Circuits for Intelligence

Research Thrust: Neural Circuits for Intelligence

Winrich FreiwaldThis extensive set of lectures provides a broad introduction to neuroscience, spanning the structure of neurons and neural circuits to the functional anatomy of the brain, and covering both empirical and computational approaches used to probe and model the behavior of neural circuits at multiple spatial and temporal scales. Lectures also describe the nature of neural signals and methods for decoding the information that these signals represent. Current research by CBMM faculty is highlighted, including work on visual processing in the ventral stream in support of tasks such as object recognition and face processing; the neural basis of visual attention, memory and spatial navigation; fMRI studies of the functional organization of the brain; and development of new methods for probing the behavior of ensembles of neurons.

Presentations

Gabriel Kreiman: Neurons and Models

Gabriel Kreiman: Neurons and Models

Topics: General features of brain-based computations, brain anatomy, structure of neurons, equivalent electrical circuit, synapses, single neuron models at multiple resolutions, integrate-and-fire model, Hodgkin-Huxley model; empirical methods used to study the brain at different spatial and temporal scales, lesion studies, visual illusions; brief overview of the CBMM research thrust on Circuits for Intelligence: face processing (Freiwald), interrogating neural circuits in the human brain (Kreiman), modulation of neural activity with light (Boyden/Desimone), electrode arrays (Boyden), and activity of neural ensembles (Desimone, Poggio, Wilson)

Jed Singer: Neural Coding

Jed Singer: Neural Coding

Topics: Characterizing neural firing rates, tuning curves, identifying effective stimuli, modeling spike trains, integrating information across time and across neurons, estimating response using reverse correlation, decoding fundamentals, two-way decoding, optimal decoding (Bayesian inference, ML, MAP), evaluating performance of an estimator, and kernel-based decoding from spike trains

Emily Mackevicius: Learning from a Computational Neuroscience Perspective

Emily Mackevicius: Learning from a Computational Neuroscience Perspective

Topics: Marr levels of analysis, types of learning (unsupervised, supervised, reinforcement), Hebb rule, LTP, correlation and covariance based learning, reinforcement learning, classical conditioning, conditioning paradigms, credit assignment problem, TD learning, model-free vs. model based learning; birdsong: behavior, how the brain produces song, refinement through reinforcement learning (Goldberg, Fee, J. Neurophysiology 2011)

Jim DiCarlo: Introduction to the Visual System, Part 1

Jim DiCarlo: Introduction to the Visual System, Part 1

Topics: Why study object recognition in the brain; comparison of behavior in humans and monkeys; overview of the ventral visual stream and the ventral (what) vs. dorsal (where) pathways; retinal receptive fields; simple and complex cells in V1; shape features that drive V4 responses; response properties of IT neurons; increase in size of receptive fields along the ventral stream; spatial organization of IT cortex; face patches; the challenge of recognition: image variation, object manifolds (DiCarlo, Cox, TICS 2007; Pinto, Cox, DiCarlo, PLoS Computational Biology 2008; DiCarlo, Zoccolan, Rust, Neuron 2012)

Jim DiCarlo: Introduction to the Visual System, Part 2

Jim DiCarlo: Introduction to the Visual System, Part 2

Topics: Decoding of IT signals for object classification (Poggio, DiCarlo, Science 2009); 3D object models; detection experiments with objects of different pose placed on random background images; neural population state space; LaWS of RAD IT decoding algorithm predicts human behavior (Majaj, Hong, Solomon, DiCarlo, Cosyne 2012); optogenetic experiments (Afraz, Boyden, DiCarlo, SfN 2013, VSS 2014); models of encoding visual input at various stages along the ventral stream, involving filter, threshold & saturate, pool, and normalize operations

Nancy Kanwisher: The Functional Architecture of Human Intelligence

Nancy Kanwisher: The Functional Architecture of Human Intelligence

Topics: Is the functional organization of the brain based on special-purpose vs. general-purpose machinery? Brief history of efforts to find specialized machinery (Spearman, Gall, lesion studies); introduction to fMRI methods and data; validation of fMRI through replication of physiological results; using fMRI to identify specialized face areas, with appropriate controls for other functions; response properties in the fusiform face area; selective cortical regions for color, scenes, movement, human body and parts, pitch, speech sounds, meaning of a sentence, theory of mind, complex thinking; future directions for fMRI studies

Ben Deen: Multivoxel Pattern Analysis for Understanding Representational Content

Ben Deen: Multivoxel Pattern Analysis for Understanding Representational Content

Topics: Motivation for multivoxel pattern analysis (MVPA); correlation based classification analysis; results of analysis of EBA and pSTS cortical regions: EBA patterns carry information about body pose that is invariant to body motion kinematics, pSTS patterns carry information about body motion kinematics that is invariant to body pose; examples of other MVPA results; application of different kinds of classifiers; pattern resolution; representational similarity analysis; RSA example: gaze direction codes

Winrich Freiwald: Taking Apart the Neural Circuits of Face Processing

Winrich Freiwald: Taking Apart the Neural Circuits of Face Processing

Topics: Connection of face recognition to intelligence: social cognition and perception start with faces, e.g. facial expression and communication; requirements for face recognition: (1) detect face, (2) encode structural properties, (3) encode dynamic properties, (4) extract different kinds of information (e.g. species, ethnicity, gender, age, identity, mood, direction of attention, attractiveness, likeability, trustworthiness), and (5) activate other parts of the brain to generate emotional response, activate a memory, draw attention, or elicit a motor response; early IT studies; fusiform face area; fMRI reveals face patches in the temporal lobe; response properties of middle face patch neurons; combining stimulation and fMRI to discover connectivity of face patches; connections of face network to lateral amygdala and pulvinar; development of face selectivity along a hierarchy that evolves from responses to faces in general, to faces in a particular location, to individual faces; connection to models of the ventral stream

Bob Desimone: Visual Attention

Bob Desimone: Visual Attention

Topics: Change blindness; receptive fields increase from V1 to IT; clutter is a challenge for recognition; one computational purpose of attention is biased competition; training an object classifier using recordings from IT in monkeys attending images of objects; normalization models: response is a weighted sum of all inputs with attention modeled as an increase in weights from the attended stimulus (e.g. Reynolds, Heeger, Neuron 2009; Ni, Ray, Maunsell, The New Cognitive Neurosciences 2012); biological mechanisms (Wilson, Runyan, Wang, Sur, PNAS 2012); evidence for role of synchrony (Singer) in spatial attention effects; study of dual recordings in FEF and V4 to test whether top-down inputs from FEF cause V4 synchrony (Gregoriou et al., Science 2009); feature attention; study of source of top-down object feature signals using MEG and fMRI in humans (Baldauf, Desimone, Science 2014) showing that FEF biases visual processing to different visual field locations and IFJ biases processing to different objects/features

Matt Wilson: Hippocampal memory reactivation in awake and sleep states

Matt Wilson: Hippocampal memory reactivation in awake and sleep states

Topics: Role of hippocampus in formation of episodic memory (linkage of events) and spatial memory used in navigation (linkage of spatial locations); both capabilities depend on temporal sequence encoding; place fields emerge for rats on linear tracks; hippocampal place cells; ensemble decoding; decoding sleep reactivation; interaction of asymmetric excitation with oscillatory variation in inhibition to translate space into time, suggested by hippocampal phase precession; hippocampal spatial representations encoded as sequences during behavior