Neural Signatures of Intransitive Preferences

It is often assumed that decisions are made by rank-ordering and thus comparing the available choice options based on their subjective values. Rank-ordering requires that the alternatives’ subjective values are mentally represented at least on an ordinal scale. Because one alternative cannot be at the same time better and worse than another alternative, choices should satisfy transitivity (if alternative A is preferred over B, and B is preferred over C, A should be preferred over C). Yet, individuals often demonstrate striking violations of transitivity (preferring C over A). We used functional magnetic resonance imaging to study the neural correlates of intransitive choices between gambles varying in magnitude and probability of financial gains. Behavioral intransitivities were common. They occurred because participants did not evaluate the gambles independently, but in comparison with the alternative gamble presented. Neural value signals in prefrontal and parietal cortex were not ordinal-scaled and transitive, but reflected fluctuations in the gambles’ local, pairing-dependent preference-ranks. Detailed behavioral analysis of gamble preferences showed that, depending on the difference in the offered gambles’ attributes, participants gave variable priority to magnitude or probability and thus shifted between preferring richer or safer gambles. The variable, context-dependent priority given to magnitude and probability was tracked by insula (magnitude) and posterior cingulate (probability). Their activation-balance may reflect the individual decision rules leading to intransitivities. Thus, the phenomenon of intransitivity is reflected in the organization of the neural systems involved in risky decision-making

Multimodal Representation Learning for Place Recognition Using Deep Hebbian Predictive Coding

Recognising familiar places is a competence required in many engineering applications that interact with the real world such as robot navigation. Combining information from different sensory sources promotes robustness and accuracy of place recognition. However, mismatch in data registration, dimensionality, and timing between modalities remain challenging problems in multisensory place recognition. Spurious data generated by sensor drop-out in multisensory environments is particularly problematic and often resolved through adhoc and brittle solutions. An effective approach to these problems is demonstrated by animals as they gracefully move through the world. Therefore, we take a neuro-ethological approach by adopting self-supervised representation learning based on a neuroscientific model of visual cortex known as predictive coding. We demonstrate how this parsimonious network algorithm which is trained using a local learning rule can be extended to combine visual and tactile sensory cues from a biomimetic robot as it naturally explores a visually aliased environment. The place recognition performance obtained using joint latent representations generated by the network is significantly better than contemporary representation learning techniques. Further, we see evidence of improved robustness at place recognition in face of unimodal sensor drop-out. The proposed multimodal deep predictive coding algorithm presented is also linearly extensible to accommodate more than two sensory modalities, thereby providing an intriguing example of the value of neuro-biologically plausible representation learning for multimodal navigation

Population-Level Neural Codes Are Robust to Single-Neuron Variability from a Multidimensional Coding Perspective

Sensory neurons are often tuned to particular stimulus features, but their responses to repeated presentation of the same stimulus can vary over subsequent trials. This presents a problem for understanding the functioning of the brain, because downstream neuronal populations ought to construct accurate stimulus representations, even upon singular exposure. To study how trial-by-trial fluctuations (i.e., noise) in activity influence cortical representations of sensory input, we performed chronic calcium imaging of GCaMP6-expressing populations in mouse V1. We observed that high-dimensional response correlations, i.e., dependencies in activation strength among multiple neurons, can be used to predict single-trial, single-neuron noise. These multidimensional correlations are structured such that variability in the response of single neurons is relatively harmless to population representations of visual stimuli. We propose that multidimensional coding may represent a canonical principle of cortical circuits, explaining why the apparent noisiness of neuronal responses is compatible with accurate neural representations of stimulus features

Neural coding of reward magnitude in the orbitofrontal cortex of the rat during a five-odor olfactory discrimination task

The orbitofrontal cortex (OBFc) has been suggested to code the motivational value of environmental stimuli and to use this information for the flexible guidance of goal-directed behavior. To examine whether information regarding reward prediction is quantitatively represented in the rat OBFc, neural activity was recorded during an olfactory discrimination “go”/“no-go” task in which five different odor stimuli were predictive for various amounts of reward or an aversive reinforcer. Neural correlates related to both actual and expected reward magnitude were observed. Responses related to reward expectation occurred during the execution of the behavioral response toward the reward site and within a waiting period prior to reinforcement delivery. About one-half of these neurons demonstrated differential firing toward the different reward sizes. These data provide new and strong evidence that reward expectancy, regardless of reward magnitude, is coded by neurons of the rat OBFc, and are indicative for representation of quantitative information concerning expected reward. Moreover, neural correlates of reward expectancy appear to be distributed across both motor and nonmotor phases of the task

Author Correction: Multisensory task demands temporally extend the causal requirement for visual cortex in perception

The original version of this Article incorrectly included the 6th author Cyriel M. A. Pennartz as a corresponding author. The correct version lists only 7th author Umberto Olcese as corresponding author. In addition, the original version of the Article omitted to include a statement to indicate that Cyriel M. A. Pennartz and Umberto Olcese jointly supervised the work. This has been corrected in both the PDF and HTML versions of the Article

Real-time position reconstruction with hippocampal place cells

Brain-computer interfaces (BCI) are using the electroencephalogram, the electrocorticogram and trains of action potentials as inputs to analyze brain activity for communication purposes and/or the control of external devices. Thus far it is not known whether a BCI system can be developed that utilizes the states of brain structures that are situated well below the cortical surface, such as the hippocampus. In order to address this question we used the activity of hippocampal place cells (PCs) to predict the position of an rodent in real-time. First, spike activity was recorded from the hippocampus during foraging and analyzed off-line to optimize the spike sorting and position reconstruction algorithm of rats. Then the spike activity was recorded and analyzed in real-time. The rat was running in a box of 80 cm × 80 cm and its locomotor movement was captured with a video tracking system. Data were acquired to calculate the rat's trajectories and to identify place fields. Then a Bayesian classifier was trained to predict the position of the rat given its neural activity. This information was used in subsequent trials to predict the rat's position in real-time. The real-time experiments were successfully performed and yielded an error between 12.2 and 17.4% using 5-6 neurons. It must be noted here that the encoding step was done with data recorded before the real- time experiment and comparable accuracies between off-line (mean error of 15.9% for three rats) and real-time experiments (mean error of 14.7%) were achieved. The experiment shows proof of principle that position reconstruction can be done in real-time, that PCs were stable and spike sorting was robust enough to generalize from the training run to the real-time reconstruction phase of the experiment. Real-time reconstruction may be used for a variety of purposes, including creating behavioral-neuronal feedback loops or for implementing neuroprosthetic control

The pairwise phase consistency: a bias-free measure of rhythmic neuronal synchronization

Contains fulltext : 83850.pdf (Publisher’s version ) (Closed access)Oscillatory activity is a widespread phenomenon in nervous systems and has been implicated in numerous functions. Signals that are generated by two separate neuronal sources often demonstrate a consistent phase-relationship in a particular frequency-band, i.e., they demonstrate rhythmic neuronal synchronization. This consistency is conventionally measured by the PLV (phase-locking value) or the spectral coherence measure. Both statistical measures suffer from significant bias, in that their sample estimates overestimate the population statistics for finite sample sizes. This is a significant problem in the neurosciences where statistical comparisons are often made between conditions with a different number of trials or between neurons with a different number of spikes. We introduce a new circular statistic, the PPC (pairwise phase consistency). We demonstrate that the sample estimate of the PPC is a bias-free and consistent estimator of its corresponding population parameter. We show, both analytically and by means of numerical simulations, that the population statistic of the PPC is equivalent to the population statistic of the squared PLV. The variance and mean squared error of the PPC and PLV are compared. Finally, we demonstrate the practical relevance of the method in actual neuronal data recorded from the orbitofrontal cortex of rats that engage in a two-odour discrimination task. We find a strong increase in rhythmic synchronization of spikes relative to the local field potential (as measured by the PPC) for a wide range of low frequencies (including the theta-band) during the anticipation of sucrose delivery in comparison to the anticipation of quinine delivery.11 p