How Do We Recognize a Face?

How individual faces are encoded by neurons in high-level visual areas has been a subject of active debate. An influential model is that neurons encode specific faces. However, Chang and Tsao conclusively show that, instead, these neurons encode features along specific axes, which explains why they were previously found to respond to apparently different faces

Neuronal codes for visual perception and memory.

In this review, I describe and contrast the representation of stimuli in visual cortical areas and in the medial temporal lobe (MTL). While cortex is characterized by a distributed and implicit coding that is optimal for recognition and storage of semantic information, the MTL shows a much sparser and explicit coding of specific concepts that is ideal for episodic memory. I will describe the main characteristics of the coding in the MTL by the so-called concept cells and will then propose a model of the formation and recall of episodic memory based on partially overlapping assemblies

Past, present and future of spike sorting techniques

Spike sorting is a crucial step to extract information from extracellular recordings. With new recording opportunities provided by the development of new electrodes that allow monitoring hundreds of neurons simultaneously, the scenario for the new generation of algorithms is both exciting and challenging. However, this will require a new approach to the problem and the development of a common reference framework to quickly assess the performance of new algorithms. In this work, we review the basic concepts of spike sorting, including the requirements for different applications, together with the problems faced by presently available algorithms. We conclude by proposing a roadmap stressing the crucial points to be addressed to support the neuroscientific research of the near future

Perceptual and contextual awareness: methodological considerations in the search for the neural correlates of consciousness.

In the last decades, the neural correlates of consciousness (NCCs) have been explored using both invasive and non-invasive recordings by comparing the brain activity elicited by seen versus unseen visual stimuli (i.e., the contrastive analysis). Here, we review a selection of these studies and discuss a set of considerations to improve the search for the NCCs using the contrastive analysis. In particular, we first argue in favor of implementing paradigms where different perceptual outputs are obtained using identical visual inputs. Second, we propose that the large disagreement in the field -in terms of the dissimilar neural patterns proposed as NCCs- is partially explained by the fact that different studies report the neural correlates of different conscious processes in the brain. More specifically, we distinguish between the perceptual awareness of a visual stimulus, associated to a boost in object-selective neural assemblies, and a more elaborate process (contextual awareness) that we argue is reflected in the firing of concept neurons in the medial temporal lobe, triggering a rich representation of the context, associations, and memories linked to the specific stimulus

Timing of single-neuron and local field potential responses in the human medial temporal lobe

The relationship between the firing of single cells and local field potentials (LFPs) has received increasing attention, with studies in animals and humans. Recordings in the human medial temporal lobe (MTL) have demonstrated the existence of neurons with selective and invariant responses, with a relatively late but precise response onset around 300 ms after stimulus presentation and firing only upon conscious recognition of the stimulus. This represents a much later onset than expected from direct projections from inferotemporal cortex. The neural mechanisms underlying this onset remain unclear. To address this issue, we performed a joint analysis of single-cell and LFP responses during a visual recognition task. Single-neuron responses were preceded by a global LFP deflection in the theta range. In addition, there was a local and stimulus-specific increase in the single-trial gamma power. These LFP responses correlated with conscious recognition. The timing of the neurons’ firing was phase locked to these LFP responses. We propose that whereas the gamma phase locking reflects the activation of local networks encoding particular recognized stimuli, the theta phase locking reflects a global activation that provides a temporal window for processing consciously perceived stimuli in the MTL

Rapid Encoding of New Memories by Individual Neurons in the Human Brain

The creation of memories about real-life episodes requires rapid neuronal changes that may appear after a single occurrence of an event. How is such demand met by neurons in the medial temporal lobe (MTL), which plays a fundamental role in episodic memory formation? We recorded the activity of MTL neurons in neurosurgical patients while they learned new associations. Pairs of unrelated pictures, one of a person and another of a place, were used to construct a meaningful association modeling the episodic memory of meeting a person in a particular place. We found that a large proportion of responsive MTL neurons expanded their selectivity to encode these specific associations within a few trials: cells initially responsive to one picture started firing to the associated one but not to others. Our results provide a plausible neural substrate for the inception of associations, which are crucial for the formation of episodic memories

Single trial analysis of field potentials in perception, learning and memory.

The analysis of single trial responses of field potentials is an important tool to study brain signals. Single trial analyses can indeed provide additional information that is obscured or simply not available in the average responses. The importance of studying single trial responses is reinforced by the fact that different brain processes are correlated with trial-by-trial variation of the responses. Here, we review key studies implementing single trial analyses of field potentials-using methods such as single trial latency, amplitude and power changes, spike and LFP relationships, correlations between areas, cross frequency coupling, decoding of the presented stimuli-that bring light into the neural basis of perception, learning and memory

Dissociation between the neural correlates of conscious face perception and visual attention.

Given the higher chance to recognize attended compared to unattended stimuli, the specific neural correlates of these two processes, attention and awareness, tend to be intermingled in experimental designs. In this study, we dissociated the neural correlates of conscious face perception from the effects of visual attention. To do this, we presented faces at the threshold of awareness and manipulated attention through the use of exogenous prestimulus cues. We show that the N170 component, a scalp EEG marker of face perception, was modulated independently by attention and by awareness. An earlier P1 component was not modulated by either of the two effects and a later P3 component was indicative of awareness but not of attention. These claims are supported by converging evidence from (a) modulations observed in the average evoked potentials, (b) correlations between neural and behavioral data at the single-subject level, and (c) single-trial analyses. Overall, our results show a clear dissociation between the neural substrates of attention and awareness. Based on these results, we argue that conscious face perception is triggered by a boost in face-selective cortical ensembles that can be modulated by, but are still independent from, visual attention

A novel and fully automatic spike-sorting implementation with variable number of features

The most widely used spike-sorting algorithms are semiautomatic in practice, requiring manual tuning of the automatic solution to achieve good performance. In this work, we propose a new fully automatic spike-sorting algorithm that can capture multiple clusters of different sizes and densities. In addition, we introduce an improved feature selection method, by using a variable number of wavelet coefficients, based on the degree of non-Gaussianity of their distributions. We evaluated the performance of the proposed algorithm with real and simulated data. With real data from single-channel recordings, in ~95% of the cases the new algorithm replicated, in an unsupervised way, the solutions obtained by expert sorters, who manually optimized the solution of a previous semiautomatic algorithm. This was done while maintaining a low number of false positives. With simulated data from single-channel and tetrode recordings, the new algorithm was able to correctly detect many more neurons compared with previous implementations and also compared with recently introduced algorithms, while significantly reducing the number of false positives. In addition, the proposed algorithm showed good performance when tested with real tetrode recordings

Bayes optimal template matching for spike sorting - combining fisher discriminant analysis with optimal filtering

Spike sorting, i.e., the separation of the firing activity of different neurons from extracellular measurements, is a crucial but often error-prone step in the analysis of neuronal responses. Usually, three different problems have to be solved: the detection of spikes in the extracellular recordings, the estimation of the number of neurons and their prototypical (template) spike waveforms, and the assignment of individual spikes to those putative neurons. If the template spike waveforms are known, template matching can be used to solve the detection and classification problem. Here, we show that for the colored Gaussian noise case the optimal template matching is given by a form of linear filtering, which can be derived via linear discriminant analysis. This provides a Bayesian interpretation for the well-known matched filter output. Moreover, with this approach it is possible to compute a spike detection threshold analytically. The method can be implemented by a linear filter bank derived from the templates, and can be used for online spike sorting of multielectrode recordings. It may also be applicable to detection and classification problems of transient signals in general. Its application significantly decreases the error rate on two publicly available spike-sorting benchmark data sets in comparison to state-of-the-art template matching procedures. Finally, we explore the possibility to resolve overlapping spikes using the template matching outputs and show that they can be resolved with high accuracy