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Perception is shaped by past experience, both cumulative and contextual. Serial dependence reflects a contextual attractive bias to perceive or report the world as more stable than it truly is. As serial dependence has often been examined in continuous report or change detection tasks, it unclear whether attraction is towards the identity of the previous stimulus feature, or rather to theresponsemade to indicate theperceivedstimulus value on the previous trial. The physical and reported identities can be highly correlated depending on properties of the stimulus and task design. However, they are distinct values and dissociating them is important because it can reveal information about the role of sensory and non-sensory contributions to attractive biases. These alternative possibilities can be challenging to disentangle because 1) stimulus values and responses are typically strongly correlated and 2) measuring response biases using standard techniques can be confounded bycontext-independentbiases such ascardinal biasfor orientation (i.e., higher precision, but repelled, responses from vertical and horizontal orientations). Here we explore the issues and confounds related to measuring response biases using simulations. Under a range of conditions, we find that response-induced biases can be reliably distinguished from stimulus-induced biases and from confounds introduced bycontext-independentbiases. We then applied these approaches to a delayed report dataset (N=18) and found evidence for response over a stimulus driven history bias. This work demonstrates that stimulus and response driven history biases can be reliably dissociated and provides code to implement these analysis procedures.

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.

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., Memories formed around the same time are linked together by a shared temporal context. Here, the authors show that the ability to selectively retrieve distinct episodic memories formed close together in time is related to how quickly neural representations of temporal context change over time during encoding.

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.

Sensory responses and behavior are strongly shaped by stimulus history. For instance, perceptual reports are sometimes biased towards previously viewed stimuli (serial dependence). While behavioral studies have pointed to both perceptual and post-perceptual origins of this phenomenon, neural data that could elucidate where these biases emerge is limited. We recorded 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.

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.

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 STATEMENTWe 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.

Memory performance is highly variable between individuals. Most studies examining human memory, however, have largely focused on the neural correlates of successful memory formation within individuals, rather than the differences between 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 (PSD) 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 StatementWe 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 provide a unique window into the neural markers of individual differences in memory performance which have hitherto been underexplored.

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.

Photoacoustic microscopy (PAM) provides excellent image contrast based on optical absorption. Lipidrich tissue, such as atherosclerotic plaques and myelinated nerve fibers, exhibits an optical absorption peak near 1210 nm. Unfortunately, pulsed lasers operating in this wavelength range use expensive optical parametric oscillator (OPO) systems. We demonstrate a simple approach to convert an inexpensive 1064 nm pulsed laser into a multi-wavelength source suitable for spectroscopic PAM of lipid-rich tissue. Wavelength conversion is achieved by using stimulated Raman scattering (SRS) in an optical fiber. A sufficiently intense laser pulse nonlinearly interacts with the internal vibrations of the glass molecular structure to produce a series of down-shifted frequency components (Stokes lines). We use a Q-switched Nd:YAG microchip laser producing 0.6 ns duration pulses at 1064 nm with 8 uJ of energy at a 7.4 kHz repetition rate. The laser pulses are coupled into a 20 meter long single-mode fiber. The multi-color fiber output goes through a band pass filter, where the selected wavelength is sent to a photoacoustic microscopy system employing optical focusing. Pulse energies above 200 nJ are produced within spectral bands at 1064, 1100, 1175, 1225, 1275, and 1325 nm. Imaging experiments with phantoms containing butter clearly show the expected absorption peak near 1210 nm. A major advantage of our technique is the simple arrangement to convert a single-wavelength laser into a multi-color source for spectroscopic PAM. We believe this multi-color technique is a promising method to achieve high-speed spectroscopic photoacoustic microscopy of lipid-rich tissue.