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theoretical neuroscience computational mathematical modeling neural systems peter dayan lf abbott cambridge mit press 2001 576 pp 5000 upon time experimental neurobiologist asked whether thought role theory neurobiology answered resounding explained reasoning giving answer irony story experimentalist theory role theory serious note experimental research activity planning experiment interpreting data requires mind abstract representation studied phenomenon representation matter simplistic complex theory indispensable coordinated research effort undoubtedly role theory neurobiology real question facing neurobiology today whether role theorists although theorist obviously think answer yes may evident everyone argument favor theorists based division labor concept professional millers bakers results better cheaper bread professional theorists experimentalists likely result higher quality papers efficient research theorists role science create abstract representation studied phenomenon valid representation must logically selfconsistent relevant representation must explain experimental observations make experimentally testable predictions often challenging requirements demand fulltime effort scientist thus supporting division labor argument however division labor may always advantageous even physics theorists wellaccepted role today situation different two hundred years ago scientist source theoretical experimental skills work drove division labor physics everincreasing sophistication experiment theory figure 1 one tell whether neurobiology reached level sophistication requiring professional theorists sophistication theory reflected mainly extensive use mathematics natural sciences mathematics incredibly useful precisely formulating communicating ideas therefore building theories take advantage mathematical theories two conditions must met construction mathematical theory requires existence fundamental principles testing mathematical theory requires quantitative experimental measurements subfields neurobiology conditions beginning met moreover immense complexity brain guarantees sophistication neurobiology keep growing thus driving demand theorists makes future theoretical neuroscience rather bright someone learn craft theoretical neurobiology first generation theoretical neuroscientists came mostly physics background physics theoretical culture exists craft learned students courses textbooks become selfperpetuating field neuroscience develop theoretical culture fundamental texts already several excellent books covering various specialized topics theoretical neurobiology example spikes exploring neural code f rieke et al mit press 1996 representation sensory signals neural spike trains using information theory biophysics computation information processing single neurons c koch oxford university press 1998 combining biophysical computational approaches neuronal function recently published book peter dayan lf abbott theoretical neuroscience computational mathematical modeling neural systems attempts accomplish cover one specialized topic attempts cover whole field theoretical neuroscience think authors mostly succeeded herculean task book set high standard theoretical neuroscience defines field book three parts loosely corresponding three levels description brain function defined relation action potential spikes part addresses information encoded neuronal spike trains decoded theoretical framework grew experiments extracellular recordings single neurons relating recordings sensory input motor output represents midlevel description brain function part ii devoted modeling biophysical properties neurons synapses theory closely related experiments intracellular recordings describes physical processes going spike generation represents lowlevel description also discusses modeling networks spiking neurons part iii discusses field machine learning application neurobiology relates experiments behavioral representational learning highlevel description brain function necessarily rely existence action potentials peter dayan larry abbott exceptionally strong productive theoretical neuroscientists book certain firsthand quality authors clearly thought subjects personally contributed many manifests elegant concise presentations example sections information theory neuroelectronics presentation many neuroscience topics combines deep intuition mathematical rigor balance two generally well maintained reader assumed mathematical background supplemented mathematical appendices importantly book demonstrates tools found toolbox theoretical neurobiologist today great thing book knowledge accumulated theoretical neuroscience collected one cover using uniform notation style presentation one search original articles try translate different notation systems topics particularly benefited systematic approach visual receptive fields synaptic plasticity rules classical conditioning although ordering topics deductive may expected theoretical physics textbook probably characteristic state theoretical neuroscience yet discover unifying principles would make deductive presentation possible authors took unusual step making book freely available internet publication think right thing reduction sales caused free availability must offset positive effect generated publicity importantly step eliminated temporarily financial barrier access scientific information often penalizes scientists economically disadvantaged countries theoretical neuroscience well textbook advanced undergraduate graduate courses covers wide range topics offers pedagogical presentation internetbased exercises guidance reading necessary mathematical background presented appendices throughout book end chapters selfcontained allowing reshuffling instructor although book gives neurobiology background authors recommend readers new field refer general neuroscience textbook although book excellent job covering aspects theoretical neuroscience touches perhaps lightly anatomy neuronal circuits importantly physical biological constraints shaping example widespread use learning rules developmental models often ignores fact neuronal circuits must wired physical world thus subject constraints valuable resources space time energy electrical engineer designing modern computers aware constraints wouldnt nature deal believe need study understand constraints need understanding constraints emphasized shortcomings many computational models brain function often existing computational models focus single feature studied system ignoring fact system performs great variety tasks makes problem building model underconstrained thus allowing multiple solutions example many different developmental models cortical map formation overcome would helpful understand constraints imposed biological hardware studies biophysical constraints likely lead discovery engineering principles like laws conservation physics rule many incorrect candidate solutions allow theoretical neuroscientists narrow set possible solutions better focus efforts overall theoretical neuroscience excellent book strongly recommend anyone seriously interested learning field book top choice anyone teaching course theoretical neuroscience