PATTERN-MATCHING IN A MODEL OF DENDRITIC SPINES

Pattern matching, the ability to recognize and maximally respond to an input pattern that is similar to a previously learned pattern, is an essential step in any learning process. To investigate the properties of pattern matching in biological neurons, and in particular the role of a calcium-dependent potassium conductance, a circuit model of a sma ll area of dendritic membrane with a number of dendritic spines is dev eloped. Circuit model simulations show that dendritic membrane depolar ization is greater in response to a previously learned pattern of syna ptic inputs than in response to a novel pattern of synaptic inputs. Th ese simulations, in combination with an analysis of the circuit model equations, reveal that when a synaptic input pattern is similar to the learned pattern of synaptic inputs, the total dendritic depolarizatio n is a linear combination of dendritic depolarization contributed by i ndividual spines. When at least one synaptic input differs markedly fr om the learned value, dendritic depolarization is at nonlinear combina tion of individual spine depolarizations. These principles of spine in teractions are captured in a computationally simple set of 'similarity measure' equations which are shown to reproduce the response surface of the circuit model output. Thus, these similarity measure equations not only describe a biologically plausible model of pattern matching, they also satisfy computational requirements for use in artificial neu ral networks.