Human scalp potentials reflect a mixture of decision-related signals during perceptual choices

Single-unit animal studies have consistently reported decision-related activity mirroring a process of temporal accumulation of sensory evidence to a fixed internal decision boundary. To date, our understanding of how response patterns seen in single-unit data manifest themselves at the macroscopic level of brain activity obtained from human neuroimaging data remains limited. Here, we use single-trial analysis of human electroencephalography data to show that population responses on the scalp can capture choice-predictive activity that builds up gradually over time with a rate proportional to the amount of sensory evidence, consistent with the properties of a drift-diffusion-like process as characterized by computational modeling. Interestingly, at time of choice, scalp potentials continue to appear parametrically modulated by the amount of sensory evidence rather than converging to a fixed decision boundary as predicted by our model. We show that trial-to-trial fluctuations in these response-locked signals exert independent leverage on behavior compared with the rate of evidence accumulation earlier in the trial. These results suggest that in addition to accumulator signals, population responses on the scalp reflect the influence of other decision-related signals that continue to covary with the amount of evidence at time of choice

Feature Topography and Sound Intensity Level Encoding in Primary Auditory Cortex

The primary auditory cortex: A1) in mammals is one of the first areas in the neocortex that receives auditory related spiking activity from the thalamus. Because the neocortex is implicated in regulating high-level brain phenomena, such as attention and perception, it is therefore important in regards to these high-level behaviors to understand how sounds are represented and transformed by neuronal circuits in this area. The topographic organization of neuronal responses to auditory features in A1 provides evidence for potential mechanisms and functional roles of this neural circuitry. This dissertation presents results from models of topographic organization supporting the notion that if the topographic organization of frequency responses, termed tonotopy or cochleotopy, is aligned along the longest anatomical line segment in A1, as supported by some physiological studies, then it is unlikely that any other topography is mapped monotonically along the orthogonal axis. Thresholds of neuronal responses to sound intensity level represent a particular feature that may have a local, highly periodic topography and that is vital to the sensitivity of the auditory system. The neuronal representation of sound level in A1, particularly as it relates to encoding accuracy, contains a distribution of neurons with varying amounts of inhibition at high sound levels. Neurons with large amounts of this high-level inhibition are described as nonmonotonic or level-tuned. This dissertation presents evidence from single neuron recordings in A1 that neurons exhibiting greater high-level inhibition also exhibit lower neuronal thresholds and that lower thresholds in these nonmonotonic neurons are preserved even when much of the neuronal population is adapted for accurately encoding more intense sounds. Evidence presented in this dissertation also suggests that nonmonotonic neurons have transient responses to time-varying: dynamic) level stimuli that adapt more quickly in response to low-level sounds than those of monotonic neurons. Together these results imply that under static, steady-state-dynamic and transient-dynamic sound level conditions, nonmonotonic neurons are specialized encoders of less intense sounds that allow the auditory system to maintain sensitivity under a variety of environmental conditions

Testing multi-alternative decision models with non-stationary evidence

Recent research has investigated the process of integrating perceptual evidence toward a decision, converging on a number of sequential sampling choice models, such as variants of race and diffusion models and the non-linear leaky competing accumulator (LCA) model. Here we study extensions of these models to multi-alternative choice, considering how well they can account for data from a psychophysical experiment in which the evidence supporting each of the alternatives changes dynamically during the trial, in a way that creates temporal correlations. We find that participants exhibit a tendency to choose an alternative whose evidence profile is temporally anti-correlated with (or dissimilar from) that of other alternatives. This advantage of the anti-correlated alternative is well accounted for in the LCA, and provides constraints that challenge several other models of multi-alternative choice

Principles of Neuromorphic Photonics

In an age overrun with information, the ability to process reams of data has become crucial. The demand for data will continue to grow as smart gadgets multiply and become increasingly integrated into our daily lives. Next-generation industries in artificial intelligence services and high-performance computing are so far supported by microelectronic platforms. These data-intensive enterprises rely on continual improvements in hardware. Their prospects are running up against a stark reality: conventional one-size-fits-all solutions offered by digital electronics can no longer satisfy this need, as Moore's law (exponential hardware scaling), interconnection density, and the von Neumann architecture reach their limits. With its superior speed and reconfigurability, analog photonics can provide some relief to these problems; however, complex applications of analog photonics have remained largely unexplored due to the absence of a robust photonic integration industry. Recently, the landscape for commercially-manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. The scientific community has set out to build bridges between the domains of photonic device physics and neural networks, giving rise to the field of \emph{neuromorphic photonics}. This article reviews the recent progress in integrated neuromorphic photonics. We provide an overview of neuromorphic computing, discuss the associated technology (microelectronic and photonic) platforms and compare their metric performance. We discuss photonic neural network approaches and challenges for integrated neuromorphic photonic processors while providing an in-depth description of photonic neurons and a candidate interconnection architecture. We conclude with a future outlook of neuro-inspired photonic processing.Comment: 28 pages, 19 figure

Choices change the temporal weighting of decision evidence

Many decisions result from the accumulation of decision-relevant information (evidence) over time. Even when maximizing decision accuracy requires weighting all the evidence equally, decision-makers often give stronger weight to evidence occurring early or late in the evidence stream. Here, we show changes in such temporal biases within participants as a function of intermittent judgments about parts of the evidence stream. Human participants performed a decision task that required a continuous estimation of the mean evidence at the end of the stream. The evidence was either perceptual (noisy random dot motion) or symbolic (variable sequences of numbers). Participants also reported a categorical judgment of the preceding evidence half-way through the stream in one condition or executed an evidence-independent motor response in another condition. The relative impact of early versus late evidence on the final estimation flipped between these two conditions. In particular, participants’ sensitivity to late evidence after the intermittent judgment, but not the simple motor response, was decreased. Both the intermittent response as well as the final estimation reports were accompanied by nonluminance-mediated increases of pupil diameter. These pupil dilations were bigger during intermittent judgments than simple motor responses and bigger during estimation when the late evidence was consistent than inconsistent with the initial judgment. In sum, decisions activate pupil-linked arousal systems and alter the temporal weighting of decision evidence. Our results are consistent with the idea that categorical choices in the face of uncertainty induce a change in the state of the neural circuits underlying decision-making. NEW & NOTEWORTHY The psychology and neuroscience of decision-making have extensively studied the accumulation of decision-relevant information toward a categorical choice. Much fewer studies have assessed the impact of a choice on the processing of subsequent information. Here, we show that intermittent choices during a protracted stream of input reduce the sensitivity to subsequent decision information and transiently boost arousal. Choices might trigger a state change in the neural machinery for decision-making

Neural correlates and mechanisms of sounds localization in everyday reverberant settings

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 161-176).Nearly all listening environments-indoors and outdoors alike-are full of boundary surfaces (e.g., walls, trees, and rocks) that produce acoustic reflections. These reflections interfere with the direct sound arriving at a listener's ears, distorting the binaural cues for sound localization. Yet, human listeners have little difficulty localizing sounds in most settings. This thesis addresses fundamental questions regarding the neural basis of sound localization in everyday reverberant environments. In the first set of experiments, we investigate the effects of reverberation on the directional sensitivity of low-frequency auditory neurons sensitive to interaural time differences (ITD), the principal cue for localizing sound containing low frequency energy. Because reverberant energy builds up over time, the source location is represented relatively faithfully during the early portion of a sound, but this representation becomes increasingly degraded later in the stimulus. We show that the directional sensitivity of ITD-sensitive neurons in the auditory midbrain of anesthetized cats and awake rabbits follows a similar time course. However, the tendency of neurons to fire preferentially at the onset of a stimulus results in more robust directional sensitivity than expected, suggesting a simple mechanism for improving directional sensitivity in reverberation. To probe the role of temporal response dynamics, we use a conditioning paradigm to systematically alter temporal response patterns of single neurons. Results suggest that making temporal response patterns less onset-dominated typically leads to poorer directional sensitivity in reverberation. In parallel behavioral experiments, we show that human lateralization judgments are consistent with predictions from a population rate model for decoding the observed midbrain responses, suggesting a subcortical origin for robust sound localization in reverberant environments. In the second part of the thesis we examine the effects of reverberation on directional sensitivity of neurons across the tonotopic axis in the awake rabbit auditory midbrain. We find that reverberation degrades the directional sensitivity of single neurons, although the amount of degradation depends on the characteristic frequency and the type of binaural cues available. When ITD is the only available directional cue, low frequency neurons sensitive to ITD in the fine-time structure maintain better directional sensitivity in reverberation than high frequency neurons sensitive to ITD in the envelope. On the other hand, when both ITD and interaural level differences (ILD) cues are available, directional sensitivity is comparable throughout the tonotopic axis, suggesting that, at high frequencies, ILDs provide better directional information than envelope ITDs in reverberation. These findings can account for results from human psychophysical studies of spatial hearing in reverberant environments. This thesis marks fundamental progress towards elucidating the neural basis for spatial hearing in everyday settings. Overall, our results suggest that the information contained in the rate responses of neurons in the auditory midbrain is sufficient to account for human sound localization in reverberant environments.by Sasha Devore.Ph.D

The Neural Mechanisms Underlying Visual Target Search

The task of finding specific objects and switching between targets is ubiquitous in everyday life. Searching for a particular object requires our brains to activate and maintain a representation of the target (working memory), identify each encountered object (object recognition), and determine whether the currently viewed object matches the sought target (decision making). The comparison of working memory and visual information is thought to happen via feedback of target information from higher-order brain areas to the ventral visual pathway. However, what is exactly represented by these areas and how do they implement this comparison still remains unknown. To investigate these questions, we employed a combined approach involving electrophysiology experiments and computational modeling. In particular, we recorded neural responses in inferotemporal (IT) and perirhinal (PRH) cortex as monkeys performed a visual target search task, and we adopted population-based read-outs to measure the amount and format of information contained in these neural populations. In Chapter 2 we report that the total amount of target match information was matched in IT and PRH, but this information was contained in a more explicit (i.e. linearly separable) format in PRH. These results suggest that PRH implements an untangling computation to reformat its inputs from IT. Consistent with this hypothesis, a simple linear-nonlinear model was sufficient to capture the transformation between the two areas. In Chapter 3, we report that the untangling computation in PRH takes time to evolve. While this type of dynamic reformatting is normally attributed to complex recurrent circuits, here we demonstrated that this phenomenon could be accounted by the same instantaneous linear-nonlinear model presented in Chapter 2. This counterintuitive finding was due to the existence of non-stationarities in the IT neural representation. Finally, in Chapter 4 we completely describe a novel set of methods that we developed and applied in Chapters 2 and 3 to quantify the task-specific signals contained in the heterogeneous neural responses in IT and PRH, and to relate these signals to measures of task performance. Together, this body of work revealed a previously unknown untangling computation in PRH during visual search, and demonstrated that a feed-forward linear-nonlinear model is sufficient to describe this computation