Opponent learning with different representations in the cortico-basal ganglia pathways can develop obsession-compulsion cycle.

Obsessive-compulsive disorder (OCD) has been suggested to be associated with impairment of model-based behavioral control. Meanwhile, recent work suggested shorter memory trace for negative than positive prediction errors (PEs) in OCD. We explored relations between these two suggestions through computational modeling. Based on the properties of cortico-basal ganglia pathways, we modeled human as an agent having a combination of successor representation (SR)-based system that enables model-based-like control and individual representation (IR)-based system that only hosts model-free control, with the two systems potentially learning from positive and negative PEs in different rates. We simulated the agent's behavior in the environmental model used in the recent work that describes potential development of obsession-compulsion cycle. We found that the dual-system agent could develop enhanced obsession-compulsion cycle, similarly to the agent having memory trace imbalance in the recent work, if the SR- and IR-based systems learned mainly from positive and negative PEs, respectively. We then simulated the behavior of such an opponent SR+IR agent in the two-stage decision task, in comparison with the agent having only SR-based control. Fitting of the agents' behavior by the model weighing model-based and model-free control developed in the original two-stage task study resulted in smaller weights of model-based control for the opponent SR+IR agent than for the SR-only agent. These results reconcile the previous suggestions about OCD, i.e., impaired model-based control and memory trace imbalance, raising a novel possibility that opponent learning in model(SR)-based and model-free controllers underlies obsession-compulsion. Our model cannot explain the behavior of OCD patients in punishment, rather than reward, contexts, but it could be resolved if opponent SR+IR learning operates also in the recently revealed non-canonical cortico-basal ganglia-dopamine circuit for threat/aversiveness, rather than reward, reinforcement learning, and the aversive SR + appetitive IR agent could actually develop obsession-compulsion if the environment is modeled differently.

Corticocortical innervation subtypes of layer 5 intratelencephalic cells in the murine secondary motor cortex.

Feedback projections from the secondary motor cortex (M2) to the primary motor and sensory cortices are essential for behavior selection and sensory perception. Intratelencephalic (IT) cells in layer 5 (L5) contribute feedback projections to diverse cortical areas. Here we show that L5 IT cells participating in feedback connections to layer 1 (L1) exhibit distinct projection patterns, genetic profiles, and electrophysiological properties relative to other L5 IT cells. An analysis of the MouseLight database found that L5 IT cells preferentially targeting L1 project broadly to more cortical regions, including the perirhinal and auditory cortices, and innervate a larger volume of striatum than the other L5 IT cells. We found experimentally that in upper L5 (L5a), ER81 (ETV1) was found more often in L1-preferring IT cells, and in IT cells projecting to perirhinal/auditory regions than those projecting to primary motor or somatosensory regions. The perirhinal region-projecting L5a IT cells were synaptically connected to each other and displayed lower input resistance than contra-M2 projecting IT cells including L1-preferring and nonpreferring cells. Our findings suggest that M2-L5a IT L1-preferring cells exhibit stronger ER81 expression and broader cortical/striatal projection fields than do cells that do not preferentially target L1.

Rigid reduced successor representation as a potential mechanism for addiction.

Difficulty in cessation of drinking, smoking, or gambling has been widely recognized. Conventional theories proposed relative dominance of habitual over goal-directed control, but human studies have not convincingly supported them. Referring to the recently suggested "successor representation (SR)" of states that enables partially goal-directed control, we propose a dopamine-related mechanism that makes resistance to habitual reward-obtaining particularly difficult. We considered that long-standing behavior towards a certain reward without resisting temptation can (but not always) lead to a formation of rigid dimension-reduced SR based on the goal state, which cannot be updated. Then, in our model assuming such rigid reduced SR, whereas no reward prediction error (RPE) is generated at the goal while no resistance is made, a sustained large positive RPE is generated upon goal reaching once the person starts resisting temptation. Such sustained RPE is somewhat similar to the hypothesized sustained fictitious RPE caused by drug-induced dopamine. In contrast, if rigid reduced SR is not formed and states are represented individually as in simple reinforcement learning models, no sustained RPE is generated at the goal. Formation of rigid reduced SR also attenuates the resistance-dependent decrease in the value of the cue for behavior, makes subsequent introduction of punishment after the goal ineffective, and potentially enhances the propensity of nonresistance through the influence of RPEs via the spiral striatum-midbrain circuit. These results suggest that formation of rigid reduced SR makes cessation of habitual reward-obtaining particularly difficult and can thus be a mechanism for addiction, common to substance and nonsubstance reward.

K

The two-particle momentum correlation function of a K^{-}p pair from high-energy nuclear collisions is evaluated in the K[over ¯]N-πΣ-πΛ coupled-channel framework. The effects of all coupled channels together with the Coulomb potential and the threshold energy difference between K^{-}p and K[over ¯]^{0}n are treated completely for the first time. Realistic potentials based on the chiral SU(3) dynamics are used which fit the available scattering data. The recently measured correlation function is found to be well reproduced by allowing variations of the source size and the relative weight of the source function of πΣ with respect to that of K[over ¯]N. The predicted K^{-}p correlation function from larger systems indicates that the investigation of its source size dependence is useful in providing further constraints in the study of the K[over ¯]N interaction.

Neural Mechanisms for Adaptive Learned Avoidance of Mental Effort.

Humans tend to avoid mental effort. Previous studies have demonstrated this tendency using various demand-selection tasks; participants generally avoid options associated with higher cognitive demand. However, it remains unclear whether humans avoid mental effort adaptively in uncertain and nonstationary environments. If so, it also remains unclear what neural mechanisms underlie such learned avoidance and whether they remain the same regardless of cognitive-demand types. We addressed these issues by developing novel demand-selection tasks where associations between choice options and cognitive-demand levels change over time, with two variations using mental arithmetic and spatial reasoning problems (males/females: 29:4 and 18:2). Most participants showed avoidance, and their choices depended on the demand experienced on multiple preceding trials. We assumed that participants updated the expected cost of mental effort through experience, and fitted their choices by reinforcement learning models, comparing several possibilities. Model-based fMRI analyses revealed that activity in the dorsomedial and lateral frontal cortices was positively correlated with the trial-by-trial expected cost for the chosen option commonly across the different types of cognitive demand. Analyses also revealed a trend of negative correlation in the ventromedial prefrontal cortex. We further identified correlates of cost-prediction error at time of problem presentation or answering the problem, the latter of which partially overlapped with or were proximal to the correlates of expected cost at time of choice cue in the dorsomedial frontal cortex. These results suggest that humans adaptively learn to avoid mental effort, having neural mechanisms to represent expected cost and cost-prediction error, and the same mechanisms operate for various types of cognitive demand.SIGNIFICANCE STATEMENT In daily life, humans encounter various cognitive demands and tend to avoid high-demand options. However, it remains unclear whether humans avoid mental effort adaptively under dynamically changing environments. If so, it also remains unclear what the underlying neural mechanisms are and whether they operate regardless of cognitive-demand types. To address these issues, we developed novel tasks where participants could learn to avoid high-demand options under uncertain and nonstationary environments. Through model-based fMRI analyses, we found regions whose activity was correlated with the expected mental effort cost, or cost-prediction error, regardless of demand type. These regions overlap, or are adjacent with each other, in the dorsomedial frontal cortex. This finding helps clarify the mechanisms for cognitive-demand avoidance, and provides empirical building blocks for the emerging computational theory of mental effort.

Dynamical state transitions into addictive behaviour and their early-warning signals.

The theory of critical transitions in complex systems (ecosystems, climate, etc.), and especially its ability to predict abrupt changes by early-warning signals based on analysis of fluctuations close to tipping points, is seen as a promising avenue to study disease dynamics. However, the biomedical field still lacks a clear demonstration of this concept. Here, we used a well-established animal model in which initial alcohol exposure followed by deprivation and subsequent reintroduction of alcohol induces excessive alcohol drinking as an example of disease onset. Intensive longitudinal data (ILD) of rat drinking behaviour and locomotor activity were acquired by a fully automated drinkometer device over 14 weeks. Dynamical characteristics of ILD were extracted using a multi-scale computational approach. Our analysis shows a transition into addictive behaviour preceded by early-warning signals such as instability of drinking patterns and locomotor circadian rhythms, and a resultant increase in low frequency, ultradian rhythms during the first week of deprivation. We find evidence that during prolonged deprivation, a critical transition takes place pushing the system to excessive alcohol consumption. This study provides an adaptable framework for processing ILD from clinical studies and for examining disease dynamics and early-warning signals in the biomedical field.

Forgetting in Reinforcement Learning Links Sustained Dopamine Signals to Motivation.

It has been suggested that dopamine (DA) represents reward-prediction-error (RPE) defined in reinforcement learning and therefore DA responds to unpredicted but not predicted reward. However, recent studies have found DA response sustained towards predictable reward in tasks involving self-paced behavior, and suggested that this response represents a motivational signal. We have previously shown that RPE can sustain if there is decay/forgetting of learned-values, which can be implemented as decay of synaptic strengths storing learned-values. This account, however, did not explain the suggested link between tonic/sustained DA and motivation. In the present work, we explored the motivational effects of the value-decay in self-paced approach behavior, modeled as a series of 'Go' or 'No-Go' selections towards a goal. Through simulations, we found that the value-decay can enhance motivation, specifically, facilitate fast goal-reaching, albeit counterintuitively. Mathematical analyses revealed that underlying potential mechanisms are twofold: (1) decay-induced sustained RPE creates a gradient of 'Go' values towards a goal, and (2) value-contrasts between 'Go' and 'No-Go' are generated because while chosen values are continually updated, unchosen values simply decay. Our model provides potential explanations for the key experimental findings that suggest DA's roles in motivation: (i) slowdown of behavior by post-training blockade of DA signaling, (ii) observations that DA blockade severely impairs effortful actions to obtain rewards while largely sparing seeking of easily obtainable rewards, and (iii) relationships between the reward amount, the level of motivation reflected in the speed of behavior, and the average level of DA. These results indicate that reinforcement learning with value-decay, or forgetting, provides a parsimonious mechanistic account for the DA's roles in value-learning and motivation. Our results also suggest that when biological systems for value-learning are active even though learning has apparently converged, the systems might be in a state of dynamic equilibrium, where learning and forgetting are balanced.

Computing reward-prediction error: an integrated account of cortical timing and basal-ganglia pathways for appetitive and aversive learning.

There are two prevailing notions regarding the involvement of the corticobasal ganglia system in value-based learning: (i) the direct and indirect pathways of the basal ganglia are crucial for appetitive and aversive learning, respectively, and (ii) the activity of midbrain dopamine neurons represents reward-prediction error. Although (ii) constitutes a critical assumption of (i), it remains elusive how (ii) holds given (i), with the basal-ganglia influence on the dopamine neurons. Here we present a computational neural-circuit model that potentially resolves this issue. Based on the latest analyses of the heterogeneous corticostriatal neurons and connections, our model posits that the direct and indirect pathways, respectively, represent the values of upcoming and previous actions, and up-regulate and down-regulate the dopamine neurons via the basal-ganglia output nuclei. This explains how the difference between the upcoming and previous values, which constitutes the core of reward-prediction error, is calculated. Simultaneously, it predicts that blockade of the direct/indirect pathway causes a negative/positive shift of reward-prediction error and thereby impairs learning from positive/negative error, i.e. appetitive/aversive learning. Through simulation of reward-reversal learning and punishment-avoidance learning, we show that our model could indeed account for the experimentally observed features that are suggested to support notion (i) and could also provide predictions on neural activity. We also present a behavioral prediction of our model, through simulation of inter-temporal choice, on how the balance between the two pathways relates to the subject's time preference. These results indicate that our model, incorporating the heterogeneity of the cortical influence on the basal ganglia, is expected to provide a closed-circuit mechanistic understanding of appetitive/aversive learning.

Dopaminergic control of motivation and reinforcement learning: a closed-circuit account for reward-oriented behavior.

Humans and animals take actions quickly when they expect that the actions lead to reward, reflecting their motivation. Injection of dopamine receptor antagonists into the striatum has been shown to slow such reward-seeking behavior, suggesting that dopamine is involved in the control of motivational processes. Meanwhile, neurophysiological studies have revealed that phasic response of dopamine neurons appears to represent reward prediction error, indicating that dopamine plays central roles in reinforcement learning. However, previous attempts to elucidate the mechanisms of these dopaminergic controls have not fully explained how the motivational and learning aspects are related and whether they can be understood by the way the activity of dopamine neurons itself is controlled by their upstream circuitries. To address this issue, we constructed a closed-circuit model of the corticobasal ganglia system based on recent findings regarding intracortical and corticostriatal circuit architectures. Simulations show that the model could reproduce the observed distinct motivational effects of D1- and D2-type dopamine receptor antagonists. Simultaneously, our model successfully explains the dopaminergic representation of reward prediction error as observed in behaving animals during learning tasks and could also explain distinct choice biases induced by optogenetic stimulation of the D1 and D2 receptor-expressing striatal neurons. These results indicate that the suggested roles of dopamine in motivational control and reinforcement learning can be understood in a unified manner through a notion that the indirect pathway of the basal ganglia represents the value of states/actions at a previous time point, an empirically driven key assumption of our model.

Differential cortical activation of the striatal direct and indirect pathway cells: reconciling the anatomical and optogenetic results by using a computational method.

The corticostriatal system is considered to be crucially involved in learning and action selection. Anatomical studies have shown that two types of corticostriatal neurons, intratelencephalic (IT) and pyramidal tract (PT) cells, preferentially project to dopamine D1 or D2 receptor-expressing striatal projection neurons, respectively. In contrast, an optogenetic study has shown that stimulation of IT axons evokes comparable responses in D1 and D2 cells and that stimulation of PT axons evokes larger responses in D1 cells. Since the optogenetic study applied brief stimulation only, however, the overall impacts of repetitive inputs remain unclear. Moreover, the apparent contradiction between the anatomical and optogenetic results remains to be resolved. I addressed these issues by using a computational approach. Specifically, I constructed a model of striatal response to cortical inputs, with parameters regarding short-term synaptic plasticity and anatomical connection strength for each connection type. Under the constraint of the optogenetic results, I then explored the parameters that best explain the previously reported paired-pulse ratio of response in D1 and D2 cells to cortical and intrastriatal stimulations, which presumably recruit different compositions of IT and PT fibers. The results indicate that 1) IT→D1 and PT→D2 connections are anatomically stronger than IT→D2 and PT→D1 connections, respectively, consistent with the previous findings, and that 2) IT→D1 and PT→D2 synapses entail short-term facilitation, whereas IT→D2 and PT→D1 synapses would basically show depression, and thereby 3) repetitive IT or PT inputs have larger overall impacts on D1 or D2 cells, respectively, supporting a recently proposed hypothesis on the roles of corticostriatal circuits in reinforcement learning.

QCD sum rules for magnetically induced mixing between ηc and J/ψ.

We investigate the properties of charmonia in strong magnetic fields by using QCD sum rules. We show how to implement the mixing effects between η(c) and J/ψ on the basis of field-theoretical approaches, and then show that the sum rules are saturated by the mixing effects with phenomenologically determined parameters. Consequently, we find that the mixing effects are the dominant contribution to the mass shifts of the static charmonia in strong magnetic fields.

Intraindividual Fluctuation in Optimism Under Daily Life Circumstances: A Longitudinal Study.

Optimism is typically conceptualized as a relatively static tendency regarding positive expectations about one's future. However, recent studies suggest that optimism may meaningfully fluctuate within individuals over time. To date, little is known about the characteristics of such state optimism and potential cultural difference in state optimism. Accordingly, we developed a Japanese version of the State Optimism Measure (J-SOM) and examined its validity and the nature of intraindividual state optimism fluctuations; we also examined relationships between the J-SOM and other measures of mental health, including trait optimism. We conducted two online longitudinal surveys with different time intervals (weekly, n = 97; monthly, n = 99) targeting university students. Results were largely consistent between the two surveys. We confirmed high factor validity and internal consistency of the J-SOM. The J-SOM showed significant correlations in expected directions with other measures such as depressive mood and subjective happiness. In addition, intraindividual changes in the J-SOM were associated with changes in mood and quality of daily life. Importantly, these associations between intraindividual change in optimism and in other variables were minimal for trait optimism. We also found that state optimism, compared with trait optimism, tended to show larger intraindividual changes over 1, 2, 3, 4, and 8 weeks. In summary, this study developed a translated version of the SOM and validated it, and then showed, for the first time, that state optimism can fluctuate within individuals in daily life over a span of several weeks. Supplementary Information: The online version contains supplementary material available at 10.1007/s42761-023-00224-y.

Control of layer 5 pyramidal cell spiking by oscillatory inhibition in the distal apical dendrites: a computational modeling study.

The distal apical dendrites of layer 5 pyramidal neurons receive cortico-cortical and thalamocortical top-down and feedback inputs, as well as local recurrent inputs. A prominent source of recurrent inhibition in the neocortical circuit is somatostatin-positive Martinotti cells, which preferentially target distal apical dendrites of pyramidal cells. These electrically coupled cells can fire synchronously at various frequencies, including over a relatively slow range (5∼30 Hz), thereby imposing oscillatory inhibition on the pyramidal apical tuft dendrites. We examined how such distal oscillatory inhibition influences the firing of a biophysically detailed layer 5 pyramidal neuron model, which reproduced the spatiotemporal properties of sodium, calcium, and N-methyl-D-aspartate receptor spikes found experimentally. We found that oscillatory synchronization strongly influences the impact of distal inhibition on the pyramidal cell firing. Whereas asynchronous inhibition largely cancels out the facilitatory effects of distal excitatory inputs, inhibition oscillating synchronously at around 10∼20 Hz allows distal excitation to drive axosomatic firing, as if distal inhibition were absent. Underlying this is a switch from relatively infrequent burst firing to single spike firing at every period of the inhibitory oscillation. This phenomenon depends on hyperpolarization-activated cation current-dependent membrane potential resonance in the dendrite, but also, in a novel manner, on a cooperative amplification of this resonance by N-methyl-D-aspartate-receptor-driven dendritic action potentials. Our results point to a surprising dependence of the effect of recurrent inhibition by Martinotti cells on their oscillatory synchronization, which may control not only the local circuit activity, but also how it is transmitted to and decoded by downstream circuits.

Opponent Learning with Different Representations in the Cortico-Basal Ganglia Circuits.

The direct and indirect pathways of the basal ganglia (BG) have been suggested to learn mainly from positive and negative feedbacks, respectively. Since these pathways unevenly receive inputs from different cortical neuron types and/or regions, they may preferentially use different state/action representations. We explored whether such a combined use of different representations, coupled with different learning rates from positive and negative reward prediction errors (RPEs), has computational benefits. We modeled animal as an agent equipped with two learning systems, each of which adopted individual representation (IR) or successor representation (SR) of states. With varying the combination of IR or SR and also the learning rates from positive and negative RPEs in each system, we examined how the agent performed in a dynamic reward navigation task. We found that combination of SR-based system learning mainly from positive RPEs and IR-based system learning mainly from negative RPEs could achieve a good performance in the task, as compared with other combinations. In such a combination of appetitive SR-based and aversive IR-based systems, both systems show activities of comparable magnitudes with opposite signs, consistent with the suggested profiles of the two BG pathways. Moreover, the architecture of such a combination provides a novel coherent explanation for the functional significance and underlying mechanism of diverse findings about the cortico-BG circuits. These results suggest that particularly combining different representations with appetitive and aversive learning could be an effective learning strategy in certain dynamic environments, and it might actually be implemented in the cortico-BG circuits.

Computational models of behavioral addictions: State of the art and future directions.

Non-pharmacological behavioral addictions, such as pathological gambling, videogaming, social networking, or internet use, are becoming major public health concerns. It is not yet clear how behavioral addictions could share many major neurobiological and behavioral characteristics with substance use disorders, despite the absence of direct pharmacological influences. A deeper understanding of the neurocognitive mechanisms of addictive behavior is needed, and computational modeling could be one promising approach to explain intricately entwined cognitive and neural dynamics. This review describes computational models of addiction based on reinforcement learning algorithms, Bayesian inference, and biophysical neural simulations. We discuss whether computational frameworks originally conceived to explain maladaptive behavior in substance use disorders can be effectively extended to non-substance-related behavioral addictions. Moreover, we introduce recent studies on behavioral addictions that exemplify the possibility of such extension and propose future directions.

Highly differentiated projection-specific cortical subnetworks.

Pyramidal cells in the neocortex are differentiated into several subgroups based on their extracortical projection targets. However, little is known regarding the relative intracortical connectivity of pyramidal neurons specialized for these specific output channels. We used paired recordings and quantitative morphological analysis to reveal distinct synaptic transmission properties, connection patterns, and morphological differentiation correlated with heterogeneous thalamic input to two different groups of pyramidal cells residing in layer 5 (L5) of rat frontal cortex. Retrograde tracers were used to label two projection subtypes in L5: crossed-corticostriatal (CCS) cells projecting to both sides of the striatum, and corticopontine (CPn) cells projecting to the ipsilateral pons. Although CPn/CPn and CCS/CCS pairs had similar connection probabilities, CPn/CPn pairs exhibited greater reciprocal connectivity, stronger unitary synaptic transmission, and more facilitation of paired-pulse responses. These synaptic characteristics were strongly correlated to the projection subtype of the presynaptic neuron. CPn and CCS cells were further differentiated according to their somatic position (L5a and L5b, the latter denser thalamic afferent fibers) and their dendritic/axonal arborizations. Together, our data demonstrate that the pyramidal projection system is segregated into different output channels according to subcortical target and thalamic input, and that information flow within and between these channels is selectively organized.

Dopamine ramps for accurate value learning under uncertainty.

Dopamine signals ramping towards reward timings have become widely reported, but their functions remain elusive. Through modeling analyses and experiments in mice, a recent study by Mikhael, Kim et al. shows that such signals represent reward prediction errors used for accurate value learning in conditions with uncertainty about upcoming state and its resolution by sensory feedback.

Charmonium spectra at finite temperature from QCD sum rules with the maximum entropy method.

Charmonia spectral functions at finite temperature are studied using QCD sum rules in combination with the maximum entropy method. This approach enables us to directly obtain the spectral function from the sum rules, without having to introduce any specific assumption about its functional form. As a result, it is found that while J/ψ and η(c) manifest themselves as significant peaks in the spectral function below the deconfinement temperature T(c), they quickly dissolve into the continuum and almost completely disappear at temperatures between 1.0T(c) and 1.1T(c).

A Reinforcement Learning Approach to Understanding Procrastination: Does Inaccurate Value Approximation Cause Irrational Postponing of a Task?

Procrastination is the voluntary but irrational postponing of a task despite being aware that the delay can lead to worse consequences. It has been extensively studied in psychological field, from contributing factors, to theoretical models. From value-based decision making and reinforcement learning (RL) perspective, procrastination has been suggested to be caused by non-optimal choice resulting from cognitive limitations. Exactly what sort of cognitive limitations are involved, however, remains elusive. In the current study, we examined if a particular type of cognitive limitation, namely, inaccurate valuation resulting from inadequate state representation, would cause procrastination. Recent work has suggested that humans may adopt a particular type of state representation called the successor representation (SR) and that humans can learn to represent states by relatively low-dimensional features. Combining these suggestions, we assumed a dimension-reduced version of SR. We modeled a series of behaviors of a "student" doing assignments during the school term, when putting off doing the assignments (i.e., procrastination) is not allowed, and during the vacation, when whether to procrastinate or not can be freely chosen. We assumed that the "student" had acquired a rigid reduced SR of each state, corresponding to each step in completing an assignment, under the policy without procrastination. The "student" learned the approximated value of each state which was computed as a linear function of features of the states in the rigid reduced SR, through temporal-difference (TD) learning. During the vacation, the "student" made decisions at each time-step whether to procrastinate based on these approximated values. Simulation results showed that the reduced SR-based RL model generated procrastination behavior, which worsened across episodes. According to the values approximated by the "student," to procrastinate was the better choice, whereas not to procrastinate was mostly better according to the true values. Thus, the current model generated procrastination behavior caused by inaccurate value approximation, which resulted from the adoption of the reduced SR as state representation. These findings indicate that the reduced SR, or more generally, the dimension reduction in state representation, can be a potential form of cognitive limitation that leads to procrastination.

Film-Thickness-Controllable System for Preparing Silver Nanofilms through Absorbance Monitoring of the Thickness during a Silver-Mirror Reaction.

An innovative technique is proposed for forming silver thin films of nanometer-order thickness via a silver-mirror reaction. This approach is made possible by the real-time monitoring of the thickness of a silver thin film formed on the edge surface of a fiber core during the silver-mirror reaction using a homemade absorbance measurement system. The monitored absorbance value increases as silver plating progresses, and the relationship between the absorbance values and the thickness of the silver thin film is linear in the thickness range from approximately 30 to 60 nm. This technique was applied to the preparation of a fiber-optic surface plasmon resonance (FO-SPR) sensor. The sensor was successfully used to measure sucrose solutions with concentrations of less than 16% (w/v). The sensitivity of the sensor probe was estimated to be 2205 nm/RIU in the refractive index range of 1.333 - 1.357. The relative standard deviation of the wavelength shift obtained from measurements using different sensor probes was estimated to be less than 3.3%.