Tag Archives: Brain and Cognitive Sciences MIT

Brain and Cognitive Sciences MIT Lecture Notes

Lecture 1: From Neurons to Neural Networks

The Liu Lab at MIT, where we are working to elucidate the biophysical mechanisms of plasticity in synapses, cells and networks. We are particularly interested in innovating new techologies that permit conclusive experiments – to understand the rules of plasticity in terms of their functional logic as well as their biological implementations. Ultimately we attempt to examine how rules at different functional levels (from synapse to network) interact. We believe that this integrative approach will eventually lead to a coherent general framework for neural plasticity.
A Culture System for Direct Neuronal Manipulation

We have perfected a culture system that allows us to grow dissociated neurons in a dish where they can be directly observed. The neurons form connections and self-assemble into functional networks, allowing us access to the same phenomena of plasticity that occur in vivo. The system is also amenable to genetic transfections, and provides a very short incubation period for studying the role of various genes. Best of all, the cultured cells can be viewed, measured and manipulated without dissection, providing what is perhaps the most convenient preparation for plasticity studies.
Synaptic Staining and Functional Visualization

The lab incorporates a wide variety of staining and dynamical imaging techniques for locating synapses and characterizing their functional capabilities. Functional dyes such as FM1-43 and FM4-64 are used in nearly all of our experiments to depict which synapses on a given cell or network are functional, as well as providing some index of the synapses’ strengths. We have also been experimenting with methods for using the exchange of these dyes to visualize synaptic activity, and have recently fostered collaborations to develop new staining molecules for better functional assays. These methods can combine with calcium-sensitive fluorescence and immunostaining to provide a visual description of the synapses along a cultured cell or neural network.
Stimulation and Recording of Single Synapses

A major challenge in studying the synapse is how to tell what observed effects are due to presynaptic factors and which to postsynaptic ones. A large proportion of the lab’s efforts have gone towards finding a technique to directly control the behavior of one side of the synapse – the presynaptic one – by replacing it with an artificial terminal under our control. By using a carefully optimized form of iontophoresis, we are able to deliver specified profiles of neurotransmitter directly to single synapses.

A slight holding potential can retain transmitter inside a quartz micropipette that can then be robotically positioned directly alongside a single synapse (located using dyes such as those described above). A specialized amplifier and software program then delivers an iontophoretic current to eject transmitter from the pipette, generating a time course that exactly mimics that observed during real synaptic transmission. A patch-clamped electrode on the receiving cell can then record any post-synaptic activity that results from this simulated presynaptic release. This method therefore allows the researcher to study the synapse one side at a time, by effectively replacing the presynaptic terminal with an artificial “terminal” capable of delivering arbitrary profiles of neurotransmitter.
Functional Mapping of Dendritic Trees

One of our recent techniques is to provide a complete functional description of a given neuron’s dendrites. After labeling functional synapses with FM1-43, automated computer software drives robotic manipulators to guide iontophoresis pipettes to synaptic sites. Applying iontophoretic pulses while patch clamping the postsynaptic cell then yields a functional description of each synapse’s strength. After approximately five minutes a dendritic tree of many synapses can be precisely mapped, giving information that can be useful for describing poly-synaptic interactions within a given neuron.
Confocal and Two-photon Microscopy

The use of cultured neurons permits direct visualization that we try to realize via the best optical equipment. The lab is equipped with three confocal microscopes, many with muiltiple lasers, that provide high-resolution imaging of cells and fluorscent markers during recording and stimulation. The lab has also recently gained access to a two-photon microscope setup, as well as acquired our own high-resolution digital video microcamera for rapid, real-time visualization of network activity.
Genetic Manipulations in Cultured Cells and Animal Models

We are also equipped with full facilities for transfecting our cell cultures with inserted genes, with up to 50% of all cells within a culture successfully transfected. Also, thanks to a collaboration with Prof. Susumu Tonegawa, we are able to probe questions of synaptic plasticity in vivo at the physiological and behavioral levels, by creating mice overexpressing or deficient in particular genes.
Computational Analysis and Biologically-inspired Modeling

The lab is unusual among groups working at a similar level of experimental biology in the number of students with computational backgrounds that we employ. Our group has numerous members with degrees in computer science and engineering, and we hope to continue to attract more. Modeling studies are currently underway to predict the transmitter release and binding kinetics at single synapses, while other models seek to discern equations governing the homeostasis of synaptic input. We especially hope to require computational analysis in the coming years as our cultured neural network technology matures. Towards this end, collaborations are currently underway with students in the Seung Lab for Theoretical Neurobiology.
Multi-electrode Arrays for Observing Cultured Neural Networks

One of our recent acquisitions is a 64-channel multi-electrode array, which supports recording and inducing the electrical activity of many cultured neurons. The system can be used in conjunction with patch clamp and iontophoresis, as well as our visualization protocols. Commercial software allows for in-depth analysis of network activity in real time, and stimulation protocols that automatically respond to the system’s observations. We have been working on ways to deploy this system in an incubator to extend our interface with the cells from hours to days.
Lecture 2 Prefrontal Cortex and the Neural Basis of Cognitive Control

Our research interests center around the neural mechanisms for voluntary, goal-directed, behavior. Much effort is directed at the prefrontal cortex, a brain region associated with the highest levels of cognitive function. We combine a sophisticated behavioral methodology with techniques for examining the activity of groups of neurons.

The prefrontal cortex (PFC), a cortical region at the anterior end of the brain, has long been known play a central role in orchestrating complex thoughts and actions. Its damage or dysfunction seems to result in a loss of the brain’s “executive”. It disrupts our ability to ignore distractions, hold important information “in mind”, plan behavior, and control impulses.

Results from our laboratory suggests that the PFC provides an infrastructure for the rapid synthesis of the diverse information. Its major function may be to acquire and implement our internal representations of the “rules of the game” needed to achieve a given goal in a given situation. This could lay the foundation for the complex and elaborate forms of behavior observed in primates, in whom this structure is most elaborate.
Lecture 3: Hippocampal Memory Formation and the Role of Sleep

Research in the Wilson laboratory focuses on the study of information representation across large populations of neurons in the mammalian nervous system, as well as on the mechanisms that underlie formation and maintenance of distributed memories in freely behaving animals. To study the basis of these processes, the lab employs a combination of molecular genetic, electrophysiological, pharmacological, behavioral, and computational approaches. Using techniques that allow the simultaneous activity of ensembles of hundreds of single neurons to be examined in freely behaving animals, the lab examines how memories of places and events are encoded across networks of cells within the hippocampus ­ a region of the brain long implicated in the processes underlying learning and memory.

These studies of learning and memory in awake, behaving animals have led to the exploration of the nature of sleep and its role in memory. Previous theories have suggested that sleep states may be involved in the process of memory consolidation, in which memories are transferred from short to longer-term stores and possibly reorganized into more efficient forms. Recent evidence has shown that ensembles of neurons within the hippocampus, which had been activated during behavior are reactivated during periods of dreaming. By reconstructing the content of these states, specific memories can be tracked during the course of the consolidation process.
Lecture 4: The Formation of Internal Modes for Learning Motor Skills

In this talk, I will discuss a new perspective on how the central nervous system (CNS) represents and solves some of the most fundamental computational problems of motor control. In particular, I will discuss the task of transforming a planned limb movement into an adequate set of motor commands. To carry out this task the CNS must solve a complex inverse dynamic problem. This problem involves the trans-formation from a desired motion to the forces that are needed to drive the limb. The inverse dynamic problem is a hard computational challenge because of the need to coordinate multiple limb segments and because of the continuous changes in the mechanical properties of the limbs and of the environment with which they come in contact. A number of studies of motor learning have provided support for the idea that the CNS creates, updates and exploits internal representations of limb dynamics in order to deal with the complexity of inverse dynamics. In the talk I will discuss how such internal representations are likely to be built by combining the modular primitives in the spinal cord as well as other building blocks found in higher brain structures. Experimental studies on spinalized frogs and rats have led to the conclusion that the premotor circuits within the spinal cord are organized into a set of discrete modules. Each module, when activated, induces a specific force field and the simultaneous activation of multiple modules leads to the vectorial combination of the corresponding fields. I regard these force fields as computational primitives that are used by the CNS for generating a rich grammar of motor behaviors.
Lecture 5: Look and See: How the Brain Selects Objects and Directs the Eyes
Purpose of the Research

To determine how visual perception is processed by the brain and how visually guided eye movements are generated.

The methods used in the Schiller lab are:

Physiological studies in non-human primates that utilize single-cell recordings, microstimulation, pharmacological manipulation, tissue inactivation and ablation.
Behavioral studies in normal human subjects, in patients with brain infarcts, and in non-human primates that examine visual and oculomotor capacities.

Lecture 6: How the Brain Wires Itself
Plasticity and Dynamics in the Developing and Adult Cerebral Cortex

Plasticity, or the adaptive response of the brain to changes in inputs, is essential to brain development and function. The developing brain requires a genetic blueprint but is also acutely sensitive to the environment. The adult brain constantly adapts to changes in stimuli, and this plasticity is manifest not only as learning and memory but also as dynamic changes in information transmission and processing. The goal is to understand long-term plasticity and short-term dynamics in networks of the developing and adult cortex.

Brain Anatomy

Annotated histology sections of developing mouse is a digital atlas of mouse development and a database resource for spatially mapped data such as in situ gene expression and cell lineage. The 3D Mouse atlas (MRI based) site allows visitors to query cerebral structures of a 13.5 dpc mouse embryo and view 3-D reconstructions of those structures as well as reconstructions of the entire embryo. Cranial nerve tutorial. Fundamental information about the cranial nerves. Neuroanatomy online at University of Utah. This site contains interactive multimedia which allows the learner to move between diagrams, gross anatomy, microscopic specimens, definitions, and atlases in a manner tailored to the student’s needs. Loyola University. Online neuroanatomy course information and content.

Source: http://ocw.mit.edu/
Instructors: Prof. Peter H. Schiller
MIT Course Number: 9.95-A

Readings on Neurology, Neuropsychology, and Neurobiology of Aging:

1 Introduction: The Aging Brain, Corkin Lecture Squire, L. “Memory systems of the brain: A brief history and current perspective.” Neurobiology of Learning and Memory 82 (2004): 171-177.

Hof, P. R., and J. H. Morrison. “The aging brain (includes Alzheimer’s): morphomolecular senescence of cortical circuits.” Trends in Neurosci 27 (2004): 607-613.


Piguet, O., and S. Corkin. “The aging brain.” In Learning and the Brain: An Encyclopedia. Edited by S. Feinstein. Westport, CT: Greenwood Publishing Group. (In press)

2 Imaging the Aging and Demented Brain Jack, C. R., B. A. Shiung, J. L. Gunter, et al. “Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD.” Neurology 62 (2004): 591-600.

Karas, G. B., O. Scheltens, S. A. R. B. Rombouts, et al. “Global and local gray matter loss in mild cognitive impairment and Alzheimer’s disease.” NeuroImage 23 (2004): 708-716.

Salat, D., D. Tuch, D. Greve, et al. “Age-related alterations in white matter microstructure measured by diffusion tensor imaging.” Neurobiology of Aging. (In press)

Den, Heijer T., M. Oudkerk, L. J. Launder, et al. “Hippocampal, amygdalar, and global brain atrophy in different apolipoprotein E genotypes.” Neurology 59 (2002): 746-748.

Klunk, W., H. Engler, A. Nordberg, et al. “Imaging brain amyloid in Alzheimer’s disease with Pittsburgh compound-B.” Annals of Neurology 55 (2004): 306-319.


Petersen, R. “Mild cognitive impairment as a diagnostic entity.” Journal of Internal Medicine 256 (2004): 183-194.

3 Working Memory in Aging and Alzheimer’s Bowles, R. P., and T. A. Salthouse. “Assessing the age-related effects of proactive interference on working memory tasks using the Rasch model.” Psychology and Aging 18 (2003): 608-615.

Lamar, M., D. M. Yousem, and S. M. Resnick. “Age differences in orbitofrontal activation: an fMRI investigation of delayed match and nonmatch to sample.” Neuroimage 21 (2004): 1368-1376.

Park, D. C., R. C. Welsh, C. Marshuetz, et al. “Working memory for complex scenes: age differences in frontal and hippocampal activations.” JOCN 15 (2003): 1122-1134.

Cabeza, R., S. M. Daselaar, F. Dolcos, et al. “Task-independent and taskspecific age effects on brain activity during working memory, visual attention and episodic retrieval.” Cereb Cortex 14 (2004): 364-375.

Baddeley, A. D., S. Bressi, Sala S. Della, R. Logie R, and H. Spinnler. “The decline of working memory in Alzheimer’s disease. A longitudinal study.” Brain 114 (1991): 2521-2542.


Salthouse, T. “The aging of working memory.” Neuropsychology 8 (1994): 353-543.

4 Recollection and Familiarity in Healthy Aging Naveh-Benjamin, M., J. Guez, A. Kilb, and S. Reedy S. “The associative memory deficit of older adults: further support using face-name associations.” Psychology and Aging 19 (2004): 541-546.

Glisky, E. L., S. R. Rubin, and P. S. Davidson. “Source memory in older adults: an encoding or retrieval problem?” JEP: LMC 27 (2001): 1131-1146.

Castel, A. D., and F. I. Craik. “The effects of aging and divided attention on memory for item and associative information.” Psychology and Aging 18 (2003): 873-885.

Clarys, D., M. Isingrini, and K. Gana. “Mediators of age-related differences in recollective experience in recognition memory.” Acta Psychologica 109 (2002): 315-329.

Vandenbroucke, M. W., R. Goekoop, E. J. Duschek, et al. “Interindividual differences of medial temporal lobe activation during encoding in an elderly population studied by fMRI.” Neuroimage 21 (2004): 173-180.


Naveh-Benjamin, M. “Effects of divided attention on encoding and retrieval processes: assessment of attentional costs and a componential analysis.” JEP: LMC 26 (2000): 1170-1187.

5 Emotional Memory in Aging and Age-related Disease Leigland, L., L. Schulz, and J. Janowsky. “Age related changes in emotional memory.” Neurobiology of Aging 25 (2004): 1117-1124.

Kensinger, E., A. Anderson, J. Growdon, and S. Corkin. “Effects of Alzheimer disease on memory for verbal emotional information.” Neuropsychologia 42 (2004): 791-800.

Denburg, N., T. Buchanan, D. Tranel, and R. Adolphs. “Evidence for preserved emotional memory in normal older persons.” Emotion 3 (2003): 239-253.

Charles, S. T., M. Mather, and L. Cstensen. “Aging and emotional memory: The forgettable nature of negative images for older adults.” JEP: General 132 (2003): 310-324.

Kensinger, E. “Memory for contextual details: Effects of emotion and aging.” Psychology and Aging. (In press)

6 Midterm Exam (1 Hour)

Implicit Memory

Woodruff-Pak, D. S., and R. G. Finkbiner. “Larger nondeclarative than declarative deficits in learning and memory in human aging.” Psychol Aging 10, no. 3 (1995): 416-26.

Light, L. L., and A. Singh. “Implicit and explicit memory in young and older adults.” J Exp Psychol Learn Mem Cogn 13, no. 4 (1987): 531-41.

7 Alzheimer’s Disease: Natural History, Genetics, and Pathophysiology
(Guest Lecture: Michael C. Irizarry, M. D., Alzheimer Disease Research Unit, Massachusetts General Hospital)

Ingelsson, M., H. Fukumoto, K. L. Newell, J. H. Growdon, E. T. Hedley-Whyte, M. P. Frosch, M. S. Albert, B. T. Hyman, and M. C. Irizarry. “Early Aß accumulation and progressive synaptic loss, gliosis, and tangle formation in AD brain.” Neurology 62 (2004): 925-31.

8 Alzheimer’s Disease: Early Molecular Biology, Genetics, Animal Models Nestor, P. J., P. Scheltens, and J. R. Hodges. “Advances in the early detection of Alzheimer’s disease.” Nature Medicine 10 Suppl. (2004): S34-S41.

Melov, S. “Modeling mitochondrial function in aging neurons.” Trends in Neurosci 27 (2004): 601-606.

Mattson, M. P., S. Maudsley, and B. Martin. “BDNF and 5-HT: a dynamic duo in age-related neuronal plasticity and neurodegenerative disorders.” Trends in Neurosci 27 (2004): 589-594.

Toescu, E. C., A. Verkhrasky, and P. W. Landfield. “Ca++ regulation and gene expression in normal brain aging.” Trends in Neurosci 27 (2004): 614-620.

9 Alzheimer’s Disease (cont.) Glenner, G. “Amyloid ß protein and the basis for Alzheimer’s disease.” In Progress in Clinical and Biological Research. Edited by N. Back, G. Brewer, V. Eijsvoogel, R. Grover, K. Hirschhorn, et al. Vol. 317, Alzheimer’s Disease and Related Disorders, edited by K. Iqbal, H. Wisniewski, and B. Winblad. New York, NY: Alan R. Liss, Inc., 1989, pp. 857-868.

Schenk, D., R. Barbour, W. Dunn, et al. “Immunization with amyloid-ß attenuates Alzheimer-disease-like pathology in the PDAPP mouse.” Nature 400, no. 6740 (1999): 173-7.

Selkoe, D. J., and D. Schenk. “Alzheimer’s Disease: Molecular understanding predicts amyloid-based therapeutics.” Annu Rev Pharmacol Toxicol 43 (2003): 545-584.

10 Alzheimer’s Disease: ß-amyloid and LTP, Distribution of AD Target Tissues, Tauopathies Teter, B. and C. E. Finch. “Caliban’s heritance and the genetics of neuronal aging.” Trends in Neurosci 27 (2004): 627-632.

Citron, M., T. Oltersdorf, C. Haass, et al. “Mutation of the ß-amyloid precursor protein in familial Alzheimer’s disease increases bprotein production.” Nature 360 (1992): 672-4.

Schenk, D., M. Hagen, and P. Seubert. “Current progress in ß-amyloid immunotherapy.” Curr Opin Immunol 16 (2004): 599-606.

Hock, C., U. Konietzko, J. R. Streffer, et al. “Antibodies against betaamyloid slow cognitive decline in Alzheimer’s disease.” Neuron 38, no. 4 (2003): 547-54.

Blanchard, B. J., A. Chen, L. M. Rozeboom, K. A. Stafford, P. Weigele, and V. M. Ingram. “Efficient reversal of Alzheimer’s disease fibril formation and elimination of neurotoxicity by a small molecule.” Proc Natl Acad Sci U S A 101 (2004): 14326-32.

Ye, C., D. M. Walsh, D. J. Selkoe, and D. M. Hartley. “Amyloid ß-protein induced electrophysiological changes are dependent on aggregation state: N-methyl-D-aspartate (NMDA) versus non-NMDA receptor/channel activation.” Neurosci Letters 366 (2004): 320-325.

11 Parkinson’s Disease/Huntington’s Disease: Molecular, Genetic Mechanisms, Memory Gilbert, B., S. Belleville, L. Bherer, and S. Chouinard. “Study of verbal working memory in patients with Parkinson’s disease.” Neuropsychology 19 (2005): 106-14.

Ross, C. A., and M. A. Poirier. “Protein Aggregation and neurodegenerative disease.” Nature Medicine 10 Suppl. (2004): S10-S17.

Lemiere, J., M. Decruyenaere, G. Evers-Kiebooms, et al. “Cognitive changes in patients with Huntington’s disease (HD) and asymptomatic carriers of the HD mutation-a longitudinal followup study.” J Neurol 251 (2004): 935-42.

12 Parkinson’s Disease/Huntington’s Disease: Molecular, Genetic Mechanisms, Memory (cont.) Bossy-Wetzel, E., R. Schwarzenbacher, and S. A. Lipton. “Molecular pathways to neurodegeneration.” Nature Medicine 10 Suppl. (2004): S2-S9.

Castner, S., and P. Goldman-Rakic P. “Enhancement of working memory in aged monkeys by a sensitizing regimen of dopamine D1 receptor stimulation.” J Neurosci 24, no. 6 (2004): 1446-50.

Knopman, D. S., B. F. Boeve, and R. C. Petersen. “Essentials of the proper diagnoses of mild cognitive impairment, dementia, and major subtypes of dementia.” Mayo Clin Proc 78, no. 10 (2003): 1290-308.

Final Exam

Source: http://ocw.mit.edu/
Prof. Suzanne Corkin
Prof. Vernon M. Ingram
MIT Course Number: 9.110J / 7.92J



Introductory Lectures


Required Readings

Amazon logo Ullman, S. High-level Vision. Cambridge, MA: MIT Press, 1996, chapters 1 and 2. ISBN: 0262710072.

Riesenhuber, M., and T. Poggio. "Models of object recognition." Nature Neuroscience 3 Suppl (2000): 1199-1204.

Grill-Spector, K., and R. Malach. "The human visual cortex." Annual Review of Neuroscience 27 (2004): 649-677.

Orban, G. A., D. Van Essen, and W. Vanduffel. "Comparative mapping of higher visual areas in monkeys and humans." Trends in Cognitive Sciences 8, no. 7 (2004): 315-324.

Logothetis, N. K., and D. L. Sheinberg. "Visual object recognition." Annual Review of Neuroscience 19 (1996): 577-621.

Parker, A. J., and W. T. Newsome. "Sense and the single neuron: probing the physiology of perception." Annual Review of Neuroscience 21 (1998): 227-277.

Fujita, I. "The inferior temporal cortex: architecture, computation, and representation." Journal of Neurocytology 31 (2002): 359-371.

Supplemental Readings

Those with little neuroscience background should read a textbook version of the anatomy and physiology of the ventral visual stream:

Amazon logo Kandel, and Schwartz. Principles of Neural Science. 4th ed. New York, NY: McGraw-Hill, Health Professions Division, c2000, chapters 25, 27, 28. ISBN: 0838577016.

More Ventral Stream Background:

Amazon logo Chalupa, and Werner. The Visual Neurosciences. Cambridge, MA: MIT Press, November 2003, chapter 78. ISBN: 0262033089. (E. Rolls)

Tanaka, K. "Mechanisms of visual object recognition: monkey and human studies." Curr Opin Neurobiol 7 (1997): 523-529.

Computational Issues Involved in Recognition:

Sinha, P. "Recognizing complex patterns." Nature Neuroscience 5 Suppl (2002): 1093-1097.

Relationship of fMRI and Spikes:

Logothetis, N. K., and B. A. Wandell. "Interpreting the BOLD signal." Annual Reviews of Physiology 66 (2004): 735-769.

Logothetis, N. K., J. Pauls, M. Augath, T. Trinath, and A. Oeltermann. "Neurophysiological investigation of the basis of the fMRI signal." Nature 412 (2001): 150-157.

Comparison of Monkeys and Humans:

Brewer, A. A., W. A. Press, N. K. Logothetis, and B. A. Wandell. "Visual areas in macaque cortex measured using functional magnetic resonance imaging." Journal of Neuroscience 22 (2002): 10416-10426.

History of Science:

Gross, C. G. "How inferior temporal cortex became a visual area." Cereb Cortex 4 (1994): 455-469.


Neuronal Object Representations: Spatial Aspect of the Code

1) How sparse versus distributed are the neural codes for objects?

2) How physically/spatially localized are cortical object representations?

3) Are the answers to (1) and (2) different for different categories of objects?

Required Readings

Amazon logo Barlow, H. "The neuron doctrine in perception." In The Cognitive Neurosciences. Edited by M. Gazzaniga. Cambridge, MA: MIT Press, 1995, pp. 415-435. ISBN: 0262071576.

Tanaka, K. "Columns for complex visual object features in the inferotemporal cortex: clustering of cells with similar but slightly different stimulus selectivities." Cereb Cortex 13 (2003): 90-99.

Tsao, D. Y., W. A. Freiwald, T. A. Knutsen, J. B. Mandeville, and R. B. Tootell. "Faces and objects in macaque cerebral cortex." Nature Neuroscience 6, no. 9 (2003): 989-995.

Supplemental Readings (Required to read at least two ‘to be presented’ papers, indicated by *)

Sparse vs. Distributed Representations:

*Olshausen, B. A., and D. J. Field. "Sparse coding of sensory inputs." Curr Opin Neurobiol 14 (2004): 481-487.

Simoncelli, E. P. "Vision and the statistics of the visual environment." Curr Opin Neurobiol 13 (2003): 144-149.

Olshausen, B. A., and D. J. Field. "Emergence of simple-cell receptive field properties by learning a sparse code for natural images." Nature 381 (1996): 607-609. (See comments.)

Peters, R. J., F. Gabbiani, and C. Koch. "Human visual object categorization can be described by models with low memory capacity." Vision Research 43 (2003): 2265-2280.

Okada, M. "Notions of Associative Memory and Sparse Coding." Neural Netw 9 (1996): 1429-1458.


*Rolls, E. T, and M. J. Tovee. "Sparseness of the neuronal representation of stimuli in the primate temporal visual cortex." Journal of Neurophysiology 73 (1995): 713-726.

*Tsunoda, K., Y. Yamane, M. Nishizaki, and M. Tanifuji. "Complex objects are represented in macaque inferotemporal cortex by the combination of feature columns." Nature Neuroscience 4 (2001): 832-838.

Baddeley, R., L. F. Abbott, M. C. Booth, F. Sengpiel, T. Freeman, E. A. Wakeman, and E. T. Rolls. "Responses of neurons in primary and inferior temporal visual cortices to natural scenes." Proc R Soc Lond B Biol Sci 264 (1997): 1775-1783.

Young, M. P., and S. Yamane. "Sparse population coding of faces in the inferotemporal cortex." Science 256 (1992): 1327-1331.

Vinje, W. E., and J. L. Gallant. "Sparse coding and decorrelation in primary visual cortex during natural vision." Science 287 (2000): 1273-1276.

Weliky, M., J. Fiser, R. H. Hunt, and D. N. Wagner. "Coding of natural scenes in primary visual cortex." Neuron 37 (2003): 703-718.


Haxby, J. V., M. I. Gobbini, M. L. Furey, A. Ishai, J. L. Schouten, and P. Pietrini. "Distributed and overlapping representations of faces and objects in ventral temporal cortex." Science 293 (2001): 2425.

*Carlson, T. A., P. Schrater, and S. He. "Patterns of activity in the categorical representations of objects." Journal of Cognitive Neuroscience 15, no. 5 (2003): 704-717.

Spiridon, M., and N. Kanwisher. "How distributed is visual category information in human occipito-temporal cortex? An fMRI study." Neuron 35, no. 6 (2002): 1157-1165.

Cox, D. D., and R. L. Savoy. "Functional magnetic resonance imaging (fMRI) "brain reading": detecting and classifying distributed patterns of fMRI activity in human visual cortex." Neuroimage 19 (2003): 261-270.

Levy, I., U. Hasson, and R. Malach. "One picture is worth at least a million neurons." Curr Biol 14, no. 11 (2004): 996-1001.

Spatially Localized Representations and Maps:

Amazon logo Kanwisher, N. Chalupa, and Werner. The Visual Neurosciences. Cambridge, MA: MIT Press, November 2003, chapter 79. ISBN: 0262033089.

Kohonen, T., and R. Hari. "Where the abstract feature maps of the brain might come from." Trends in Neurosciences 22 (1999): 135-139.

Kohonen, T. "Cortical maps." Nature 346, no. 24 (1990).

Gawne, T. J., T. W. Kjaer, J. A. Hertz, and B. J. Richmond. "Adjacent visual cortical complex cells share about 20% of their stimulus-related information." Cereb Cortex 6 (1996): 482-489.


Neuronal Object Representations: Temporal Aspect of the Code

1) What is the latency of the neuronal representation (time lag)?

2) How many objects can be conveyed per second (capacity)?

3) Over what time scales is object information represented in the neuronal representation?
Empirical evidence from behavior, physiology and fMRI.

Required Readings

Nowak, L. G., and J. Bullier. "The timing of information transfer in the visual system." In Cerebral Cortex: Extrastriate Cortex in Primate. Edited by K. Rockland, J. Kaas, and A. Peters. New York, NY: Plenum Publishing Corporation, 1997.

Rousselet, G. A., S. J. Thorpe, and M. Fabre-Thorpe. "How parallel is visual processing in the ventral pathway?" Trends in Cognitive Sciences 8, no. 8 (2004): 363-370.

Supplemental Readings (Required to read at least two ‘to be presented’ papers, indicated by *)

Behavior / EEG:

*VanRullen, R., and S. J. Thorpe. "The time course of visual processing: from early perception to decision-making." Journal of Cognitive Neuroscience 13, no. 4 (2001): 454-461.

Physiology: Latency and Temporal Capacity (stimuli/sec):

*Keysers, C., D. K. Xiao, P. Foldiak, and D. I. Perrett. "The speed of sight." Journal of Cognitive Neuroscience 13, no. 1 (2001): 90-101.

*Sugase, Y., S. Yamane, S. Ueno, and K. Kawano. "Global and fine information coded by single neurons in the temporal visual cortex." Nature 400, no. 6747 (1999): 869-873.

Foldiak, P., D. Xiao, C. Keysers, R. Edwards, and D. I. Perrett. "Rapid serial visual presentation for the determination of neural selectivity in area STSa." Progress in Brain Research 144 (2004): 107-116.

Eifuku, S., W. C. De Souza, R. Tamura, H. Nishijo, and T. Ono. "Neuronal correlates of face identification in the monkey anterior temporal cortical areas." Journal of Neurophysiology 91, no. 1 (2004): 358-371.

Schmolesky, M. T., Y. Wang, D. P. Hanes, K. G. Thompson, S. Leutgeb, J. D. Schall, and A. G. Leventhal. "Signal timing across the macaque visual system." Journal of Neurophysiology 79 (1998): 3272-3278.

Gawne, T. J., T. W. Kjaer, and B. J. Richmond. "Latency: another potential code for feature binding in striate cortex." Journal of Neurophysiology 76 (1996): 1356-1360.

DiCarlo, J. J., and J. H. Maunsell. "Using neuronal latency to determine sensorymotor processing pathways in reaction time tasks." Journal of Neurophysiology. (In press.)

Rolls, E. T., and M. J. Tovee. "Processing speed in the cerebral cortex and the neurophysiology of visual masking." Proc R Soc Lond B Biol Sci 257 (1994): 9-15.

Kovacs, G., R. Vogels, and G. A. Orban. "Cortical correlate of pattern backward masking." Proceedings of the National Academy of Sciences U S A 92 (1995): 5587-5591.

Physiology: Information in the Temporal Structure of the Responses:

*Richmond, B. J., L. M. Optican, M. Podell, and Spitzer H. "Temporal encoding of two-dimensional patterns by single units in primate inferior temporal cortex. I. Response characteristics." Journal of Neurophysiology 57 (1987): 132-146.


Adaptation Effects: fMRI and Neurophysiology

1) Under what conditions and time scales do neural responses to visuallypresented objects adapt?

2) What are the mechanisms underlying such adaptation?

3) What do the answers to (1) and (2) say about the validity of fMRI adaptation as a tool for studying object representations?


Kourtzi, Z., and K. Grill-Spector. "fMR-adaptation: a tool for studying visual representations in the primate brain." In Fitting the Mind to the World: Aftereffects in High-Level Vision. Edited by Clifford, and Rhodes. New York, NY: Oxford University Press. (In press.)

Ringo, J. L. "Stimulus specific adaptation in inferior temporal and medial temporal cortex of the monkey." Behavioural Brain Research 76 (1996): 191-197.

Supplemental Readings (Required to read at least two ‘to be presented’ papers, indicated by *)


*Leopold, D. A., A. J. O’Toole, T. Vetter, and V. Blanz. "Prototype-referenced shape encoding revealed by high-level aftereffects." Nature Neuroscience 4, no. 1 (2001): 89-94.

Moradi, F., C. Koch, and S. Shimojo. "Face adaptation depends on seeing the face." Neuron 45, no. 1 (2005): 169-175.

Rhodes, G., L. Jeffery, T. L. Watson, E. Jaquet, C. Winkler, and C. W. Clifford. "Orientation-contingent face aftereffects and implications for face-coding mechanisms." Curr Biol 14, no. 23 (2004): 2119-2123.

Watson, T. L., and C. W. Clifford. "Pulling faces: an investigation of the face-distortion aftereffect." Perception 32, no. 9 (2003): 1109-1116.


*Lueschow, A., E. K. Miller, and R. Desimone. "Inferior temporal mechanisms for invariant object recognition." Cereb Cortex 4, no. 5 (1994): 523-531.

*Li, L., E. K. Miller, and R. Desimone. "The representation of stimulus familiarity in anterior inferior temporal cortex." Journal of Neurophysiology 69, no. 6 (1993): 1918-1929.

Kohn, A., and J. A. Movshon. "Adaptation changes the direction tuning of macaque MT neurons." Nature Neuroscience 7, no. 7 (2004): 764-772.

Suzuki, W. "The long and the short of it: Memory signals in the medial temporal lobe." Neuron 24 (1999): 295-298.

Muller, J. R., A. B. Metha, J. Krauskopf, and P. Lennie. "Rapid adaptation in visual cortex to the structure of images." Science 285, no. 5432 (1999): 1405-1408.


*Rotshtein, P., R. N. Henson, A. Treves, J. Driver, and R. J. Dolan. "Morphing Marilyn into Maggie dissociates physical and identity face representations in the brain." Nature Neuroscience 8, no. 1 (2005): 107-113.

Winston, J. S., R. N. Henson, M. R. Fine-Goulden, and R. J. Dolan. "fMRI-adaptation reveals dissociable neural representations of identity and expression in face perception." Journal of Neurophysiology 92, no. 3 (2004): 1830-1839.

Henson, R. N., A. Rylands, E. Ross, P. Vuilleumeir, and M. D. Rugg. "The effect of repetition lag on electrophysiological and haemodynamic correlates of visual object priming." Neuroimage 21, no. 4 (2004): 1674-1689.

Boynton, G. M., and E. M. Finney. "Orientation-specific adaptation in human visual cortex." Journal of Neuroscience 23, no. 25 (2003): 8781-8787.

Henson, R., T. Shallice, and R. Dolan. "Neuroimaging evidence for dissociable forms of repetition priming." Science 287, no. 5456 (2000): 1269-1272.

Kourtzi, Z., A. S. Tolias, C. F. Altmann, M. Augath, and N. K. Logothetis. "Integration of local features into global shapes: monkey and human FMRI studies." Neuron 37, no. 2 (2003): 333-346.


The Content of Neuronal Representations: Shape, Features, Curvature, Non-accidental Properties, …

1) What kinds of descriptors might be useful for object recognition from a computational perspective?

2) What aspects of object shape are coded in the ventral visual pathway? That is, what aspects of the shape are neurons/voxels most sensitive to?

Required Readings

Amazon logo Ullman. "Approaches to Visual Recognition." In Functional Neuroimaging of Visual Cognition. Edited by Kanwisher, and Duncan. A&P XX volume. Oxford; New York: Oxford University Press, 2004. ISBN: 0198528450.

Singh, M., and D. D. Hoffman. "Constructing and representing visual objects." Trends in Cognitive Sciences 1 (1997): 98-102.

Supplemental Readings (Required to read at least two ‘to be presented’ papers, indicated by *)

*Ullman, S., M. Vidal-Naquet, and E. Sali. "Visual features of intermediate complexity and their use in classification." Nature Neuroscience 5 (2002): 682-687.

*Brincat, S. L., and C. E. Connor. "Underlying principles of visual shape selectivity in posterior inferotemporal cortex." Nature Neuroscience 7 (2004): 880-886. (Please include background from Pasupathy and Connor 2001 in your presentation.)

Pasupathy, A., and C. E. Connor. "Shape representation in area V4: positionspecific tuning for boundary conformation." Journal of Neurophysiology 86 (2001): 2505-2519.

Pasupathy, A., and C. E. Connor. "Population coding of shape in area V4." Nature Neuroscience 5 (2002): 1332-1338.

Kayaert, G., I. Biederman, and R. Vogels. "Shape tuning in macaque inferior temporal cortex." Journal of Neuroscience 23 (2003): 3016-3027.

*Sary, G., R. Vogels, G. A. Orban. "Cue-invariant shape selectivity of macaque inferior temporal neurons." Science 260, no. 5110 (1993): 995-997.

Kobatake, E., and K. Tanaka. "Neuronal selectivities to complex object-features in the ventral visual pathway of the macaque cerebral cortex." Journal of Neurophysiology 71 (1994): 856-867.

Lerner, Y., T. Hendler, D. Ben-Bashat, M. Harel, and R. Malach. "A hierarchical axis of object processing stages in the human visual cortex." Cereb Cortex 11, no. 4 (2001): 287-297.

*Grill-Spector, K., T. Kushnir, S. Edelman, Y. Itzchak, and R. Malach. "Cue-invariant activation in object-related areas of the human occipital lobe." Neuron 21, no. 1 (1998): 191-202.

Op de Beeck, H., J. Wagemans, and R. Vogels. "Inferotemporal neurons represent low-dimensional configurations of parameterized shapes." Nature Neuroscience 4 (2001): 1244-1252.

Wilkinson, F., T. W. James, H. R. Wilson, J. S. Gati, R. S. Menon, and M. A. Goodale. "An fMRI study of the selective activation of human extrastriate form vision areas by radial and concentric gratings." Curr Biol 10, no. 22 (2000): 1455-1458.

Self, M. W., and S. Zeki. "The Integration of Colour and Motion by the Human Visual Brain." Cereb Cortex (December 22, 2004).

Kourtzi, Z., and N. Kanwisher. "Representation of perceived object shape by the human lateral occipital complex." Science 293, no. 5534 (2001): 1506-1509.


The Tolerances (invariances) of Neuronal Representations

Part 1: Affine Transformations: Position and Scale

1) What aspects of the retinal image are neurons/voxels able to ignore or otherwise discount or re-code?

2) Specifically, what is the tolerance to changes in object position and size.? Is position and size information discarded or somehow re-coded? Binding problem?

Required Readings

Amazon logo Ashbridge, E., and D. I. Perrett. "Generalizing across object orientation and size." In Perceptual Constancy. Edited by V. Walsh, and J. Kulikowski. Cambridge, UK: Cambridge University Press, 1998, pp. 192-209. ISBN: 0521460611.

Rolls, E. T. "Functions of the primate temporal lobe cortical visual areas in invariant visual object and face recognition." Neuron 27 (2000): 205-218.

Supplemental Readings (Required to read at least two ‘to be presented’ papers, indicated by *)


*Nazir, T. A., and J. K. O’Regan. "Some results on translation invariance in the human visual system." Spat Vis 5 (1990): 81-100.

Biederman, I., and E. E. Cooper. "Evidence for complete translational and reflectional invariance in visual object priming." Perception 20 (1991): 585-593.

———. "Size invariance in visual object priming." J Exp Psychol [Hum Percept and Perform] 18 (1992): 121-133.

Dill, M., and M. Fahle. "Limited translation invariance of human pattern recognition." Perception & Psychophysics 60 (1998): 65-81.

Some Theoretical Approaches:

*Riesenhuber, M., and T. Poggio. "Hierarchical models of object recognition in cortex." Nature Neuroscience 2 (1999): 1019-1025.

Olshausen, B. A., C. H. Anderson, and D. C. Van Essen. "A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information." Journal of Neuroscience 13 (1993): 4700-4719.

Salinas, E., and L. F. Abbott. "Invariant visual responses from attentional gain fields." Journal of Neurophysiology 77 (1997): 3267-3272.


Logothetis, N. K., J. Pauls, and T. Poggio. "Shape representation in the inferior temporal cortex of monkeys." Curr Biol 5 (1995): 552-563.

Tovée, M. J., E. T. Rolls, and P. Azzopardi. "Translation invariance in the responses to faces of single neurons in the temporal visual cortical areas of the alert monkey." Journal of Neurophysiology 72 (1994): 1049-1060.

*Ito, M., H. Tamura, I. Fujita, and K. Tanaka. "Size and position invariance of neuronal responses in monkey inferotemporal cortex." Journal of Neurophysiology 73 (1995): 218-226.

DiCarlo, J. J., and J. H. R. Maunsell. "Anterior Inferotemporal Neurons of Monkeys Engaged in Object Recognition Can be Highly Sensitive to Object Retinal Position." Journal of Neurophysiology 89 (2003): 3264-3278.

Op de Beeck, H., and R. Vogels. "Spatial sensitivity of macaque inferior temporal neurons." J Comp Neurol 426 (2000): 505-518.

Amazon logo Gross, C. G., and M. Mishkin. "The neural basis of stimulus equivalence across retinal translation." In Lateralization in the Nervous System. Edited by S. Harnad. New York, NY: Academic Press, 1977, pp. 109-122. ISBN: 0123257506.

Desimone, R., T. D. Albright, C. G. Gross, and C. Bruce. "Stimulus-selective properties of inferior temporal neurons in the macaque." Journal of Neuroscience 4 (1984): 2051-2062.


*Grill-Spector, K., T. Kushnir, S. Edelman, G. Avidan, Y. Itzchak, and R. Malach. "Differential processing of objects under various viewing conditions in the human lateral occipital complex." Neuron 24, no. 1 (1999): 187-203.

Andrews, T. J., and M. P. Ewbank. "Distinct representations for facial identity and changeable aspects of faces in the human temporal lobe." Neuroimage 23, no. 3 (2004): 905-913.


The Tolerances (invariances) of Neuronal Representations

Part 2: Pose, Illumination

1) What aspects of the retinal image are neurons/voxels able to ignore or otherwise discount or re-code?

2) Specifically, what is the tolerance to changes in object pose and illumination?

Required Readings

Tarr, M. J., and H. H. Bulthoff. "Image-based object recognition in man, monkey and machine." Cognition 67, no. 1-2 (1998): 1-20. (Review.)

Ullman, S., and E. Bart. "Recognition invariance obtained by extended and invariant features." Neural Network 17, no. 5-6 (2004): 833-848.

Supplemental Readings (Required to read at least two ‘to be presented’ papers, indicated by *)


Simons, D. J., R. F. Wang, and D. Roddenberry. "Object recognition is mediated by extraretinal information." Percept Psychophys 64 (2002): 521-530.

*Sinha, P., and T. Poggio. "Role of learning in three-dimensional form perception." Nature 384 (1996): 460-463.

Kourtzi, Z., and M. Shiffrar. "The visual representation of three-dimensional, rotating objects." Acta Psychol 102, no. 2-3 (1999): 265-292.


*Booth, M. C. A., and E. T. Rolls. "View-invariant representations of familiar objects by neurons in the inferior temporal visual cortex." Cerebral Cortex 8 (1998): 510-523.

*Logothetis, N. K., and J. P. Pauls. "Psychophysical and physiological evidence for viewer-centered object representation in the primate." Cerebral Cortex 5 (1995): 270-288.

Wang, G., M. Tanifuji, and K. Tanaka. "Functional architecture in monkey inferotemporal cortex revealed by in vivo optical imaging." Neurosci Res 32 (1998): 33-46.


*James, T. W., G. K. Humphrey, J. S. Gati, R. S. Menon, and M. A. Goodale. "Differential effects of viewpoint on object-driven activation in dorsal and ventral streams." Neuron 35, no. 4 (2002): 793-801.

Epstein, R., S. Higgins, and S. Thompson-Schill. "Learning places from views: Variation in scene processing as a function of experience and navigational ability. JOCN 17, no. 1 (2005): 73-83.

Vuilleumier, P., R. N. Henson, J. Driver, and R. J. Dolan. "Multiple levels of visual object constancy revealed by event-related fMRI of repetition priming." Nature Neuroscience 5 (2002): 491-499. (Rely on adaptation, but we have already covered that.)


Effects of Training/Experience on Object Representations

1) Should we view neuronal representations in IT/LOC as largely fixed or plastic?

2) What are the mechanisms that allow us to improve our object discrimination abilities?

3) Under what conditions and time scales do changes in behavior and neuronal representations take place?

4) Are the invariances of object representations learned or automatic?

Required Readings

Miyashita, Y. "Inferior temporal cortex: where visual perception meets memory." Ann Rev Neurosci 16 (1993): 245-263.

Supplemental Readings (Required to read at least two ‘to be presented’ papers, indicated by *)


*Wallis, G., and H. Bulthoff. "Learning to recognize objects." Trends in Cognitive Sciences 3 (1999): 22-31.

Foldiak, P. "Learning invariance from transformation sequences." Neural Comp 3 (1991): 194-200.

Karklin, Y., and M. S. Lewicki. "Learning higher-order structures in natural images." Network 14 (2003): 483-499.


*Furmanski, C. S., and S. A. Engel. "Perceptual learning in object recognition: object specificity and size invariance." Vision Research 40, no. 5 (2000): 473-484.

Gold, J., P. J. Bennett, and A. B. Sekuler. "Signal but not noise changes with perceptual learning." Nature 1402, no. 6758 (1999): 176-178.

Ahissar, M., and S. Hochstein. "The reverse hierarchy theory of visual perceptual learning." Trends in Cognitive Sciences 8, no. 10 (2004): 457-464.

Fabre-Thorpe, M., A. Delorme, C. Marlot, and S. Thorpe. "A limit to the speed of processing in ultra-rapid visual categorization of novel natural scenes." Journal of Cognitive Neuroscience 13, no. 2 (2001): 171-180.


Miyashita, Y. "Neuronal correlate of visual associative long-term memory in the primate visual cortex." Nature 335 (1988): 817-820.

Kobatake, E., G. Wang, and K. Tanaka. "Effects of shape-discrimination training on the selectivity of inferotemporal cells in adult monkeys." Journal of Neurophysiology 80 (1998): 324-330.

*Baker, C. I., M. Behrmann, and C. R. Olson. "Impact of learning on representation of parts and wholes in monkey inferotemporal cortex." Nature Neuroscience 5 (2002): 1210-1216.

Erickson, C. A., B. Jagadeesh, and R. Desimone. "Clustering of perirhinal neurons with similar properties following visual experience in adult monkeys." Nature Neuroscience 3 (2000): 1143-1148.

fMRI Papers:

*Op de Beeck, H., C. I. Baker, J. J. DiCarlo, and N. K. Kanwisher. (Submitted)

Janzen, G., and M. van Turennout. "Selective neural representation of objects relevant for navigation." Nature Neuroscience 7 (2004): 673-677.

Furmanski, C. S., D. Schluppeck, and S. A. Engel. "Learning strengthens the response of primary visual cortex to simple patterns." Current Biology 14 (2004): 573-578.

Gauthier, I., P. Skudlarski, J. C. Gore, and A. W. Anderson. "Expertise for cars and birds recruits brain areas involved in face recognition." Nature Neuroscience 3, no. 2 (2000): 191-197.

George, et. al. "Contrast polarity and face recognition in the human fusiform gyrus." Nature Neuroscience 2, no. 6 (1999): 574-804.


Attention and Object Recognition

1) How do neural representations of objects differ when the subject is versus is not attending to the object?

2) How does the brain process and represent multiple visual objects?

Required Readings

Amazon logo Treisman, A. "Psychological Issues in Selective Attention." In The Cognitive Neurosciences III. Edited by Gazzaniga. Cambridge, MA: MIT Press, c2004. ISBN: 0262072548.

Maunsell, J. H. R. "The brain’s visual world: representation of visual targets in cerebral cortex." Science 270 (1995): 764-769.

Other Optional Review Articles:

Itti, L., and C. Koch. "Computational modelling of visual attention." Nature Reviews Neuroscience 2 (2001): 194-203.

Desimone, R., and J. Duncan. "Neural mechanisms of selective visual attention." Annu Rev Neurosci 18 (1995): 193-222.

Amazon logo Freiwald, W. A., and N. Kanwisher. "Visual Selective Attention: insights from Brain Imaging and Neurophysiology." In The Cognitive Neurosciences III. Edited by Gazzaniga. Cambridge, MA: MIT Press, c2004. ISBN: 0262072548.

Supplemental Readings (Required to read at least two ‘to be presented’ papers, indicated by *)


*Chelazzi, L., E. K. Miller, J. Duncan, and R. Desimone. "A neural basis for visual search in inferior temporal cortex." Nature 363, no. 6427 (1993): 345-347.

*McAdams, C. J., and J. H. Maunsell. "Effects of attention on orientation-tuning functions of single neurons in macaque cortical area V4." Journal of Neuroscience 19 (1999): 431-441.

Motter, B. C. "Neural correlates of attentive s election for color or luminance in extrastriate area V4." Journal of Neuroscience 14 (1994): 2178-2187.

Connor, C. E., D. C. Preddie, J. L. Gallant, and D. C. Van Essen. "Spatial attention effects in macaque area V4." Journal of Neuroscience 17 (1997): 3201-3214.

Maunsell, J. H. R., and E. P. Cook. "The role of attention in visual processing." Philos Trans R Soc Lond B Biol Sci 357 (2002): 1063-1072.

Treisman, A. M., and N. G. Kanwisher. "Perceiving visually presented objects: recognition, awareness, and modularity." Curr Opin Neurobiol 8 (1998): 218-226.


*Murray, S. O., and E. Wojciulik. "Attention increases neural selectivity in the human lateral occipital complex." Nature Neuroscience 7, no. 1 (2004): 70-74.

Ress, D., B. T. Backus, and D. J. Heeger. "Activity in primary visual cortex predicts performance in a visual detection task." Nature Neuroscience 3, no. 9 (2000): 940-945.

*O’Craven, K. M., P. E. Downing, and N. Kanwisher. "fMRI evidence for objects as the units of attentional selection." Nature 401, no. 6753 (1999): 584-587.


Perceptual Awareness

1) How do neural representations of objects differ when we are aware of them and when we are not?

2) What are the neural necessary conditions for perceptual awareness? For example, what if anything is the role of top-down feedback in perceptual awareness?

3) What are the processing consequences of perceptual awareness?

Required Redings

Crick, F., and C. Koch. "A framework for consciousness." Nature Neuroscience 6 (2003): 119-126.

Dehaene, S., C. Sergent, and J. P. Changeux. "A neuronal network model linking subjective reports and objective physiological data during conscious perception." Proc Natl Acad Sci U S A 100, no. 14 (2003): 8520-8525.

Optional Readings

Hochstein, S., and M. Ahissar. "View from the top: hierarchies and reverse hierarchies in the visual system." Neuron 36 (2002): 791-804.

Kanwisher, N. "Neural Events and Perceptual Awareness." Cognition 79 (2001): 89-113.

Sewards, T. V., and M. A. Sewards. "On the neural correlates of object recognition awareness: relationship to computational activities and activities mediating perceptual awareness." Conscious Cogn 11 (2002): 51-77.

Supplemental Readings (Required to read at least two ‘to be presented’ papers, indicated by *)


*Logothetis, N. K., D. A. Leopold, D. L. Sheinberg. "What is rivalling during binocular rivalry?" Nature 380, no. 6575 (1996): 621-624.

*Kreiman, G., I. Fried, and C. Koch. "Single-neuron correlates of subjective vision in the human medial temporal lobe." Proc Natl Acad Sci U S A 99, no. 12 (2002): 8378-8383.

Sheinberg, D. L., N. K. Logothetis. "Noticing familiar objects in real world scenes: the role of temporal cortical neurons in natural vision." Journal of Neuroscience 21, no. 4 (2001): 1340-1350.


*Pasley, B. N., L. C. Mayes, and R. T. Schultz. "Subcortical discrimination of unperceived objects during binocular rivalry." Neuron 42, no. 1 (2004): 163-172.

*Marois, R., D. J. Yi, and M. M. Chun. "The neural fate of consciously perceived and missed events in the attentional blink." Neuron 41 (2004): 465-72.

Dehaene, S., L. Naccache, L. Cohen, D. L. Bihan, J. F. Mangin, J. B. Poline, and D. Riviere. "Cerebral mechanisms of word masking and unconscious repetition priming." Nature Neuroscience 4, no. 7 (2001): 752-758.

Silvanto, J., A. Cowey, N. Lavie, and V. Walsh. "Striate cortex (V1) activity gates awareness of motion." Nature Neuroscience, no. 2 (2005): 143-144.

Pegna, A. J., A. Khateb, F. Lazeyras, and M. L. Seghier. "Discriminating emotional faces without primary visual cortices involves the right amygdala." Nature Neuroscience 8, no. 1 (2005): 24-25.

DiCarlo, James, and Nancy Kanwisher. 9.916 The Neural Basis of Visual Object Recognition in Monkeys and Humans, Spring 2005. (Massachusetts Institute of Technology: MIT OpenCourseWare), http://ocw.mit.edu (Accessed 20 Feb, 2012). License: Creative Commons BY-NC-SA