Publications by authors named "Earl K Miller"

92 Publications

Interhemispheric transfer of working memories.

Neuron 2021 03 8;109(6):1055-1066.e4. Epub 2021 Feb 8.

The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139 USA. Electronic address:

Visual working memory (WM) storage is largely independent between the left and right visual hemifields/cerebral hemispheres, yet somehow WM feels seamless. We studied how WM is integrated across hemifields by recording neural activity bilaterally from lateral prefrontal cortex. An instructed saccade during the WM delay shifted the remembered location from one hemifield to the other. Before the shift, spike rates and oscillatory power showed clear signatures of memory laterality. After the shift, the lateralization inverted, consistent with transfer of the memory trace from one hemisphere to the other. Transferred traces initially used different neural ensembles from feedforward-induced ones, but they converged at the end of the delay. Around the time of transfer, synchrony between the two prefrontal hemispheres peaked in theta and beta frequencies, with a directionality consistent with memory trace transfer. This illustrates how dynamics between the two cortical hemispheres can stitch together WM traces across visual hemifields.
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http://dx.doi.org/10.1016/j.neuron.2021.01.016DOI Listing
March 2021

Differences in visually induced MEG oscillations reflect differences in deep cortical layer activity.

Commun Biol 2020 Nov 25;3(1):707. Epub 2020 Nov 25.

The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.

Neural activity is organized at multiple scales, ranging from the cellular to the whole brain level. Connecting neural dynamics at different scales is important for understanding brain pathology. Neurological diseases and disorders arise from interactions between factors that are expressed in multiple scales. Here, we suggest a new way to link microscopic and macroscopic dynamics through combinations of computational models. This exploits results from statistical decision theory and Bayesian inference. To validate our approach, we used two independent MEG datasets. In both, we found that variability in visually induced oscillations recorded from different people in simple visual perception tasks resulted from differences in the level of inhibition specific to deep cortical layers. This suggests differences in feedback to sensory areas and each subject's hypotheses about sensations due to differences in their prior experience. Our approach provides a new link between non-invasive brain imaging data, laminar dynamics and top-down control.
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http://dx.doi.org/10.1038/s42003-020-01438-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7688644PMC
November 2020

Layer and rhythm specificity for predictive routing.

Proc Natl Acad Sci U S A 2020 12 23;117(49):31459-31469. Epub 2020 Nov 23.

The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139.

In predictive coding, experience generates predictions that attenuate the feeding forward of predicted stimuli while passing forward unpredicted "errors." Different models have suggested distinct cortical layers, and rhythms implement predictive coding. We recorded spikes and local field potentials from laminar electrodes in five cortical areas (visual area 4 [V4], lateral intraparietal [LIP], posterior parietal area 7A, frontal eye field [FEF], and prefrontal cortex [PFC]) while monkeys performed a task that modulated visual stimulus predictability. During predictable blocks, there was enhanced alpha (8 to 14 Hz) or beta (15 to 30 Hz) power in all areas during stimulus processing and prestimulus beta (15 to 30 Hz) functional connectivity in deep layers of PFC to the other areas. Unpredictable stimuli were associated with increases in spiking and in gamma-band (40 to 90 Hz) power/connectivity that fed forward up the cortical hierarchy via superficial-layer cortex. Power and spiking modulation by predictability was stimulus specific. Alpha/beta power in LIP, FEF, and PFC inhibited spiking in deep layers of V4. Area 7A uniquely showed increases in high-beta (∼22 to 28 Hz) power/connectivity to unpredictable stimuli. These results motivate a conceptual model, predictive routing. It suggests that predictive coding may be implemented via lower-frequency alpha/beta rhythms that "prepare" pathways processing-predicted inputs by inhibiting feedforward gamma rhythms and associated spiking.
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http://dx.doi.org/10.1073/pnas.2014868117DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7733827PMC
December 2020

Conjunctive representation of what and when in monkey hippocampus and lateral prefrontal cortex during an associative memory task.

Hippocampus 2020 Dec 11;30(12):1332-1346. Epub 2020 Nov 11.

Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts, USA.

Adaptive memory requires the organism to form associations that bridge between events separated in time. Many studies show interactions between hippocampus (HPC) and prefrontal cortex (PFC) during formation of such associations. We analyze neural recording from monkey HPC and PFC during a memory task that requires the monkey to associate stimuli separated by about a second in time. After the first stimulus was presented, large numbers of units in both HPC and PFC fired in sequence. Many units fired only when a particular stimulus was presented at a particular time in the past. These results indicate that both HPC and PFC maintain a temporal record of events that could be used to form associations across time. This temporal record of the past is a key component of the temporal coding hypothesis, a hypothesis in psychology that memory not only encodes what happened, but when it happened.
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http://dx.doi.org/10.1002/hipo.23282DOI Listing
December 2020

Charles Gordon Gross (1936-2019).

Prog Neurobiol 2020 12 20;195:101934. Epub 2020 Oct 20.

The McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States.

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http://dx.doi.org/10.1016/j.pneurobio.2020.101934DOI Listing
December 2020

Achieving stable dynamics in neural circuits.

PLoS Comput Biol 2020 08 7;16(8):e1007659. Epub 2020 Aug 7.

The Picower Institute for Learning & Memory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America.

The brain consists of many interconnected networks with time-varying, partially autonomous activity. There are multiple sources of noise and variation yet activity has to eventually converge to a stable, reproducible state (or sequence of states) for its computations to make sense. We approached this problem from a control-theory perspective by applying contraction analysis to recurrent neural networks. This allowed us to find mechanisms for achieving stability in multiple connected networks with biologically realistic dynamics, including synaptic plasticity and time-varying inputs. These mechanisms included inhibitory Hebbian plasticity, excitatory anti-Hebbian plasticity, synaptic sparsity and excitatory-inhibitory balance. Our findings shed light on how stable computations might be achieved despite biological complexity. Crucially, our analysis is not limited to analyzing the stability of fixed geometric objects in state space (e.g points, lines, planes), but rather the stability of state trajectories which may be complex and time-varying.
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http://dx.doi.org/10.1371/journal.pcbi.1007659DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7446801PMC
August 2020

Task-evoked activity quenches neural correlations and variability across cortical areas.

PLoS Comput Biol 2020 08 3;16(8):e1007983. Epub 2020 Aug 3.

Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America.

Many large-scale functional connectivity studies have emphasized the importance of communication through increased inter-region correlations during task states. In contrast, local circuit studies have demonstrated that task states primarily reduce correlations among pairs of neurons, likely enhancing their information coding by suppressing shared spontaneous activity. Here we sought to adjudicate between these conflicting perspectives, assessing whether co-active brain regions during task states tend to increase or decrease their correlations. We found that variability and correlations primarily decrease across a variety of cortical regions in two highly distinct data sets: non-human primate spiking data and human functional magnetic resonance imaging data. Moreover, this observed variability and correlation reduction was accompanied by an overall increase in dimensionality (reflecting less information redundancy) during task states, suggesting that decreased correlations increased information coding capacity. We further found in both spiking and neural mass computational models that task-evoked activity increased the stability around a stable attractor, globally quenching neural variability and correlations. Together, our results provide an integrative mechanistic account that encompasses measures of large-scale neural activity, variability, and correlations during resting and task states.
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http://dx.doi.org/10.1371/journal.pcbi.1007983DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7425988PMC
August 2020

Preservation and Changes in Oscillatory Dynamics across the Cortical Hierarchy.

J Cogn Neurosci 2020 10 23;32(10):2024-2035. Epub 2020 Jun 23.

Massachusetts Institute of Technology.

Theta (2-8 Hz), alpha (8-12 Hz), beta (12-35 Hz), and gamma (>35 Hz) rhythms are ubiquitous in the cortex. However, there is little understanding of whether they have similar properties and functions in different cortical areas because they have rarely been compared across them. We record neuronal spikes and local field potentials simultaneously at several levels of the cortical hierarchy in monkeys. Theta, alpha, beta, and gamma oscillations had similar relationships to spiking activity in visual, parietal, and prefrontal cortices. However, the frequencies in all bands increased up the cortical hierarchy. These results suggest that these rhythms have similar inhibitory and excitatory functions across the cortex. We discuss how the increase in frequencies up the cortical hierarchy may help sculpt cortical flow and processing.
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http://dx.doi.org/10.1162/jocn_a_01600DOI Listing
October 2020

Prefrontal oscillations modulate the propagation of neuronal activity required for working memory.

Neurobiol Learn Mem 2020 09 17;173:107228. Epub 2020 Jun 17.

Department of Mathematics and Statistics, Boston University, Boston, MA 02215, United States. Electronic address:

Cognition involves using attended information, maintained in working memory (WM), to guide action. During a cognitive task, a correct response requires flexible, selective gating so that only the appropriate information flows from WM to downstream effectors that carry out the response. In this work, we used biophysically-detailed modeling to explore the hypothesis that network oscillations in prefrontal cortex (PFC), leveraging local inhibition, can independently gate responses to items in WM. The key role of local inhibition was to control the period between spike bursts in the outputs, and to produce an oscillatory response no matter whether the WM item was maintained in an asynchronous or oscillatory state. We found that the WM item that induced an oscillatory population response in the PFC output layer with the shortest period between spike bursts was most reliably propagated. The network resonant frequency (i.e., the input frequency that produces the largest response) of the output layer can be flexibly tuned by varying the excitability of deep layer principal cells. Our model suggests that experimentally-observed modulation of PFC beta-frequency (15-30 Hz) and gamma-frequency (30-80 Hz) oscillations could leverage network resonance and local inhibition to govern the flexible routing of signals in service to cognitive processes like gating outputs from working memory and the selection of rule-based actions. Importantly, we show for the first time that nonspecific changes in deep layer excitability can tune the output gate's resonant frequency, enabling the specific selection of signals encoded by populations in asynchronous or fast oscillatory states. More generally, this represents a dynamic mechanism by which adjusting network excitability can govern the propagation of asynchronous and oscillatory signals throughout neocortex.
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http://dx.doi.org/10.1016/j.nlm.2020.107228DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7429344PMC
September 2020

A Geometric Characterization of Population Coding in the Prefrontal Cortex and Hippocampus during a Paired-Associate Learning Task.

J Cogn Neurosci 2020 08 7;32(8):1455-1465. Epub 2020 May 7.

Boston University.

Large-scale neuronal recording techniques have enabled discoveries of population-level mechanisms for neural computation. However, it is not clear how these mechanisms form by trial-and-error learning. In this article, we present an initial effort to characterize the population activity in monkey prefrontal cortex (PFC) and hippocampus (HPC) during the learning phase of a paired-associate task. To analyze the population data, we introduce the normalized distance, a dimensionless metric that describes the encoding of cognitive variables from the geometrical relationship among neural trajectories in state space. It is found that PFC exhibits a more sustained encoding of the visual stimuli, whereas HPC only transiently encodes the identity of the associate stimuli. Surprisingly, after learning, the neural activity is not reorganized to reflect the task structure, raising the possibility that learning is accompanied by some "silent" mechanism that does not explicitly change the neural representations. We did find partial evidence on the learning-dependent changes for some of the task variables. This study shows the feasibility of using normalized distance as a metric to characterize and compare population-level encoding of task variables and suggests further directions to explore learning-dependent changes in the neural circuits.
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http://dx.doi.org/10.1162/jocn_a_01569DOI Listing
August 2020

An Ultra-Sensitive Step-Function Opsin for Minimally Invasive Optogenetic Stimulation in Mice and Macaques.

Neuron 2020 07 29;107(1):38-51.e8. Epub 2020 Apr 29.

McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Electronic address:

Optogenetics is among the most widely employed techniques to manipulate neuronal activity. However, a major drawback is the need for invasive implantation of optical fibers. To develop a minimally invasive optogenetic method that overcomes this challenge, we engineered a new step-function opsin with ultra-high light sensitivity (SOUL). We show that SOUL can activate neurons located in deep mouse brain regions via transcranial optical stimulation and elicit behavioral changes in SOUL knock-in mice. Moreover, SOUL can be used to modulate neuronal spiking and induce oscillations reversibly in macaque cortex via optical stimulation from outside the dura. By enabling external light delivery, our new opsin offers a minimally invasive tool for manipulating neuronal activity in rodent and primate models with fewer limitations on the depth and size of target brain regions and may further facilitate the development of minimally invasive optogenetic tools for the treatment of neurological disorders.
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http://dx.doi.org/10.1016/j.neuron.2020.03.032DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7351618PMC
July 2020

Extracellular Spike Waveform Dissociates Four Functionally Distinct Cell Classes in Primate Cortex.

Curr Biol 2019 09 22;29(18):2973-2982.e5. Epub 2019 Aug 22.

Centre for Integrative Neuroscience, University of Tübingen, Otfried-Müller-Strasse 25, 72076 Tübingen, Germany; Hertie Institute for Clinical Brain Research, University of Tübingen, Otfried-Müller-Strasse 27, 72076 Tübingen, Germany; MEG Center, University of Tübingen, Otfried-Müller-Strasse 47, 72076 Tübingen, Germany. Electronic address:

Understanding the function of different neuronal cell types is key to understanding brain function. However, cell-type diversity is typically overlooked in electrophysiological studies in awake behaving animals. Here, we show that four functionally distinct cell classes can be robustly identified from extracellular recordings in several cortical regions of awake behaving monkeys. We recorded extracellular spiking activity from dorsolateral prefrontal cortex (dlPFC), the frontal eye field (FEF), and the lateral intraparietal area of macaque monkeys during a visuomotor decision-making task. We employed unsupervised clustering of spike waveforms, which robustly dissociated four distinct cell classes across all three brain regions. The four cell classes were functionally distinct. They showed different baseline firing statistics, visual response dynamics, and coding of visual information. Although cell-class-specific baseline statistics were consistent across brain regions, response dynamics and information coding were regionally specific. Our results identify four functionally distinct spike-waveform-based cell classes in primate cortex. This opens a new window to dissect and study the cell-type-specific function of cortical circuits.
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http://dx.doi.org/10.1016/j.cub.2019.07.051DOI Listing
September 2019

Sensory processing and categorization in cortical and deep neural networks.

Neuroimage 2019 11 21;202:116118. Epub 2019 Aug 21.

The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.

Many recent advances in artificial intelligence (AI) are rooted in visual neuroscience. However, ideas from more complicated paradigms like decision-making are less used. Although automated decision-making systems are ubiquitous (driverless cars, pilot support systems, medical diagnosis algorithms etc.), achieving human-level performance in decision making tasks is still a challenge. At the same time, these tasks that are hard for AI are easy for humans. Thus, understanding human brain dynamics during these decision-making tasks and modeling them using deep neural networks could improve AI performance. Here we modelled some of the complex neural interactions during a sensorimotor decision making task. We investigated how brain dynamics flexibly represented and distinguished between sensory processing and categorization in two sensory domains: motion direction and color. We used two different approaches for understanding neural representations. We compared brain responses to 1) the geometry of a sensory or category domain (domain selectivity) and 2) predictions from deep neural networks (computation selectivity). Both approaches gave us similar results. This confirmed the validity of our analyses. Using the first approach, we found that neural representations changed depending on context. We then trained deep recurrent neural networks to perform the same tasks as the animals. Using the second approach, we found that computations in different brain areas also changed flexibly depending on context. Color computations appeared to rely more on sensory processing, while motion computations more on abstract categories. Overall, our results shed light to the biological basis of categorization and differences in selectivity and computations in different brain areas. They also suggest a way for studying sensory and categorical representations in the brain: compare brain responses to both a behavioral model and a deep neural network and test if they give similar results.
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http://dx.doi.org/10.1016/j.neuroimage.2019.116118DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6819254PMC
November 2019

Monkey EEG links neuronal color and motion information across species and scales.

Elife 2019 07 9;8. Epub 2019 Jul 9.

Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany.

It remains challenging to relate EEG and MEG to underlying circuit processes and comparable experiments on both spatial scales are rare. To close this gap between invasive and non-invasive electrophysiology we developed and recorded human-comparable EEG in macaque monkeys during visual stimulation with colored dynamic random dot patterns. Furthermore, we performed simultaneous microelectrode recordings from 6 areas of macaque cortex and human MEG. Motion direction and color information were accessible in all signals. Tuning of the non-invasive signals was similar to V4 and IT, but not to dorsal and frontal areas. Thus, MEG and EEG were dominated by early visual and ventral stream sources. Source level analysis revealed corresponding information and latency gradients across cortex. We show how information-based methods and monkey EEG can identify analogous properties of visual processing in signals spanning spatial scales from single units to MEG - a valuable framework for relating human and animal studies.
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http://dx.doi.org/10.7554/eLife.45645DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6615858PMC
July 2019

Targeting Cognition and Networks Through Neural Oscillations: Next-Generation Clinical Brain Stimulation.

JAMA Psychiatry 2019 07;76(7):671-672

The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge.

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http://dx.doi.org/10.1001/jamapsychiatry.2019.0740DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7067567PMC
July 2019

Altering alpha-frequency brain oscillations with rapid analog feedback-driven neurostimulation.

PLoS One 2018 5;13(12):e0207781. Epub 2018 Dec 5.

Picower Institute for Learning & Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.

Oscillations of the brain's local field potential (LFP) may coordinate neural ensembles and brain networks. It has been difficult to causally test this model or to translate its implications into treatments, because there are few reliable ways to alter LFP oscillations. We developed a closed-loop analog circuit to enhance brain oscillations by feeding them back into cortex through phase-locked transcranial electrical stimulation. We tested the system in a rhesus macaque with chronically implanted electrode arrays, targeting 8-15 Hz (alpha) oscillations. Ten seconds of stimulation increased alpha oscillatory power for up to 1 second after stimulation offset. In contrast, open-loop stimulation decreased alpha power. There was no effect in the neighboring 15-30 Hz (beta) LFP rhythm or on a neighboring array that did not participate in closed-loop feedback. Analog closed-loop neurostimulation might thus be a useful strategy for altering brain oscillations, both for basic research and the treatment of neuro-psychiatric disease.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0207781PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6281199PMC
May 2019

Working Memory 2.0.

Neuron 2018 10;100(2):463-475

The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Working memory is the fundamental function by which we break free from reflexive input-output reactions to gain control over our own thoughts. It has two types of mechanisms: online maintenance of information and its volitional or executive control. Classic models proposed persistent spiking for maintenance but have not explicitly addressed executive control. We review recent theoretical and empirical studies that suggest updates and additions to the classic model. Synaptic weight changes between sparse bursts of spiking strengthen working memory maintenance. Executive control acts via interplay between network oscillations in gamma (30-100 Hz) in superficial cortical layers (layers 2 and 3) and alpha and beta (10-30 Hz) in deep cortical layers (layers 5 and 6). Deep-layer alpha and beta are associated with top-down information and inhibition. It regulates the flow of bottom-up sensory information associated with superficial layer gamma. We propose that interactions between different rhythms in distinct cortical layers underlie working memory maintenance and its volitional control.
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http://dx.doi.org/10.1016/j.neuron.2018.09.023DOI Listing
October 2018

On the Role of Cortex-Basal Ganglia Interactions for Category Learning: A Neurocomputational Approach.

J Neurosci 2018 10 18;38(44):9551-9562. Epub 2018 Sep 18.

Chemnitz University of Technology, Department of Computer Science, 09107 Chemnitz, Germany,

In addition to the prefrontal cortex (PFC), the basal ganglia (BG) have been increasingly often reported to play a fundamental role in category learning, but the circuit mechanisms mediating their interaction remain to be explored. We developed a novel neurocomputational model of category learning that particularly addresses the BG-PFC interplay. We propose that the BG bias PFC activity by removing the inhibition of cortico-thalamo-cortical loop and thereby provide a teaching signal to guide the acquisition of category representations in the corticocortical associations to the PFC. Our model replicates key behavioral and physiological data of macaque monkey learning a prototype distortion task from Antzoulatos and Miller (2011) Our simulations allowed us to gain a deeper insight into the observed drop of category selectivity in striatal neurons seen in the experimental data and in the model. The simulation results and a new analysis of the experimental data based on the model's predictions show that the drop in category selectivity of the striatum emerges as the variability of responses in the striatum rises when confronting the BG with an increasingly larger number of stimuli to be classified. The neurocomputational model therefore provides new testable insights of systems-level brain circuits involved in category learning that may also be generalized to better understand other cortico-BG-cortical loops. Inspired by the idea that basal ganglia (BG) teach the prefrontal cortex (PFC) to acquire category representations, we developed a novel neurocomputational model and tested it on a task that was recently applied in monkey experiments. As an advantage over previous models of category learning, our model allows to compare simulation data with single-cell recordings in PFC and BG. We not only derived model predictions, but already verified a prediction to explain the observed drop in striatal category selectivity. When testing our model with a simple, real-world face categorization task, we observed that the fast striatal learning with a performance of 85% correct responses can teach the slower PFC learning to push the model performance up to almost 100%.
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http://dx.doi.org/10.1523/JNEUROSCI.0874-18.2018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6209843PMC
October 2018

Working Memory: Delay Activity, Yes! Persistent Activity? Maybe Not.

J Neurosci 2018 08;38(32):7013-7019

Picower Institute for Learning & Memory and Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 and

Persistent spiking has been thought to underlie working memory (WM). However, virtually all of the evidence for this comes from studies that averaged spiking across time and across trials, which masks the details. On single trials, activity often occurs in sparse transient bursts. This has important computational and functional advantages. In addition, examination of more complex tasks reveals neural coding in WM is dynamic over the course of a trial. All this suggests that spiking is important for WM, but that its role is more complex than simply persistent spiking.Persistent Spiking Activity Underlies Working Memory, by Christos Constantinidis, Shintaro Funahashi, Daeyeol Lee, John D. Murray, Xue-Lian Qi, Min Wang, and Amy F.T. Arnsten.
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http://dx.doi.org/10.1523/JNEUROSCI.2485-17.2018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6083456PMC
August 2018

Gradual progression from sensory to task-related processing in cerebral cortex.

Proc Natl Acad Sci U S A 2018 07 10;115(30):E7202-E7211. Epub 2018 Jul 10.

The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139;

Somewhere along the cortical hierarchy, behaviorally relevant information is distilled from raw sensory inputs. We examined how this transformation progresses along multiple levels of the hierarchy by comparing neural representations in visual, temporal, parietal, and frontal cortices in monkeys categorizing across three visual domains (shape, motion direction, and color). Representations in visual areas middle temporal (MT) and V4 were tightly linked to external sensory inputs. In contrast, lateral prefrontal cortex (PFC) largely represented the abstracted behavioral relevance of stimuli (task rule, motion category, and color category). Intermediate-level areas, including posterior inferotemporal (PIT), lateral intraparietal (LIP), and frontal eye fields (FEF), exhibited mixed representations. While the distribution of sensory information across areas aligned well with classical functional divisions (MT carried stronger motion information, and V4 and PIT carried stronger color and shape information), categorical abstraction did not, suggesting these areas may participate in different networks for stimulus-driven and cognitive functions. Paralleling these representational differences, the dimensionality of neural population activity decreased progressively from sensory to intermediate to frontal cortex. This shows how raw sensory representations are transformed into behaviorally relevant abstractions and suggests that the dimensionality of neural activity in higher cortical regions may be specific to their current task.
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http://dx.doi.org/10.1073/pnas.1717075115DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6064981PMC
July 2018

Detecting multivariate cross-correlation between brain regions.

J Neurophysiol 2018 10 27;120(4):1962-1972. Epub 2018 Jun 27.

Department of Statistics, Carnegie Mellon University , Pittsburgh, Pennsylvania.

The problem of identifying functional connectivity from multiple time series data recorded in each of two or more brain areas arises in many neuroscientific investigations. For a single stationary time series in each of two brain areas statistical tools such as cross-correlation and Granger causality may be applied. On the other hand, to examine multivariate interactions at a single time point, canonical correlation, which finds the linear combinations of signals that maximize the correlation, may be used. We report here a new method that produces interpretations much like these standard techniques and, in addition, 1) extends the idea of canonical correlation to 3-way arrays (with dimensionality number of signals by number of time points by number of trials), 2) allows for nonstationarity, 3) also allows for nonlinearity, 4) scales well as the number of signals increases, and 5) captures predictive relationships, as is done with Granger causality. We demonstrate the effectiveness of the method through simulation studies and illustrate by analyzing local field potentials recorded from a behaving primate. NEW & NOTEWORTHY Multiple signals recorded from each of multiple brain regions may contain information about cross-region interactions. This article provides a method for visualizing the complicated interdependencies contained in these signals and assessing them statistically. The method combines signals optimally but allows the resulting measure of dependence to change, both within and between regions, as the responses evolve dynamically across time. We demonstrate the effectiveness of the method through numerical simulations and by uncovering a novel connectivity pattern between hippocampus and prefrontal cortex during a declarative memory task.
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http://dx.doi.org/10.1152/jn.00869.2017DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6230799PMC
October 2018

Compressed Timeline of Recent Experience in Monkey Lateral Prefrontal Cortex.

J Cogn Neurosci 2018 07 26;30(7):935-950. Epub 2018 Apr 26.

Boston University.

Cognitive theories suggest that working memory maintains not only the identity of recently presented stimuli but also a sense of the elapsed time since the stimuli were presented. Previous studies of the neural underpinnings of working memory have focused on sustained firing, which can account for maintenance of the stimulus identity, but not for representation of the elapsed time. We analyzed single-unit recordings from the lateral prefrontal cortex of macaque monkeys during performance of a delayed match-to-category task. Each sample stimulus triggered a consistent sequence of neurons, with each neuron in the sequence firing during a circumscribed period. These sequences of neurons encoded both stimulus identity and elapsed time. The encoding of elapsed time became less precise as the sample stimulus receded into the past. These findings suggest that working memory includes a compressed timeline of what happened when, consistent with long-standing cognitive theories of human memory.
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http://dx.doi.org/10.1162/jocn_a_01273DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7004225PMC
July 2018

Working Memory Load Modulates Neuronal Coupling.

Cereb Cortex 2019 04;29(4):1670-1681

The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.

There is a severe limitation in the number of items that can be held in working memory. However, the neurophysiological limits remain unknown. We asked whether the capacity limit might be explained by differences in neuronal coupling. We developed a theoretical model based on Predictive Coding and used it to analyze Cross Spectral Density data from the prefrontal cortex (PFC), frontal eye fields (FEF), and lateral intraparietal area (LIP). Monkeys performed a change detection task. The number of objects that had to be remembered (memory load) was varied (1-3 objects in the same visual hemifield). Changes in memory load changed the connectivity in the PFC-FEF-LIP network. Feedback (top-down) coupling broke down when the number of objects exceeded cognitive capacity. Thus, impaired behavioral performance coincided with a break-down of Prediction signals. This provides new insights into the neuronal underpinnings of cognitive capacity and how coupling in a distributed working memory network is affected by memory load.
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http://dx.doi.org/10.1093/cercor/bhy065DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302742PMC
April 2019

Different Levels of Category Abstraction by Different Dynamics in Different Prefrontal Areas.

Neuron 2018 02 27;97(3):716-726.e8. Epub 2018 Jan 27.

The Picower Institute for Learning & Memory and Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, 43 Vassar Street, Cambridge, MA 02139, USA. Electronic address:

Categories can be grouped by shared sensory attributes (i.e., cats) or a more abstract rule (i.e., animals). We explored the neural basis of abstraction by recording from multi-electrode arrays in prefrontal cortex (PFC) while monkeys performed a dot-pattern categorization task. Category abstraction was varied by the degree of exemplar distortion from the prototype pattern. Different dynamics in different PFC regions processed different levels of category abstraction. Bottom-up dynamics (stimulus-locked gamma power and spiking) in the ventral PFC processed more low-level abstractions, whereas top-down dynamics (beta power and beta spike-LFP coherence) in the dorsal PFC processed more high-level abstractions. Our results suggest a two-stage, rhythm-based model for abstracting categories.
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http://dx.doi.org/10.1016/j.neuron.2018.01.009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6091891PMC
February 2018

Gamma and beta bursts during working memory readout suggest roles in its volitional control.

Nat Commun 2018 01 26;9(1):394. Epub 2018 Jan 26.

The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, 43 Vassar Street, Cambridge, MA, 02139, USA.

Working memory (WM) activity is not as stationary or sustained as previously thought. There are brief bursts of gamma (~50-120 Hz) and beta (~20-35 Hz) oscillations, the former linked to stimulus information in spiking. We examined these dynamics in relation to readout and control mechanisms of WM. Monkeys held sequences of two objects in WM to match to subsequent sequences. Changes in beta and gamma bursting suggested their distinct roles. In anticipation of having to use an object for the match decision, there was an increase in gamma and spiking information about that object and reduced beta bursting. This readout signal was only seen before relevant test objects, and was related to premotor activity. When the objects were no longer needed, beta increased and gamma decreased together with object spiking information. Deviations from these dynamics predicted behavioral errors. Thus, beta could regulate gamma and the information in WM.
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http://dx.doi.org/10.1038/s41467-017-02791-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5785952PMC
January 2018

Laminar recordings in frontal cortex suggest distinct layers for maintenance and control of working memory.

Proc Natl Acad Sci U S A 2018 01 16;115(5):1117-1122. Epub 2018 Jan 16.

The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139;

All of the cerebral cortex has some degree of laminar organization. These different layers are composed of neurons with distinct connectivity patterns, embryonic origins, and molecular profiles. There are little data on the laminar specificity of cognitive functions in the frontal cortex, however. We recorded neuronal spiking/local field potentials (LFPs) using laminar probes in the frontal cortex (PMd, 8A, 8B, SMA/ACC, DLPFC, and VLPFC) of monkeys performing working memory (WM) tasks. LFP power in the gamma band (50-250 Hz) was strongest in superficial layers, and LFP power in the alpha/beta band (4-22 Hz) was strongest in deep layers. Memory delay activity, including spiking and stimulus-specific gamma bursting, was predominately in superficial layers. LFPs from superficial and deep layers were synchronized in the alpha/beta bands. This was primarily unidirectional, with alpha/beta bands in deep layers driving superficial layer activity. The phase of deep layer alpha/beta modulated superficial gamma bursting associated with WM encoding. Thus, alpha/beta rhythms in deep layers may regulate the superficial layer gamma bands and hence maintenance of the contents of WM.
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http://dx.doi.org/10.1073/pnas.1710323115DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5798320PMC
January 2018

Low-Beta Oscillations Turn Up the Gain During Category Judgments.

Cereb Cortex 2018 01;28(1):116-130

Department of Brain and Cognitive Sciences, The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Synchrony between local field potential (LFP) rhythms is thought to boost the signal of attended sensory inputs. Other cognitive functions could benefit from such gain control. One is categorization where decisions can be difficult if categories differ in subtle ways. Monkeys were trained to flexibly categorize smoothly varying morphed stimuli, using orthogonal boundaries to carve up the same stimulus space in 2 different ways. We found evidence for category-specific patterns of low-beta (16-20 Hz) synchrony in the lateral prefrontal cortex (PFC). This synchrony was stronger when a given category scheme was relevant. We also observed an overall increase in low-beta LFP synchrony for stimuli that were near the category boundary and thus more difficult to categorize. Beta category selectivity was evident in partial field-field coherence measurements, which measure local synchrony, but the boundary enhancement was not. Thus, it seemed that category selectivity relied on local interactions while boundary enhancement was a more global effect. The results suggest that beta synchrony helps form category ensembles and may reflect recruitment of additional cortical resources for categorizing challenging stimuli, thus serving as a form of gain control.
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http://dx.doi.org/10.1093/cercor/bhw356DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6248822PMC
January 2018

A Meta-Analysis Suggests Different Neural Correlates for Implicit and Explicit Learning.

Neuron 2017 Oct;96(2):521-534.e7

The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Electronic address:

A meta-analysis of non-human primates performing three different tasks (Object-Match, Category-Match, and Category-Saccade associations) revealed signatures of explicit and implicit learning. Performance improved equally following correct and error trials in the Match (explicit) tasks, but it improved more after correct trials in the Saccade (implicit) task, a signature of explicit versus implicit learning. Likewise, error-related negativity, a marker for error processing, was greater in the Match (explicit) tasks. All tasks showed an increase in alpha/beta (10-30 Hz) synchrony after correct choices. However, only the implicit task showed an increase in theta (3-7 Hz) synchrony after correct choices that decreased with learning. In contrast, in the explicit tasks, alpha/beta synchrony increased with learning and decreased thereafter. Our results suggest that explicit versus implicit learning engages different neural mechanisms that rely on different patterns of oscillatory synchrony.
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http://dx.doi.org/10.1016/j.neuron.2017.09.032DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5662212PMC
October 2017

Hebbian Learning in a Random Network Captures Selectivity Properties of the Prefrontal Cortex.

J Neurosci 2017 11 6;37(45):11021-11036. Epub 2017 Oct 6.

Center for Theoretical Neuroscience, College of Physicians and Surgeons,

Complex cognitive behaviors, such as context-switching and rule-following, are thought to be supported by the prefrontal cortex (PFC). Neural activity in the PFC must thus be specialized to specific tasks while retaining flexibility. Nonlinear "mixed" selectivity is an important neurophysiological trait for enabling complex and context-dependent behaviors. Here we investigate (1) the extent to which the PFC exhibits computationally relevant properties, such as mixed selectivity, and (2) how such properties could arise via circuit mechanisms. We show that PFC cells recorded from male and female rhesus macaques during a complex task show a moderate level of specialization and structure that is not replicated by a model wherein cells receive random feedforward inputs. While random connectivity can be effective at generating mixed selectivity, the data show significantly more mixed selectivity than predicted by a model with otherwise matched parameters. A simple Hebbian learning rule applied to the random connectivity, however, increases mixed selectivity and enables the model to match the data more accurately. To explain how learning achieves this, we provide analysis along with a clear geometric interpretation of the impact of learning on selectivity. After learning, the model also matches the data on measures of noise, response density, clustering, and the distribution of selectivities. Of two styles of Hebbian learning tested, the simpler and more biologically plausible option better matches the data. These modeling results provide clues about how neural properties important for cognition can arise in a circuit and make clear experimental predictions regarding how various measures of selectivity would evolve during animal training. The prefrontal cortex is a brain region believed to support the ability of animals to engage in complex behavior. How neurons in this area respond to stimuli-and in particular, to combinations of stimuli ("mixed selectivity")-is a topic of interest. Even though models with random feedforward connectivity are capable of creating computationally relevant mixed selectivity, such a model does not match the levels of mixed selectivity seen in the data analyzed in this study. Adding simple Hebbian learning to the model increases mixed selectivity to the correct level and makes the model match the data on several other relevant measures. This study thus offers predictions on how mixed selectivity and other properties evolve with training.
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http://dx.doi.org/10.1523/JNEUROSCI.1222-17.2017DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5678026PMC
November 2017