The Brain and Computation Boot Camp
2018-01-16..2018-01-19
Berkeley, United States
who it's for
computational neuroscientists, systems neuroscientists, connectomics researchers, neural modelers, neurophysiologists, neuromechanics researchers, data engineers for neuroscience
topics
Brainbow multicolor labeling; serial electron microscopy; 61-beam multisem imaging; automatic tape-collecting ultramicrotome; connectome graph extraction; Allen Institute large-scale datasets; Patch-seq linked datasets; NEURON biophysical models; dendritic NMDA spikes; two-layer neuron models; short-term synaptic plasticity (UDF); thalamocortical core versus matrix
| title | author | length |
|---|---|---|
| Mapping Neural Connections and Their Development I | Jeff Lichtman | 01h 02m |
Argues that mapping actual wiring requires Brainbow multicolor labeling plus serial electron microscopy and massive computer-assisted image analysis, not… | ||
| Mapping Neural Connections and Their Development II | Jeff Lichtman | 01h 12m |
Claimed ability to map every wire in a volume using an automatic tape‑collecting ultramicrotome plus a 61‑beam multisem SEM that generates ~11 TB/day. Pipeline… | ||
| High Throughput Connectomics | Nir Shavit | 01h 32m |
Project aims to produce petabyte-to-gigabyte reductions by extracting connectomes at the pace of imaging for a cubic millimeter (two petabytes) and ultimately… | ||
| Allen Institute Large-scale Datasets and Modeling Tools | Anton Arkhipov | 01h 36m |
Allen Institute provides large public datasets and runnable models: ~100 mouse cortical cell types from ~20,000 sequenced cells and 45,000+ recorded cells in… | ||
| Biophysics of Computation I | Bartlett Mel | 01h 02m |
Claim: many cortical neurons are not single “point” integrators but contain voltage privacy in long dendritic branches that act as independent nonlinear… | ||
| Biophysics of Computation II | Bartlett Mel | 01h 08m |
Glutamate uncaging plus detailed compartmental simulations show dendrites act as location-dependent nonlinear subunits rather than simple adders: two spatially… | ||
| Computation in Networks of Neurons in the Brain I | Wolfgang Dr. Maass (University of St. Gallen) | 01h 01m |
Argues that networks of spiking neurons can simulate any Turing machine and implement Boltzmann-like models to perform sampling-based inference, but only under… | ||
| Computation in Networks of Neurons in the Brain II | Wolfgang Dr. Maass (University of St. Gallen) | 01h 13m |
Argues that diverse neuron and synapse properties — documented in Allen Institute data and summarized by the three-parameter UDF model for short‑term… | ||
| Thalamocortical System I | Murray Sherman | 01h 02m |
Thalamus acts as a gate: relay nuclei send topographic input to cortex while a diffuse core vs matrix system projects differently; the thalamic reticular… | ||
| Thalamocortical System II | Murray Sherman | 01h 04m |
Interneurons in thalamus send most outputs from dendritic outputs that form glomerular triads where a sensory axon excites a relay cell and, at higher firing… | ||
| The Brain and Body Compute Together: Neuromechanics and Sensorimotor Control | Lena Ting | 01h 31m |
Claim: balance and gait emerge from a low‑dimensional library of motor modules rather than single muscle rules. Evidence comes from moving platform… | ||
| Neural Mechanisms of Vision | Bruno Olshausen | 01h 34m |
Estimate of eye evolution is ~500,000 years from simple photoreceptors to an imaging eye after ~176 incremental steps, retinal output is ~1.5 million optic… | ||
| Theory of Computation I | Christos Papadimitriou | 01h 01m |
Overview mixes history and concrete results: 1936 Turing machine definition and the proof that some simple problems are unsolvable; von Neumann’s 1946… | ||
| Theory of Computation II | Christos Papadimitriou | 01h 07m |
Perceptron learning gets a provable mistake bound of one over margin squared and becomes practical via the kernel trick that maps nonlinearly separable data… | ||
| Unsupervised Learning | Santosh Vempala | 01h 31m |
Proven results and concrete limits: polynomial algorithms recover Gaussian-mixture parameters but sample complexity currently blows up with number of… | ||
| Bayesian Theories of Perception and Cognition | Peggy Seriès | 01h 38m |
Argues that perception uses probabilistic inference, tests it with psychophysics plus Bayesian model fitting to extract priors and likelihoods, and reproduces… | ||
| nothing matches | ||