Attractive serial dependence overcomes repulsive neuronal adaptation.

Sensory responses and behavior are strongly shaped by stimulus history. For example, perceptual reports are sometimes biased toward previously viewed stimuli (serial dependence). While behavioral studies have pointed to both perceptual and postperceptual origins of this phenomenon, neural data that could elucidate where these biases emerge is limited. We recorded functional magnetic resonance imaging (fMRI) responses while human participants (male and female) performed a delayed orientation discrimination task. While behavioral reports were attracted to the previous stimulus, response patterns in visual cortex were repelled. We reconciled these opposing neural and behavioral biases using a model where both sensory encoding and readout are shaped by stimulus history. First, neural adaptation reduces redundancy at encoding and leads to the repulsive biases that we observed in visual cortex. Second, our modeling work suggest that serial dependence is induced by readout mechanisms that account for adaptation in visual cortex. According to this account, the visual system can simultaneously improve efficiency via adaptation while still optimizing behavior based on the temporal structure of natural stimuli.

A neural data structure for novelty detection.

Novelty detection is a fundamental biological problem that organisms must solve to determine whether a given stimulus departs from those previously experienced. In computer science, this problem is solved efficiently using a data structure called a Bloom filter. We found that the fruit fly olfactory circuit evolved a variant of a Bloom filter to assess the novelty of odors. Compared with a traditional Bloom filter, the fly adjusts novelty responses based on two additional features: the similarity of an odor to previously experienced odors and the time elapsed since the odor was last experienced. We elaborate and validate a framework to predict novelty responses of fruit flies to given pairs of odors. We also translate insights from the fly circuit to develop a class of distance- and time-sensitive Bloom filters that outperform prior filters when evaluated on several biological and computational datasets. Overall, our work illuminates the algorithmic basis of an important neurobiological problem and offers strategies for novelty detection in computational systems.

Signal Complexity of Human Intracranial EEG Tracks Successful Associative-Memory Formation across Individuals.

Memory performance is highly variable among individuals. Most studies examining human memory, however, have largely focused on the neural correlates of successful memory formation within individuals, rather than the differences among them. As such, what gives rise to this variability is poorly understood. Here, we examined intracranial EEG (iEEG) recordings captured from 43 participants (23 male) implanted with subdural electrodes for seizure monitoring as they performed a paired-associates verbal memory task. We identified three separate but related signatures of neural activity that tracked differences in successful memory formation across individuals. High-performing individuals consistently exhibited less broadband power, flatter power spectral density slopes, and greater complexity in their iEEG signals. Furthermore, within individuals across three separate time scales ranging from seconds to days, successful recall was positively associated with these same metrics. Our data therefore suggest that memory ability across individuals can be indexed by increased neural signal complexity.SIGNIFICANCE STATEMENT We show that participants whose intracranial EEG exhibits less low-frequency power, flatter power spectrums, and greater sample entropy overall are better able to memorize associations, and that the same metrics track fluctuations in memory performance across time within individuals. These metrics together signify greater neural signal complexity, which may index the brain's ability to flexibly engage with information and generate separable memory representations. Critically, the current set of results provides a unique window into the neural markers of individual differences in memory performance, which have hitherto been underexplored.

Characterizing and predicting cortical evoked responses to direct electrical stimulation of the human brain.

BACKGROUND: Direct electrical stimulation of the human brain has been used to successfully treat several neurological disorders, but the precise effects of stimulation on neural activity are poorly understood. Characterizing the neural response to stimulation, however, could allow clinicians and researchers to more accurately predict neural responses, which could in turn lead to more effective stimulation for treatment and to fundamental knowledge regarding neural function. OBJECTIVE: Here we use a linear systems approach in order to characterize the response to electrical stimulation across cortical locations and then to predict the responses to novel inputs. METHODS: We use intracranial electrodes to directly stimulate the human brain with single pulses of stimulation using amplitudes drawn from a random distribution. Based on the evoked responses, we generate a simple model capturing the characteristic response to stimulation at each cortical site. RESULTS: We find that the variable dynamics of the evoked response across cortical locations can be captured using the same simple architecture, a linear time-invariant system that operates separately on positive and negative input pulses of stimulation. We demonstrate that characterizing the response to stimulation using this simple and tractable model of evoked responses enables us to predict the responses to subsequent stimulation with single pulses with novel amplitudes, and the compound response to stimulation with multiple pulses. CONCLUSION: Our data suggest that characterizing the response to stimulation in an approximately linear manner can provide a powerful and principled approach for predicting the response to direct electrical stimulation.

Changing temporal context in human temporal lobe promotes memory of distinct episodes.

Memories of experiences that occur around the same time are linked together by a shared temporal context, represented by shared patterns of neural activity. However, shared temporal context may be problematic for selective retrieval of specific memories. Here, we examine intracranial EEG (iEEG) in the human temporal lobe as participants perform a verbal paired associates memory task that requires the encoding of distinct word pairs in memory. We find that the rate of change in patterns of low frequency (3-12 Hz) power distributed across the temporal lobe is significantly related to memory performance. We also find that exogenous electrical stimulation affects how quickly these neural representations of temporal context change with time, which directly affects the ability to successfully form memories for distinct items. Our results indicate that the ability to retrieve distinct episodic memories is related to how quickly neural representations of temporal context change over time during encoding.

Principled Approaches to Direct Brain Stimulation for Cognitive Enhancement.

In this brief review, we identify key areas of research that inform a systematic and targeted approach for invasive brain stimulation with the goal of modulating higher cognitive functions such as memory. We outline several specific challenges that must be successfully navigated in order to achieve this goal. Specifically, using direct brain stimulation to support memory requires demonstrating that (1) there are reliable neural patterns corresponding to different events and memory states, (2) stimulation can be used to induce these target activity patterns, and (3) inducing such patterns modulates memory in the expected directions. Invasive stimulation studies typically have not taken into account intrinsic brain states and dynamics, nor have they a priori targeted specific neural patterns that have previously been identified as playing an important role in memory. Moreover, the effects of stimulation on neural activity are poorly understood and are sensitive to multiple factors including the specific stimulation parameters, the processing state of the brain at the time of stimulation, and neuroanatomy of the stimulated region. As a result, several studies have reported conflicting results regarding the use of direct stimulation for memory modulation. Here, we review the latest findings relevant to these issues and discuss how we can gain better control over the effects of direct brain stimulation for modulating human memory and cognition.

Near-infrared spectroscopic photoacoustic microscopy using a multi-color fiber laser source.

We demonstrate a simple multi-wavelength optical source suitable for spectroscopic optical resolution photoacoustic microscopy (OR-PAM) of lipid-rich tissue. 1064 nm laser pulses are converted to multiple wavelengths beyond 1300 nm via nonlinear optical propagation in a birefringent optical fiber. OR-PAM experiments with lipid phantoms clearly show the expected absorption peak near 1210 nm. We believe this simple multi-color technique is a promising cost-effective approach to spectroscopic OR-PAM of lipid-rich tissue.