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Neuromodulators affect cognition and behavior via their actions on brain wide neural networks. The locus coeruleus norepinephrine (LC-NE) system is one of the brain’s major neuromodulatory systems. Aging and neurological diseases lead to changes in brain structure that impact the normal functioning of brain networks. Although the precise nature of this impact and how to diagnose it remain a challenge, recent studies have started to reveal details about neuromodulation of neural networks and behavior. I will review what we know about networks that perform computations driving perception and action and how these networks are affected by the LC-NE system. I will examine aging and Alzheimer’s disease related changes in these networks and in the LC-NE system and the resulting effects on perception and action. I will discuss how relationships between activation of this system and changes in pupil size, recently explored with precision in humans and in animal models, provide a basis for the use of pupillometry as a non-invasive assay of age- and disease-related decline in neuromodulatory function.

Extremely fast pattern recognition capabilities are necessary to find and fit billions of tracks at the hardware trigger level produced every second anticipated at high-luminosity Large Hadron Collider (HL-LHC) running conditions. Associative memory (AM)-based approaches for fast pattern recognition have been proposed as a potential solution to the tracking trigger. However, at the HL-LHC, there is much less time available, and the speed performance must be improved over previous systems while maintaining a comparable number of patterns. The vertically integrated pattern recognition AM (VIPRAM) project aims to achieve the target pattern density and performance goal using 3DIC technology. The first step taken in the VIPRAM work was the development of a 2-D prototype (protoVIPRAM00) in which the AM building blocks were designed to be compatible with the 3-D integration. In this article, we present the results from extensive performance studies of the protoVIPRAM00chip in both realistic HL-LHC and extreme conditions. Results indicate that the chip operates at the design frequency of 100 MHz with perfect correctness in realistic conditions and conclude that the building blocks are ready for 3-D stacking. We also present performance boundary characterization of the chip under extreme conditions. Extremely fast pattern recognition capabilities are necessary to find and fit billions of tracks at the hardware trigger level produced every second anticipated at high luminosity LHC (HL-LHC) running conditions. Associative Memory (AM) based approaches for fast pattern recognition have been proposed as a potential solution to the tracking trigger. However, at the HL-LHC, there is much less time available and speed performance must be improved over previous systems while maintaining a comparable number of patterns. The Vertically Integrated Pattern Recognition Associative Memory (VIPRAM) Project aims to achieve the target pattern density and performance goal using 3DIC technology. The first step taken in the VIPRAM work was the development of a 2D prototype (protoVIPRAM00) in which the associative memory building blocks were designed to be compatible with the 3D integration. In this paper, we present the results from extensive performance studies of the protoVIPRAM00 chip in both realistic HL-LHC and extreme conditions. Results indicate that the chip operates at the design frequency of 100 MHz with perfect correctness in realistic conditions and conclude that the building blocks are ready for 3D stacking. We also present performance boundary characterization of the chip under extreme conditions.

Cognitively driven pupil modulations reflect certain underlying brain functions. What do these reflections tell us? We review findings that have identified key roles for three neural systems: First, cortical modulation of the pretectal olivary nucleus (PON), which controls the pupillary light reflex; second, the superior colliculus (SC), which mediates orienting responses, including pupil changes to salient stimuli; and third, the locus coeruleus (LC)-norepinephrine (NE) neuromodulatory system, which mediates relationships between pupil-linked arousal and cognition. We discuss how these findings can inform the interpretation of pupil measurements in terms of activation of these neural systems. We also highlight caveats, open questions, and key directions for future experiments for improving these interpretations in terms of the underlying neural dynamics throughout the brain.

In this article, a low-power, high-speed arbitration tree for pixel detector readout is presented. The synchronized, binary tree priority encoder establishes a position-dependent priority list at the start of every time frame. Pixels that indicate the presence of data for readout are sequentially granted access to a shared bus for data transfer to the periphery, without the use of an additional global strobe signal. It can be used for either full frame imaging or zero-suppressed readout, in which case it can simultaneously generate the pixel address. To increase the readout frame rate, the pixel array is subdivided into two halves, which allow interleaved latching of data at the output serializer. The design was implemented in a 65-nm LP-CMOS process for the readout of a $64\times64$ pixel array. Measurement results demonstrate a deadtimeless, full frame imaging rate of ~50 kfps, achieved with a dedicated output for every ( $32\times32$ ) 1024 pixels and for a pixel data packet of 11 bits, with no bit errors detected over 1000 frames. The measured energy per bit is 0.94 pJ.

Summary Transient variations in pupil size (PS) under constant luminance are coupled to rapid changes in arousal state,1, 2, 3 which have been interpreted as vigilance,4 salience,5 or a surprise signal.6, 7, 8 Neural control of such fluctuations presumably involves multiple brain regions5,9, 10, 11 and neuromodulatory systems,3,12,13 but it is often associated with phasic activity of the noradrenergic system.9,12,14,15 Serotonin (5-HT), a neuromodulator also implicated in aspects of arousal16 such as sleep-wake transitions,17 motivational state regulation,18 and signaling of unexpected events,19 seems to affect PS,20, 21, 22, 23, 24 but these effects have not been investigated in detail. Here we show that phasic 5-HT neuron stimulation causes transient PS changes. We used optogenetic activation of 5-HT neurons in the dorsal raphe nucleus (DRN) of head-fixed mice performing a foraging task. 5-HT-driven modulations of PS were maintained throughout the photostimulation period and sustained for a few seconds after the end of stimulation. We found no evidence that the increase in PS with activation of 5-HT neurons resulted from interactions of photostimulation with behavioral variables, such as locomotion or licking. Furthermore, we observed that the effect of 5-HT on PS depended on the level of environmental uncertainty, consistent with the idea that 5-HT could report a surprise signal.19 These results advance our understanding of the neuromodulatory control of PS, revealing a tight relationship between phasic activation of 5-HT neurons and changes in PS., Graphical Abstract, Highlights • Phasic optogenetic activation of DRN 5-HT neurons increases pupil size • The effects of 5-HT neuron activation are not specific to behavioral states • The effects of 5-HT on pupil size are linked to the level of uncertainty, The effects of serotonin (5-HT) on pupil size have not been investigated in detail. Cazettes et al. show that phasic optogenetic activation of 5-HT neurons causes pupil size changes in mice performing a foraging task. The 5-HT effects on pupil size are not specific to behavioral variables but appear to be linked to the task statistics.

Ascending neuromodulatory projections from the locus coeruleus (LC) affect cortical neural networks via the release of norepinephrine (NE). However, the exact nature of these neuromodulatory effects on neural activity patterns in vivo is not well understood. Here we show that in awake monkeys, LC activation is associated with changes in coordinated activity patterns in the anterior cingulate cortex (ACC). These relationships, which are largely independent of changes in firing rates of individual ACC neurons, depend on the type of LC activation: ACC pairwise correlations tend to be reduced when ongoing (baseline) LC activity increases but enhanced when external events evoke transient LC responses. Both relationships covary with pupil changes that reflect LC activation and arousal. These results suggest that modulations of information processing that reflect changes in coordinated activity patterns in cortical networks can result partly from ongoing, context-dependent, arousal-related changes in activation of the LC-NE system.

Ascending neuromodulatory projections from the locus coeruleus (LC) affect cortical neural networks via the release of norepinephrine (NE). However, the exact nature of these modulatory effects on neural activity patterns in vivo is not well understood. Here we show that in awake monkeys, LC activation is associated with changes in coordinated activity patterns in the anterior cingulate cortex (ACC). These effects, which are largely independent of changes in firing rates of individual ACC neurons, depend on the type of LC activation: ACC pairwise correlations are reduced when tonic (baseline) LC activity increases but are enhanced when external events drive phasic LC responses. Both effects also covary with pupil changes that reflect LC activation and arousal. These results suggest that modulations of information processing that reflect changes in coordinated activity patterns in cortical networks can, at least in part, result from ongoing, context-dependent, arousal-related changes in activation of the LC-NE system.

In this work, we analyze and model the most significant parameters for the power consumption of a content addressable memory (CAM) based Pattern Recognition Associative Memory (PRAM) chip. Content Addressable Memories (CAMs) provide fast pattern search which is useful in a variety of applications. Their high speed comes from their parallel operation, which also results in high power consumption. In this study, we focus on their potential use in high energy particle physics experiments like at the LHC, to find and fit the tracks expected at the hardware trigger level. We find that common CAM blocks used in other applications like IP lookup are not fit for track fitting in high energy particle physics experiments due to the immensely different workload requirements. We identify these workload differences, which guide the design of the CAM blocks for specific workloads like tracking triggers. We show with tests on our prototype chip the high dependence of associative memory power consumption with the workload. We observe that the power consumption in the matchlines is highly dependent on the hit-rate of the workload; whereas the searchline power is influenced by the average Hamming distance between successive input data and is independent of the hit-rate. We also see that the dominant power consumption of associative memories, when used for tracking trigger applications, is in the searchlines instead of the matchlines. We present a simple power modeling strategy for these associative memories along with a description of the expected workload at the hardware trigger level of LHC, to guide future research on pattern recognition associative memory chips.

This paper reports on a content addressable memory (CAM) employing a multi-Vdd scheme for low power pattern recognition applications. The complete design, simulation and testing of the chip is presented along with an exploration of the multi-Vdd design space. The proposed design, operating at an optimal operating point in a triple-Vdd configuration, increases the delay range by 2.4 times and consumes 25.3% less power when compared to a conventional single-Vdd design operating over the same voltage range. Measurement results from a 246 kb test chip fabricated in 130nm Global Foundries Low Power CMOS technology are presented to validate the model and analysis.

Neurons in the brainstem nucleus locus ceruleus (LC) often exhibit phasic activation in the context of simple sensory-motor tasks. The functional role of this activation, which leads to the release of norepinephrine throughout the brain, is not yet understood in part because the conditions under which it occurs remain in question. Early studies focused on the relationship of LC phasic activation to salient sensory events, whereas more recent work has emphasized its timing relative to goal-directed behavioral responses, possibly representing the end of a sensory-motor decision process. To better understand the relationship between LC phasic activation and sensory, motor, and decision processing, we recorded spiking activity of neurons in the LC+ (LC and the adjacent, norepinephrine-containing subceruleus nucleus) of monkeys performing a countermanding task. The task required the monkeys to occasionally withhold planned, saccadic eye movements to a visual target. We found that many well isolated LC+ units responded to both the onset of the visual cue instructing the monkey to initiate the saccade and again after saccade onset, even when it was initiated erroneously in the presence of a stop signal. Many of these neurons did not respond to saccades made outside of the task context. In contrast, neither the appearance of the stop signal nor the successful withholding of the saccade elicited an LC+ response. Therefore, LC+ phasic activation encodes sensory and motor events related to decisions to execute, but not withhold, movements, implying a functional role in goal-directed actions, but not necessarily more covert forms of processing.

Neurons in primary visual cortex, V1, very often have extraclassical receptive fields (eCRFs). The eCRF is defined as the region of visual space where stimuli cannot elicit a spiking response but can modulate the response of a stimulus in the classical receptive field (CRF). We investigated the dependence of the eCRF on stimulus contrast and orientation in macaque V1 cells for which the laminar location was determined. The eCRF was more sensitive to contrast than the CRF across the whole population of V1 cells with the greatest contrast differential in layer 2/3. We confirmed that many V1 cells experience stronger suppression for collinear than orthogonal stimuli in the eCRF. Laminar analysis revealed that the predominant bias for collinear suppression was found in layers 2/3 and 4b. The laminar pattern of contrast and orientation dependence suggests that eCRF suppression may derive from different neural circuits in different layers, and may be comprised of two distinct components: orientation-tuned and untuned suppression. On average tuned suppression was delayed by ∼25 ms compared with the onset of untuned suppression. Therefore, response modulation by the eCRF develops dynamically and rapidly in time.

In HEP tracking trigger applications, flagging an individual detector hit is not important. Rather, the path of a charged particle through many detector layers is what must be found. Moreover, given the increased luminosity projected for future LHC experiments, this type of track finding will be required within the Level 1 Trigger system. This means that future LHC experiments require not just a chip capable of high-speed track finding but also one with a high-speed readout architecture. VIPRAML1CMS is 2-Tier Vertically Integrated chip designed to fulfill these requirements. It is a complete pipelined Pattern Recognition Associative Memory (PRAM) architecture including pattern recognition, result sparsification, and readout for Level 1 trigger applications in CMS with 15-bit wide detector addresses and eight detector layers included in the track finding. Pattern recognition is based on classic Content Addressable Memories with a Current Race Scheme to reduce timing complexity and a 4-bit Selective Precharge to minimize power consumption. VIPRAM_L1CMS uses a pipelined set of priority-encoded binary readout structures to sparsify and readout active road flags at frequencies of at least 100MHz. VIPRAM_L1CMS is designed to work directly with the Pulsar2b Architecture.

A large body of work spanning 25+ years provides compelling evidence for the involvement of the basal ganglia-superior colliculus pathway in the initiation of rapid, orienting movements of the eyes, called saccades. The role of this pathway in saccade control is similar to the role of the basal ganglia-thalamic pathway in the control of skeletal movement: a transient cessation in tonic inhibition supplied by the basal ganglia to motor structures releases movements via the direct pathway whereas a transient increase in inhibition by the basal ganglia to motor structures prevents movements via the indirect pathway. In parallel with recent advances in the study and treatment of patients with basal ganglia disease and in animal experiments in the skeletal motor system, the results of studies exploring the role of the basal ganglia-superior colliculus pathway in saccades highlight the need for a revisiting of our understanding of the role of this pathway in saccades. The discovery of many different response profiles of neurons in the substantia nigra pars reticulata of the basal ganglia and in the superior colliculus, coupled with advances in experimental and statistical techniques including sophisticated behavioral procedures and multiple neuron recording and analysis, point toward a role for the basal ganglia-superior colliculus pathway in cognitive events intervening between vision and action, such as memory, target selection and saccade choice and valuation.

Content Addressable Memories (CAM) have become increasingly more important in applications requiring high speed memory search due to their inherent massively parallel processing architecture. We present a complete power analysis methodology for CAM systems to aid the exploration of their power-performance trade-offs in future systems. Our proposed methodology uses detailed transistor level circuit simulation of power behavior and a handful of input data types to simulate full chip power consumption. Furthermore, we applied our power analysis methodology on a custom designed associative memory test chip. This chip was developed by Fermilab for the purpose of developing high performance real-time pattern recognition on high volume data produced by a future large-scale scientific experiment. We applied our methodology to configure a power model for this test chip. Our model is capable of predicting the total average power within 4% of actual power measurements. Our power analysis methodology can be generalized and applied to other CAM-like memory systems and accurately characterize their power behavior.

We describe a method that uses a modified version of juxtacellular labeling [Pinault D. A novel single-cell staining procedure performed in vivo under electrophysiological control: morpho-functional features of juxtacellularly labeled thalamic cells and other central neurons with biocytin or neurobiotin. J Neurosci Meth 1996;65:113-36], which allows us to functionally characterize and subsequently label single neurons in vivo in macaque V1. The method is generally applicable in acute in vivo preparations. Extracellular recording is made with a patch electrode when the electrode is attached to the cell membrane. Initially a 'blind' search method is used as a guide to obtaining a cell attached configuration that we refer to as a loose-patch (LP). The neuron's receptive field properties are functionally characterized, the neuron is labeled and then characterization is confirmed, all in the LP configuration. There are a number of advantages of the method that we describe over other methods. First, we have found that we can obtain stable extracellular recordings for periods of hours that enable us to make a relatively comprehensive visual functional characterization of a neuron's receptive field properties. Second, because the electrode is closely apposed to the cell we obtain excellent isolation of the extracellular spike. Third, the method provides labeling that gives complete dendritic and axonal filling that survives over a number of days, which is an important feature in acute primate experiments. Fourth, the in vivo method of labeling and reconstructing neurons gives complete three-dimensional structure of the neuron including its intra-cortical axonal arbor. These features overcome known limits of the established methods of studying neuronal morphology including the Golgi stain (limited when adult tissue is used) and in vitro whole cell methods (incomplete axonal filling due to limited slice thickness). They also overcome the known limits of the established method of combined function-morphology studies i.e. intracellular recording in vivo. The modified juxtacellular method provides a reliable alternative to the difficult method of characterization by extracellular recording and subsequent intracellular labeling [Anderson JC, Martin KAC, Whitteridge D. Form, function and intracortical projections of neurons in the striate cortex of the monkey Macacus nemestrinus. Cerebral Cortex 1993;3:412-20]. We show the method can be used to record at a range of depths through V1 cortex allowing for sampling of neurons in the different layers and functional subpopulations. Links can then be made with existing knowledge about the anatomical organization of V1, the various morphological classes of neurons found therein, their functional connectivity and visual response properties.

An associative memory-based track finding approach has been proposed for a Level 1 tracking trigger to cope with increasing luminosities at the LHC. The associative memory uses a massively parallel architecture to tackle the intrinsically complex combinatorics of track finding algorithms, thus avoiding the typical power law dependence of execution time on occupancy and solving the pattern recognition in times roughly proportional to the number of hits. This is of crucial importance given the large occupancies typical of hadronic collisions. The design of an associative memory system capable of dealing with the complexity of HL-LHC collisions and with the short latency required by Level 1 triggering poses significant, as yet unsolved, technical challenges. For this reason, an aggressive R&D program has been launched at Fermilab to advance state of-the-art associative memory technology, the so called VIPRAM (Vertically Integrated Pattern Recognition Associative Memory) project. The VIPRAM leverages emerging 3D vertical integration technology to build faster and denser Associative Memory devices. The first step is to implement in conventional VLSI the associative memory building blocks that can be used in 3D stacking, in other words, the building blocks are laid out as if it is a 3D design. In this paper, wemore » report on the first successful implementation of a 2D VIPRAM demonstrator chip (protoVIPRAM00). The results show that these building blocks are ready for 3D stacking.« less