Joaquin Navajas

An Analogue Front-End Model for Developing Neural Spike Sorting Systems

In spike sorting systems, front-end electronics is a crucial pre-processing step that not only has a direct impact on detection and sorting accuracy, but also on power and silicon area. In this work, a behavioural front-end model is proposed to assess the impact of the design parameters (including signal-to-noise ratio, filter type/order, bandwidth, converter resolution/rate) on subsequent spike processing. Initial validation of the model is provided by applying a test stimulus to a hardware platform and comparing the measured circuit response to the expected from the behavioural model. Our model is then used to demonstrate the effect of the Analogue Front-End (AFE) on subsequent spike processing by testing established spike detection and sorting methods on a selection of systems reported in the literature. It is revealed that although these designs have a wide variation in design parameters (and thus also circuit complexity), the ultimate impact on spike processing performance is relatively low (10-15%). This can be used to inform the design of future systems to have an efficient AFE whilst also maintaining good processing performance.

Minimum requirements for accurate and efficient real-time on-chip spike sorting.

BACKGROUND Extracellular recordings are performed by inserting electrodes in the brain, relaying the signals to external power-demanding devices, where spikes are detected and sorted in order to identify the firing activity of different putative neurons. A main caveat of these recordings is the necessity of wires passing through the scalp and skin in order to connect intracortical electrodes to external amplifiers. The aim of this paper is to evaluate the feasibility of an implantable platform (i.e., a chip) with the capability to wirelessly transmit the neural signals and perform real-time on-site spike sorting. NEW METHOD We computationally modelled a two-stage implementation for online, robust, and efficient spike sorting. In the first stage, spikes are detected on-chip and streamed to an external computer where mean templates are created and sent back to the chip. In the second stage, spikes are sorted in real-time through template matching. RESULTS We evaluated this procedure using realistic simulations of extracellular recordings and describe a set of specifications that optimise performance while keeping to a minimum the signal requirements and the complexity of the calculations. COMPARISON WITH EXISTING METHODS A key bottleneck for the development of long-term BMIs is to find an inexpensive method for real-time spike sorting. Here, we simulated a solution to this problem that uses both offline and online processing of the data. CONCLUSIONS Hardware implementations of this method therefore enable low-power long-term wireless transmission of multiple site extracellular recordings, with application to wireless BMIs or closed-loop stimulation designs.

Post-decisional accounts of biases in confidence

Most models of decision-making suggest that confidence, the ‘feeling of knowing’ that accompanies our choices, is constructed as the decision unfolds. However, more recent studies have noted that processes occurring after we commit to a particular choice also affect this subjective belief. This leads to the following question: when are we better judges of ourselves? If, after a decision, evidence continues to accumulate in an unbiased manner, then our confidence judgements should improve. Conversely, if post-decisional information processing is biased, our sense of confidence could be distorted, and so our confidence judgements should degrade with time. We briefly discuss recently proposed models of post-decisional evidence accumulation, and explore whether, and how, biases in confidence could arise.

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.

The idiosyncratic nature of confidence

Confidence is the ‘feeling of knowing’ that accompanies decision-making. Bayesian theory proposes that confidence is a function solely of the perceived probability of being correct. Empirical research has suggested, however, that different individuals may perform different computations to estimate confidence from uncertain evidence. To test this hypothesis, we collected confidence reports in a task in which subjects made categorical decisions about the mean of a sequence. We found that for most individuals, confidence did indeed reflect the perceived probability of being correct. However, in approximately half of them, confidence also reflected a different probabilistic quantity: the perceived uncertainty in the estimated variable. We found that the contribution of both quantities was stable over weeks. We also observed that the influence of the perceived probability of being correct was stable across two tasks, one perceptual and one cognitive. Overall, our findings provide a computational interpretation of individual differences in human confidence.Using behavioural experiments and computational modelling, Navajas and colleagues provide a systematic characterization of individual differences in human confidence.

Late EEG Responses Are Absent for Conscious But Task-Irrelevant Stimuli.

Thousands of neural circuits are activated in our brains each second, but only some of them (quite mysteriously) give rise to conscious perception ([Koch, 2004][1]). The neuroscience of consciousness is the quest to identify these processes: a crucial pursuit with a wide range of applications, such

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.

The visual development of hand-centered receptive fields in a neural network model of the primate visual system trained with experimentally recorded human gaze changes

ABSTRACT Neurons have been found in the primate brain that respond to objects in specific locations in hand-centered coordinates. A key theoretical challenge is to explain how such hand-centered neuronal responses may develop through visual experience. In this paper we show how hand-centered visual receptive fields can develop using an artificial neural network model, VisNet, of the primate visual system when driven by gaze changes recorded from human test subjects as they completed a jigsaw. A camera mounted on the head captured images of the hand and jigsaw, while eye movements were recorded using an eye-tracking device. This combination of data allowed us to reconstruct the retinal images seen as humans undertook the jigsaw task. These retinal images were then fed into the neural network model during self-organization of its synaptic connectivity using a biologically plausible trace learning rule. A trace learning mechanism encourages neurons in the model to learn to respond to input images that tend to occur in close temporal proximity. In the data recorded from human subjects, we found that the participant’s gaze often shifted through a sequence of locations around a fixed spatial configuration of the hand and one of the jigsaw pieces. In this case, trace learning should bind these retinal images together onto the same subset of output neurons. The simulation results consequently confirmed that some cells learned to respond selectively to the hand and a jigsaw piece in a fixed spatial configuration across different retinal views.

Extracting information from the shape and spatial distribution of evoked potentials

Highlights • A decoding approach for extracting and quantifying information from ERPs is proposed.• The proposed framework extracts more information than standard supervised approaches.• The method allows analysis of multichannel signals.