The document discusses modeling decision making deficits in disorders that impact the frontostriatal system using computational models of reinforcement learning. It notes that many such disorders involve changes in motivation and some have genetic heritability. However, the effects of candidate genes are generally small. The author proposes using a theoretical model of reinforcement learning that incorporates data on dopamine prediction errors and the basal ganglia to help identify which genes, tasks, and measures are most relevant. The model aims to integrate findings on how dopamine affects striatal learning of positive and negative prediction errors. Data from a temporal decision making task is presented that the model can fit at both group and single subject levels. The model may help modulate reinforcement learning parameters based on neurogenetic and pharmacological
Personality traits are known to moderate treatment response and are often an essential add-on to a symptom picture when performing a patient’s systematic evaluation. However, personality measures are often long to administer due to their large number of items. Rammstedt and John (2007) abbreviated the Big Five Inventory (BFI-44) to a 10-item version (BFI-10) and found that the shortened scales retained reasonable levels of reliability and validity. The Italian adaptation of BFI-44 was administered to 645 subjects, together with a socio-demographic questionnaire. Psychometric properties (i.e., internal consistency and construct validity) of the BFI-44 and of BFI-10 were assessed through Confirmatory Factor Analyses. Psychometric properties of the BFI-44 and BFI-10 overlapped those of the English, Spanish and German version. Confirmatory analyses revealed that the factor structure based on responses to the items of BFI-10 was invariant with the factor structure based on responses to the items of BFI-44. We also modeled the effects of social desirability, age, gender and their interactions. The effects of such covariates were substantially invariant across factor structures of BFI-10 and BFI-44. Social desirability increased the goodness of fit of the measurement model while the linear component of age was positively correlated with Conscientiousness and negatively with Nevroticism, on which females scored higher than males. Though the BFI-10 scales showed acceptable levels of reliability and validity, they do not reach the depth of construct operazionalization provided by the scales of BFI-44, which thus should be employed in systematic evaluation in clinical settings.
In this paper a new mixed nodal-mesh formulation of the PEEC
method is proposed. Based on the hypothesis that charges reside
only on the surface of conductors and that current density is
solenoidal inside them, a novel scheme is developed fully
exploiting the physical properties of charges and currents. It
comes out that the presented approach allows to reduce the number
of unknowns while preserving the accuracy. An elegant and
efficient algorithm, based on graph theory, is proposed to
automatically search independent loops on three dimensional
rectangular grids such as those arising in volumetric PEEC
formulation. The method is validated through numerical results
that confirm the accuracy of the proposed formulation from
DC-to-daylight and its capability to provide memory saving.
In many applications one observes rapid change of the solution in the boundary region. Accurate and numerically efficient resolution of the solution close to the moving boundaries is considered to be an important problem. We develop an approach to the optimization of the discretization grids for finite-difference scheme. Using the suggested approach we are able to achieve the exponential convergence of the boundary Neumann- to-Dirichlet maps. It increases the convergence order without increasing the stencil size of the finite-difference scheme and preserves stability.
Faults and Regression Testing - Fault interaction and its repercussionsICSM 2011
Paper: Fault Interaction and its Repercussions
Authors: Nicholas DiGiuseppe and James A. Jones
Seesion: Research Track 1: Faults and Regression Testing
FREE SPICE MODEL of 1S1887 in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
SPICE MODEL of 30WQ04FN (Professional Model) in SPICE PARKTsuyoshi Horigome
SPICE MODEL of 30WQ04FN (Professional Model) in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
FREE SPICE MODEL of 1SS397 in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
SPICE MODEL of RURG8060 , TC=110degree (Standard Model) in SPICE PARKTsuyoshi Horigome
SPICE MODEL of RURG8060 , TC=110degree (Standard Model) in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
SPICE MODEL of SF3L60U (Standard Model) in SPICE PARKTsuyoshi Horigome
SPICE MODEL of SF3L60U (Standard Model) in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
SPICE MODEL of M1FS4 (Standard Model) in SPICE PARKTsuyoshi Horigome
SPICE MODEL of M1FS4 (Standard Model) in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
FREE SPICE MODEL of 1SS187 in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
SPICE MODEL of 1SS187 (Professional Model) in SPICE PARKTsuyoshi Horigome
SPICE MODEL of 1SS187 (Professional Model) in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
SPICE MODEL of 1SS193 (Professional Model) in SPICE PARKTsuyoshi Horigome
SPICE MODEL of 1SS193 (Professional Model) in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
Personality traits are known to moderate treatment response and are often an essential add-on to a symptom picture when performing a patient’s systematic evaluation. However, personality measures are often long to administer due to their large number of items. Rammstedt and John (2007) abbreviated the Big Five Inventory (BFI-44) to a 10-item version (BFI-10) and found that the shortened scales retained reasonable levels of reliability and validity. The Italian adaptation of BFI-44 was administered to 645 subjects, together with a socio-demographic questionnaire. Psychometric properties (i.e., internal consistency and construct validity) of the BFI-44 and of BFI-10 were assessed through Confirmatory Factor Analyses. Psychometric properties of the BFI-44 and BFI-10 overlapped those of the English, Spanish and German version. Confirmatory analyses revealed that the factor structure based on responses to the items of BFI-10 was invariant with the factor structure based on responses to the items of BFI-44. We also modeled the effects of social desirability, age, gender and their interactions. The effects of such covariates were substantially invariant across factor structures of BFI-10 and BFI-44. Social desirability increased the goodness of fit of the measurement model while the linear component of age was positively correlated with Conscientiousness and negatively with Nevroticism, on which females scored higher than males. Though the BFI-10 scales showed acceptable levels of reliability and validity, they do not reach the depth of construct operazionalization provided by the scales of BFI-44, which thus should be employed in systematic evaluation in clinical settings.
In this paper a new mixed nodal-mesh formulation of the PEEC
method is proposed. Based on the hypothesis that charges reside
only on the surface of conductors and that current density is
solenoidal inside them, a novel scheme is developed fully
exploiting the physical properties of charges and currents. It
comes out that the presented approach allows to reduce the number
of unknowns while preserving the accuracy. An elegant and
efficient algorithm, based on graph theory, is proposed to
automatically search independent loops on three dimensional
rectangular grids such as those arising in volumetric PEEC
formulation. The method is validated through numerical results
that confirm the accuracy of the proposed formulation from
DC-to-daylight and its capability to provide memory saving.
In many applications one observes rapid change of the solution in the boundary region. Accurate and numerically efficient resolution of the solution close to the moving boundaries is considered to be an important problem. We develop an approach to the optimization of the discretization grids for finite-difference scheme. Using the suggested approach we are able to achieve the exponential convergence of the boundary Neumann- to-Dirichlet maps. It increases the convergence order without increasing the stencil size of the finite-difference scheme and preserves stability.
Faults and Regression Testing - Fault interaction and its repercussionsICSM 2011
Paper: Fault Interaction and its Repercussions
Authors: Nicholas DiGiuseppe and James A. Jones
Seesion: Research Track 1: Faults and Regression Testing
FREE SPICE MODEL of 1S1887 in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
SPICE MODEL of 30WQ04FN (Professional Model) in SPICE PARKTsuyoshi Horigome
SPICE MODEL of 30WQ04FN (Professional Model) in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
FREE SPICE MODEL of 1SS397 in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
SPICE MODEL of RURG8060 , TC=110degree (Standard Model) in SPICE PARKTsuyoshi Horigome
SPICE MODEL of RURG8060 , TC=110degree (Standard Model) in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
SPICE MODEL of SF3L60U (Standard Model) in SPICE PARKTsuyoshi Horigome
SPICE MODEL of SF3L60U (Standard Model) in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
SPICE MODEL of M1FS4 (Standard Model) in SPICE PARKTsuyoshi Horigome
SPICE MODEL of M1FS4 (Standard Model) in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
FREE SPICE MODEL of 1SS187 in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
SPICE MODEL of 1SS187 (Professional Model) in SPICE PARKTsuyoshi Horigome
SPICE MODEL of 1SS187 (Professional Model) in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
SPICE MODEL of 1SS193 (Professional Model) in SPICE PARKTsuyoshi Horigome
SPICE MODEL of 1SS193 (Professional Model) in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
Report Back from SGO 2024: What’s the Latest in Cervical Cancer?bkling
Are you curious about what’s new in cervical cancer research or unsure what the findings mean? Join Dr. Emily Ko, a gynecologic oncologist at Penn Medicine, to learn about the latest updates from the Society of Gynecologic Oncology (SGO) 2024 Annual Meeting on Women’s Cancer. Dr. Ko will discuss what the research presented at the conference means for you and answer your questions about the new developments.
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
MANAGEMENT OF ATRIOVENTRICULAR CONDUCTION BLOCK.pdfJim Jacob Roy
Cardiac conduction defects can occur due to various causes.
Atrioventricular conduction blocks ( AV blocks ) are classified into 3 types.
This document describes the acute management of AV block.
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
Maxilla, Mandible & Hyoid Bone & Clinical Correlations by Dr. RIG.pptx
Frank_NeuroInformatics11.pdf
1. Modeling decision making deficits in frontostriatal disorders
Michael Frank
Laboratory for Neural Computation and Cognition
Brown University
2. Computational Psychiatry and...
Neurogenocomputomics
• Many disorders broadly characterized by changes in motivation
• Several fronto-striatal disorders have substantial genetic heritability
• Individual differences in reinforcement learning?
3. Computational Psychiatry and...
Neurogenocomputomics
• Many disorders broadly characterized by changes in motivation
• Several fronto-striatal disorders have substantial genetic heritability
• Individual differences in reinforcement learning?
• But... Candidate gene effects are generally small
• Which genes? Which task? Which measure?
4. Computational Psychiatry and...
Neurogenocomputomics
• Many disorders broadly characterized by changes in motivation
• Several fronto-striatal disorders have substantial genetic heritability
• Individual differences in reinforcement learning?
• But... Candidate gene effects are generally small
• Which genes? Which task? Which measure?
• Need theoretical model! (and converging pharmacology/imaging)
Frank & Fossella, 2011; Maia & Frank, 2011; Huys et al, 2011
8. D1 effects on striatal learning: Positive PE
Three factor learning: presynaptic, postsynaptic and DA
9. D2 effects on striatal learning: Negative PE
Frank 2005
10. Neural model of basal ganglia and dopamine
Integrates a wide range of data into a single coherent framework
Separate Go and NoGo populations integrate statistics of reinforcement
preSMA
Input
Striatum γ [Vm− Θ]
cVm = gege[E Vm] y j ≈ γ [V − ] + 1
+
m Θ+
e
+ g g [E V ]
i i i m
+ g g [E Vm] β
l l l net = ge ≈ <x i w ij > +
N
STN + ...
w ij
GPe
xi
Go NoGo Thalamus
p p t t
∆wij ≈ (xi yj )−(xi yj )
SNc GPi/SNr
Frank, 2005, 2006 J Cog Neurosci, Neural Networks
11. Maximizing Reward via RT Adaptation:
Temporal Utility Integration Task
Reward Frequency Reward Magnitude
1.0 350
0.9 CEV CEV
DEV 300 DEV
0.8 IEV IEV
0.7 CEVR # Points Gained 250 CEVR
Probability
0.6 200
0.5
0.4 150
0.3 100
0.2
50
0.1
0.0 0
0 1000 2000 3000 4000 5000 0 1000 2000 3000 4000 5000
Time (ms) Time (ms)
Expected Value
60
Expected Value (freq*mag)
55
50
45
40
35
30
25
20 CEV
15 DEV
10 IEV
5 CEVR
0
0 1000 2000 3000 4000 5000
Time (ms)
12. RL model: Fit to data across all subjects
RL model : adjust RTs as a function of reward prediction errors
Frank, Doll, Oas-Terpstra & Moreno (2009, Nature Neuroscience)
15. Exploration vs Exploitation
• By exploiting learned strategies, we know we can get a certain amount
of reward
• But don’t know how good it can get. ⇒ Need to Explore
• Theory: Explore based on relative uncertainty about whether other
actions might yield better outcomes than status quo (Dayan & Sejnowksi 96)
16. Exploration vs Exploitation
• By exploiting learned strategies, we know we can get a certain amount
of reward
• But don’t know how good it can get. ⇒ Need to Explore
• Theory: Explore based on relative uncertainty about whether other
actions might yield better outcomes than status quo (Dayan & Sejnowksi 96)
22. EEG reveals temporal dynamics
Relative uncertainty represented prior to choice, and more so in exploratory trials
Cavanagh, Cohen, Figueroa & Frank, under review
23. Negative symptoms in schizophrenia:
Uncertainty-Based Exploration
Anhedonia & Exploration
Uncertainty-driven exploration
0.8
0.40
0.6
0.35 SZ
CN 0.4
ε (x 1e4)
0.30 0.2
0
ε (x1e4)
0.25
0.20 -0.2
** -0.4
0.15 -0.6
0.10 -0.8 r = -.44, p = .002
0.05 -1.0
0.00 -1.2
0 1 2 3 4
ε(uncert) Global Anhedonia
• Anhedonia = behavioral component of reward seeking (e.g., initiating
social/recreational activities) not capacity to experience pleasure
• Anhedonia related to exploration and not learning from reward prediction errors
Strauss et al, 2011, Biological Psychiatry
24. Obsessive Compulsive Disorder: Aversion to Uncertainty
Uncertainty-driven exploration
0.6
CN
0.4 OCD
ε (x 1e4)
0.2
0.0
-0.2
-0.4
gains losses
preliminary data, N=17 per group
with Mascha van ’t Wout, Ben Greenberg, Steve Rasmussen
25. Summary
• Dopamine modulates reinforcement learning and choice based on
positive and negative outcomes: patients, pharmacology, genetics,
imaging
• Prefrontal cortex tracks outcome uncertainty so as to reduce it
• Disruption of these mechanisms is associated with fronto-striatal
disorders, Parkinson’s, schizophrenia, OCD
• Models integrate between multiple levels of analysis:
neural mechanism to abstract computation (see Thomas Wiecki
demonstration tomorrow!).
26. Thanks To...
Bradley Doll
Christina Figueroa
Jim Cavanagh
David Badre
Jeff Cockburn
Anne Collins
Thomas Wiecki
Jim Gold
Kent Hutchison
Mascha van ’t Wout
Nicole Long
Mike Cohen
Ahmed Moustafa
Scott Sherman Lab for Neural Computation and Cognition
The patients