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<span style="font-family:'Helvetica'; color:rgba(0, 0, 0, 1.0);"><b>From: </b></span><span style="font-family:'Helvetica';">Jillian Auerbach <<a href="mailto:jilliana@mit.edu">jilliana@mit.edu</a>><br>
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<span style="font-family:'Helvetica'; color:rgba(0, 0, 0, 1.0);"><b>Subject: </b>
</span><span style="font-family:'Helvetica';"><b>*New Subject Offerings in Brain and Cognitive Sciences*</b><br>
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<span style="font-family:'Helvetica'; color:rgba(0, 0, 0, 1.0);"><b>Date: </b></span><span style="font-family:'Helvetica';">January 5, 2018 at 2:59:01 PM EST<br>
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<b><span style="font-size: 11pt;">New!</span></b><span style="font-size: 11pt;"><span class="Apple-converted-space"> </span><b>BCS Offerings in Spring 2018</b><o:p></o:p></span></div>
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<span style="font-size: 11pt;">Special topics subject 9.S51<br>
*BCS Majors and Minors may count this class as a tier 2 subject towards the BCS program requirements*<br>
Title: <b>Animal Cognition</b><br>
Instructors: Professor Irene Pepperberg<br>
Schedule: TR 1-2:30pm in 46-3310<br>
Units: 3-0-9<br>
Prerequisites: 9.00<br>
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<span style="font-size: 11pt;">Description: <o:p></o:p></span></div>
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<span style="font-size: 11pt;">Whether animals think is no longer an issue; the questions are how do they think, how do their cognitive process compare to those of humans, and how do cognitive processes compare among various species? Exactly how “smart” are
animals? What do they know, how do we determine what they know, and how much of what they know is influenced by their ecological niche, their sensory capacities, and the way that humans design the tasks they are given? This course involves reading original
papers, discussing the pros and cons of the experiments and experimental methods, and learning as much as possible about animal behavior in a single semester. The topic of animal cognition now covers an immense range of topics and species. We will do little
more than scratch the surface…but the representative topics and papers should at least serve to excite interest in the field. <br>
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Special topics subject 9.S52 <br>
*BCS Majors and Minors may count this class as a tier 2 subject towards the BCS program requirements *<br>
Title: <b>Emergent Computation within Distributed Neural Circuits</b><br>
Instructors: Robert Ajemian, Research Scientist/McGovern Institiute <br>
Schedule: MWF 4-5pm in 46-3189: Please note that the first day of class will be held in 46-3015<br>
Units: 3-0-9<br>
Prerequisites: 9.40 or equivalent<o:p></o:p></span></div>
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<i><span style="font-size: 11pt;">This class is geared towards undergraduate upper-class students in BCS, EECS, and related departments who have taken <b>9.40</b> (or the equivalent) and wish to understand elements of brain-inspired computing in comparison
to techniques in state-of-the-art artificial intelligence. <o:p></o:p></span></i></div>
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<span style="font-size: 11pt;">Description:<o:p></o:p></span></div>
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<span style="font-size: 11pt;">Brains and computers process information in radically different ways, with the constitutive elements of brains -- neurons and synapses -- exhibiting decidedly inferior performance characteristics, in terms of transmission speed,
clock rate, signal-to-noise ratio, etc. Yet somehow, brains still outperform the best computer algorithms in most domains of sensory, motor, and cognitive function. Here we explore the emergent computational mechanisms and principles by which neural ensembles
collectively instantiate remarkable behavioral competencies, despite the inherent limitations of biological wetware. This complex-systems perspective provides important insights in the domains of both neurobiology and neuro-inspired artificial intelligence.
On the neurobiological side, we look to identify signatures of the proposed computational mechanisms in actual neurophysiological data; on the artificial intelligence side, we seek to gain an understanding of which biological motifs have been successfully
utilized in neural networks and which have not.<o:p></o:p></span></div>
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<span style="font-size: 11pt;">Subject 9.11<br>
Tier 2 <br>
Title: <b>The Human Brain</b><br>
Instructors: Professor Nancy Kanwisher<br>
Schedule: MW 11-12:30pm in Bldg 46 Room 1015, (possible recitation tbd)<br>
Units: 3-0-9<br>
Prerequisites: 9.00 or 9.01; or permission of instructor<br>
Description:<br>
The last quarter century has revealed the functional organization of the human brain in glorious detail, including an unexpectedly precise mapping of specific perceptual and cognitive functions to particular brain regions. This course surveys the core perceptual
and cognitive abilities of the human mind and asks how these abilities are implemented in the brain. Key themes include the representations, development, connectivity, interspecies homologies, and degree of functional specificity of particular brain regions.
The course also emphasizes the methoods available in human cognitive neuroscience, and what inferences can and cannot be drawn from each<br>
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Subject 9.60<br>
Institute Lab and CI-M<br>
Title: <b>Machine Motivated Human Vision</b><br>
Instructors: Professor Pawan Sinha<br>
Schedule: TR 11-12:30pm in Bldg 46 Room 3015<br>
Units: 2-1-9<br>
Prerequisites: 9.00, 9.07<br>
Description:<br>
Explores how studies of human vision can be motivated by, and enhance the capabilities of, machine-based systems. Considers the twin questions of how the performance of state-of-the-art machine vision systems compares with that of humans, and what kinds of
strategies the human visual system uses in tasks where human performance exceeds that of machines. Includes presentations by engineers from companies with significant engineering efforts in vision. Based on these presentations, students define and conduct
studies to address the two aforementioned questions and present their results to the public at the end of the term. Directed towards students interested in exploring vision from computational, experimental and practical perspectives. Provides instruction and
practice in written and oral communication. <o:p></o:p></span></div>
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