Computing motion on the basis of the time-varying image intensity is a difficult problem for both artificial and biological vision systems. We will show how one well-known gradient-based computer algorithm for estimating visual motion can be implemented within the primate's visual system. This relaxation algorithm computes the optical flow field by minimizing a variational functional of a form commonly encountered in early vision, and is performed in two steps. In the first stage, local motion is computed, while in the second stage spatial integration occurs. Neurons in the second stage represent the optical flow field via a population-coding scheme, such that the vector sum of all neurons at each location codes for the direction and magnitude of the velocity at that location. The resulting network maps onto the magnocellular pathway of the primate visual system, in particular onto cells in the primary visual cortex (V1) as well as onto cells in the middle temporal area (MT). Our algorithm mimics a number of psychophysical phenomena and illusions (perception of coherent plaids, motion capture, motion coherence) as well as electrophysiological recordings. Thus, a single unifying principle ‘the final optical flow should be as smooth as possible’ (except at isolated motion discontinuities) explains a large number of phenomena and links single-cell behavior with perception and computational theory.
Bullfrog ganglia contain two classes of neurone, B and C cells, which receive different inputs and exhibit different slow synaptic potentials. B cells, to which most effort has been directed, possess slow and late slow EPSPs. The sEPSP reflects a muscarinic action of acetylcholine released from boutons on B cells, whereas the late sEPSP is caused by a peptide (similar to teleost LHRH) released from boutons on C cells. During either sEPSP there is a selective reduction in two slow potassium conductances, designated ‘M’ and ‘AHP’. The M conductance is voltage dependent and the AHP conductance is calcium dependent. Normally they act synergistically to prevent repetitive firing of action potentials during maintained stimuli. Computer stimulation of the interactions of these conductances with the other five voltage-dependent conductances present in the membrane allows a complete reconstruction of the effects of slow synaptic transmission on electrical behaviour.