Midterm 3

feedforward connections from LGN to V1, feedforward connections to V2, feedback connections from V2 to V1

rod and cone receptors -> horizontal cells -> bipolar cells -> ganglion cell…out to optic nerve fibers and then to LGN in thalamus. Process through neural spikes via light

sends info to motor/cerebellum area. It is the ‘inner eyes’

1) rod and cone cells (light detection) 2) horizontal and bipolar cells (preprocessing) 3) ganglion cells (preprocessing) 4) Lateral geniculate nucleus (relay station) 5) visual cortex (conscious perception)

rods: discriminate between brightness in low illumination . contribute to peripheral vision cones: discriminate colors, contribute to central vision

400nm (violet) - 700nm (red)

S-cones (blue) M-cones (green) L-cones (red)

evolutionarily advantageous to be able to distinguish between a wide range of colors. Since there is overlap of the wavelengths for the cones, they combine to create different colors

how does the brain combine different sensory features into one unified, coherent object

• receptive field • on-center/off-surround RF • edge detection • orientation detector • location-invarient orientation detector

the area of retina cells that trigger activity of that neuron

on-center cells - the surround ganglion cells are inhibitory and dampen the signal of the center ganglion cell. Most excitatory activation (biggest response) when light is concentrated in the center of the receptive field off-surround cells have the opposite pattern -> most activation when light concentrated in surround illumination

the on-center/off-surround RF is able to detect the change in light intensity which creates an edge

a neuron has a preferred orientation selectivity - respond to (detect) only a relatively narrow range of orientations graded responses for nearby orientations (neighboring cells share similar orientations)

first detected in a cat where the excitatory vs inhibitory area of a selective neuron corresponds to the orientation preference David Hubel and Torsten Wiesel used single-cell recordings in primary visual cortex of cats Got nobel prize in physiology/medicine

‘Boolean AND’ neuron (all LGN cells need to be activated to activate cortical cell). selective cells in the retina are organized according to their orientation.

detecting motion of the same object. Many receptive fields of ganglion cells work together and go to LGN neurons which send signals to cortical simple cells and then activate the cortical complex cell. Boolean OR computation

Retina -> LGN -> VA -> V2…-> IT -> anterior IT in anterior IT learning generalizes over orientation, location, and form

Brains are parallel, distributed, analog computers, not based on symbolic logic

david rumelhart

yes, it can solve any computable problem Further, if equipped with appropriate algorithms, the neural network can be made into an intelligent computing machine that solves problems in fast and finite time.

– Backpropagation learning rule (D. Rumelhart et al, 1986) – Hebbian learning rule (1949) – Kohonen’s self-organizing feature map (1982) – Boltzman machine (1986) – Deep learning network (G. Hinton et al, 2006)

geoffrey hinton & john hopfield content-addressable memory and deep belief nets

traditional: 1950-2008 (symbolic system models) modern: 2008-present (connectionist/neural networks)