2. Neurons and Conductance-Based Models Fundamentals of Computational Neuroscience, T. P . Trappenberg, 2010. Lecture Notes on Brain and Computation Byoung-Tak Zhang Biointelligence Laboratory School of Computer Science and Engineering Graduate Programs in Cognitive Science, Brain Science and Bioinformatics Brain-Mind-Behavior Concentration Program Seoul National University 1 E-mail: btzhang@bi.snu.ac.kr This material is available online at http://bi.snu.ac.kr/
Outline 2.1 Modeling biological neurons 2.2 Neurons are specialized cells 2.3 Basic synaptic mechanisms 2.4 The generation of action potentials: Hodgkin-Huxley equations 2.5 Dendritic trees, the propagation of action potentials, and compartmental models 2.6 Above and beyond the Hodgkin-Huxley neuron: fatigue, bursting, and simplifications 2 (C) 2009-2011 SNU Biointelligence Lab, http://bi.snu.ac.kr (C) 2009 SNU CSE Biointelligence Lab
2.1 Modeling biological neurons The networks of neuron-like elements The heart of many information-processing abilities of brain The working of single neurons Information transmission Simplified versions of the real neurons Make computations with large numbers of neurons tractable Enable certain emergent properties in networks Nodes The sophisticated computational abilities of neurons The computational approaches used to describe single neurons 3 (C) 2009-2011 SNU Biointelligence Lab, http://bi.snu.ac.kr (C) 2009 SNU CSE Biointelligence Lab
2.2 Neurons are specialized cells 4 (C) 2009-2011 SNU Biointelligence Lab, http://bi.snu.ac.kr (C) 2009 SNU CSE Biointelligence Lab
2.2.1 Structural properties (1) Fig. 2.1 5 (C) 2009-2011 SNU Biointelligence Lab, http://bi.snu.ac.kr (C) 2009 SNU CSE Biointelligence Lab
2.2.1 Structural properties (2) Morphologies of different neurons Pyramidal cell from the motor cortex (B) Granule neuron from olfactory bulb (C) Spiny neuron from the caudate nucleus (D) Golgi-stained Purkinje cell from the cerebellum (E) Fig. 2.1 6 (C) 2009-2011 SNU Biointelligence Lab, http://bi.snu.ac.kr (C) 2009 SNU CSE Biointelligence Lab
2.2.2 Information-processing mechanisms Neurons can receive signals from many other neurons Synapses (contact site) Presynaptic (from axon) Postsynaptic (to dendrite or cell body) Signal = action potential Electronic pulse 7 (C) 2009-2011 SNU Biointelligence Lab, http://bi.snu.ac.kr (C) 2009 SNU CSE Biointelligence Lab
2.2.3 Membrane potential V m Membrane potential The difference between the electric potential within the cell and its surrounding 8 (C) 2009-2011 SNU Biointelligence Lab, http://bi.snu.ac.kr (C) 2009 SNU CSE Biointelligence Lab
2.2.4 Ion channels (1) The permeability of the membrane to certain ions is achieved by ion channels Fig. 2.2 9 (C) 2009-2011 SNU Biointelligence Lab, http://bi.snu.ac.kr (C) 2009 SNU CSE Biointelligence Lab
2.2.4 Ion channels (2) Major ion channels Pump: use energy Channel: use difference of ions concentration 10 (C) 2009-2011 SNU Biointelligence Lab, http://bi.snu.ac.kr (C) 2009 SNU CSE Biointelligence Lab
2.2.4 Ion channels (3) V rest 65 mV Resting potential 11 (C) 2009-2011 SNU Biointelligence Lab, http://bi.snu.ac.kr (C) 2009 SNU CSE Biointelligence Lab
Supplement: Equilibrium potential and Nernst equation 12 (C) 2009-2011 SNU Biointelligence Lab, http://bi.snu.ac.kr (C) 2009 SNU CSE Biointelligence Lab
2.3 Basic synaptic mechanisms Signal transduction within the cell is mediated by electrical potentials. Electrical synapse or gap-junctions Chemical synapse Synaptic plasticity Fig. 2.3 13 (C) 2009-2011 SNU Biointelligence Lab, http://bi.snu.ac.kr (C) 2009 SNU CSE Biointelligence Lab
2.3.1 Chemical synapses and neurotransmitters Neurotransmitters stored in synaptic vesicles glutamate (Glu) gamma-aminobutyric acid (GABA) Dopamine (DA) acetylcholine (ACh) Synaptic cleft (a small gap of only a few micrometers) Receptor (channel) and postsynaptic potential (PSP) 14 (C) 2009-2011 SNU Biointelligence Lab, http://bi.snu.ac.kr (C) 2009 SNU CSE Biointelligence Lab
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2.3.2 Excitatory and inhibitory synapses Different types of neurotransmitters Excitatory synapse PSP: depolarization Neurotransmitters trigger the increase of the membrane potential Neurotransmitter: Glu, ACh Inhibitory synapse PSP: hyperpolarization Neurotransmitters trigger the decrease of the membrane potential Neurotransmitter: GABA Non-standard synapses Influence ion channels in an indirect way Modulation 17 (C) 2009-2011 SNU Biointelligence Lab, http://bi.snu.ac.kr (C) 2009 SNU CSE Biointelligence Lab
Excitatory postsynaptic potential (EPSP) resulting from non-NMDA receptors peak non NMDA t / t V w t e m w : amplitude factor strength of EPSP or efficiency of the synapse f ( t ) =t·exp ( -t ): α -function functional form of a PSP Inhibitory postsynaptic potential (IPSP) resulting from non-NMDA receptors peak non NMDA t / t Fig. 2.4 V w t e m EPSP resulting from NMDA receptor t / t / NMDA V c ( V ) e e 1 2 m m 18 (C) 2009-2011 SNU Biointelligence Lab, http://bi.snu.ac.kr (C) 2009 SNU CSE Biointelligence Lab
2.3.4 Superposition of PSP Electrical potentials have the physical property They superimpose as the sum of individual potentials. Linear superposition of synaptic input Nonlinear voltage-current relationship Nonlinear interaction Divisive Shunting inhibition 19 (C) 2009-2011 SNU Biointelligence Lab, http://bi.snu.ac.kr (C) 2009 SNU CSE Biointelligence Lab
2.4 The generation of action potentials Spike or action potential (AP) Voltage-dependent sodium channel Start rising phase Neurotransmitter-gated ion channels Fig. 2.5 Depolarize Voltage-dependent sodium channels Influx of Na+ Falling phase Hyperpolarization The sodium channels inactive Potassium channels open Fig. 2.6 20 (C) 2009-2011 SNU Biointelligence Lab, http://bi.snu.ac.kr (C) 2009 SNU CSE Biointelligence Lab
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2.4.3 Hodgkin-Huxley equations (1) Quantified the process of spike generation I g ( V E ) Input Current ion ion ion Electric conductance g ion Membrane potential relative to V the resting potential Equilibrium potential E ion Fig. 2.7 K+, Na+ conductance dependent 4 g g n n , The activation of the K channel K K m , The activation of the Na channel h , The inactivation of the Na channel 3 g g m h Na Na 22 (C) 2009-2011 SNU Biointelligence Lab, http://bi.snu.ac.kr (C) 2009 SNU CSE Biointelligence Lab
2.4.3 Hodgkin-Huxley equations (2) n, m, h have the same form of first-order differential-equation x should be substituted by each of the variables n, m and h t x ( V ) dt [ x - x 0 ( V )] = - dx 1 Fig. 2.8 23 (C) 2009-2011 SNU Biointelligence Lab, http://bi.snu.ac.kr (C) 2009 SNU CSE Biointelligence Lab
2.4.3 Hodgkin-Huxley equations (3) Hodgkin-Huxley model C , capacitance I ( t ), external current dV C I I ( t ) ion dt ion Three ionic currents Fig. 2.7 dV 4 3 C g n ( V E ) g m h ( V E ) g ( V E ) I ( t ) K K Na Na L L dt dn [ n n ( V )] n 0 dt dm [ m m ( V )] m 0 dt dh [ h h ( V )] h 0 dt 24 (C) 2009-2011 SNU Biointelligence Lab, http://bi.snu.ac.kr (C) 2009 SNU CSE Biointelligence Lab
2.4.4 Numerical integration A. A Hodgkin-Huxley neuron responds with constant firing to a constant external current. B. The dependence of the firing rate with the external current (nonlinear curve). (dashed line: noise added) Fig. 2.9 25 (C) 2009-2011 SNU Biointelligence Lab, http://bi.snu.ac.kr (C) 2009 SNU CSE Biointelligence Lab
2.4.5 Refractory period Absolute refractory period The inactivation of the sodium channel makes it impossible to initiate another action potential for about 1ms. Limiting the firing rates of neurons to a maximum of about 1000Hz Relative refractory period Due to the hyperpolarizing action potential it is relatively difficult to initiate another spike during this time period. Reduced the firing frequency of neurons even further 26 (C) 2009-2011 SNU Biointelligence Lab, http://bi.snu.ac.kr (C) 2009 SNU CSE Biointelligence Lab
2.4.6 Propagation of action potentials 27 (C) 2009-2011 SNU Biointelligence Lab, http://bi.snu.ac.kr (C) 2009 SNU CSE Biointelligence Lab
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