neural circuitry and behavior remains unknown. Here, we recorded populations of neurons in macaque primary visual cortex (V1) to find that perceptually unidentifiable stimuli repeatedly presented in the absence of awareness are encoded by neural populations in a way that facilitates their future processing in the context of a behavioral task. Such exposure increases stimulus sensitivity and information encoded in cell populations, even though animals are unaware of stimulus identity. This phenomenon is consistent with a Hebbian mechanism underlying an increase in functional connectivity specifically for the neurons activated by subthreshold stimuli. This form of unsupervised adaptation may constitute a vestigial pre-attention system using the mere frequency of stimulus occurrence to change stimulus representations even when sensory inputs are perceptually invisible.

Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies

In motor-related brain regions, movement intention has been successfully decoded from in-vivo spike train by isolating a lower-dimension manifold that the high-dimensional spiking activity is constrained to. The mechanism enforcing this constraint remains unclear, although it has been hypothesized to be implemented by the connectivity of the sampled neurons. We test this idea and explore the interactions between local synaptic connectivity and its ability to encode information in a lower dimensional manifold through simulations of a detailed microcircuit model with realistic sources of noise. We confirm that even in isolation such a model can encode the identity of different stimuli in a lower-dimensional space. We then demonstrate that the reliability of the encoding depends on the connectivity between the sampled neurons by specifically sampling populations whose connectivity maximizes certain topological metrics. Finally, we developed an alternative method for determining stimulus identity from the activity of neurons by combining their spike trains with their recurrent connectivity. We found that this method performs better for sampled groups of neurons that perform worse under the classical approach, predicting the possibility of two separate encoding strategies in a single microcircuit.

Behavioral and neural fusion of expectation with sensation

Humans perceive expected stimuli faster and more accurately. However, the mechanism behind the integration of expectations with sensory information during perception remains unclear. We investigated the hypothesis that such integration depends on ‘fusion’ — the weighted averaging of different cues informative about stimulus identity. We first trained participants to map a range of tones onto faces spanning a male-female continuum via associative learning. These two features served as expectation and sensory cues to sex, respectively. We then tested specific predictions about the consequences of fusion by manipulating the congruence of these cues in psychophysical and fMRI experiments. Behavioral judgments and patterns of neural activity in auditory association regions revealed fusion of sensory and expectation cues, providing evidence for a precise computational account of how expectations influence perception.

Feature Overlap and Relevance Determine Sequential Modulations in the Simon Task

Subjects usually respond faster and more accurate in trials in which the response location corresponds to a task-irrelevant stimulus location compared to when not. Previous research has shown, that this so-called Simon effect is reduced after non-corresponding compared to after corresponding trials. As of now it is yet unclear what exact mechanisms drive such sequential modulations. One theory assumes a conflict adaptation mechanism that decreases the influence of incongruent information after non-corresponding trials via increased cognitive control. However, other authors claim that feature integration processes may be the underlying mechanism as the amount of feature overlap differs between different correspondence sequences. Unfortunately, this also means that in the standard Simon task the repetition of task features and correspondence sequences are not independent. In order to address this issue, we mapped four stimuli to two responses in the present EEG study. This way, we created a task, which allows for sequences in which the stimulus identity may change without alternating the required response. These sequences may either comprise a change of the stimulus position or not and the contribution of feature integration as well as conflict adaptation processes could thus be observed independently. Our results indicate that the repetition of task features affects performance to a stronger degree than the correspondence sequence and feature integration processes do. Yet, an impact of both could still be observed. The strongest effect of feature repetitions on task performance was observed for task-relevant features, especially for the imperative stimulus identity itself. The EEG results support these findings. The amplitudes of the fronto-central N2 and the parietal P3 decreased with increasing feature overlap from one trial to the next. The posterior lateralization, reflected by the posterior contralateral negativity (PCN), however, appears to reflect mainly changes in the stimulus location and stimulus–response (S–R) correspondence rather than feature repetitions per se.

Neural network features distinguish chemosensory stimuli in Caenorhabditis elegans

Nervous systems extract and process information from their environment to alter animal behavior and physiology. Despite progress in understanding how different stimuli are represented by changes in neuronal activity, less is known about how they affect broader neural network properties. We developed a framework to use graph-theoretic features of neural network activity and predict ecologically-relevant stimulus properties – namely, stimulus identity and valence. Specifically, we used the transparent nematode, Caenorhabditis elegans, with its small nervous system, to define neural network features associated with various chemosensory stimuli. We trapped animals using a microfluidic device and exposed their noses to chemical stimuli known to be attractive or repellent, while monitoring changes in neural activity in more than 40 neurons in their heads. We found that repellents trigger higher average neural activity across the network, and that the tastant salt increases neural variability. In contrast, graph-theoretic features, which capture patterns of interactions between neurons, are better suited to decode stimulus identity than measures of neural activity. Furthermore, we show that a simple machine learning classifier trained using graph-theoretic features alone or in combination with neural activity features can accurately predict stimulus identity. These results indicate that graph theory reveals network characteristics that are distinct from neural activity, confirming its utility in extracting stimulus properties from neural population data.Significance StatementChanges in the external environment (stimuli) alter patterns of neural activity in animal nervous systems. A central challenge in computational neuroscience is to identify how stimulus properties alter interactions between neurons. We recorded neural activity data from C. elegans head neurons while the animal experienced various chemosensory stimuli. We then used a combination of activity statistics (i.e., average, standard deviation, and several frequency-based measures) and graph-theoretic features of network structure (e.g., modularity – the extent to which a network can be divided into independent clusters) to define neural properties that can accurately predict stimulus identity. Our method is general and can be used across species.