Multiplexed coding in Purkinje neuron dendrites (Zang and De Schutter 2021)

 Download zip file 
Help downloading and running models
Neuronal firing patterns are crucial to underpin circuit level behaviors. In cerebellar Purkinje cells (PCs), both spike rates and pauses are used for behavioral coding, but the cellular mechanisms causing code transitions remain unknown. We use a well-validated PC model to explore the coding strategy that individual PCs use to process parallel fiber (PF) inputs. We find increasing input intensity shifts PCs from linear rate-coders to burst-pause timing-coders by triggering localized dendritic spikes. We validate dendritic spike properties with experimental data, elucidate spiking mechanisms, and predict spiking thresholds with and without inhibition. Both linear and burst-pause computations use individual branches as computational units, which challenges the traditional view of PCs as linear point neurons. Dendritic spike thresholds can be regulated by voltage state, compartmentalized channel modulation, between-branch interaction and synaptic inhibition to expand the dynamic range of linear computation or burst-pause computation. In addition, co-activated PF inputs between branches can modify somatic maximum spike rates and pause durations to make them carry analogue signals. Our results provide new insights into the strategies used by individual neurons to expand their capacity of information processing.
1 . Zang Y, De Schutter E (2021) The Cellular Electrophysiological Properties Underlying Multiplexed Coding in Purkinje Cells. J Neurosci [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Dendrite; Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Cerebellum;
Cell Type(s): Cerebellum Purkinje GABA cell;
Channel(s): I T low threshold; I Na,p; I h; I Potassium; I Sodium; I p,q; I K,Ca;
Gap Junctions:
Simulation Environment: NEURON;
Model Concept(s): Dendritic Action Potentials; Detailed Neuronal Models; Synaptic Integration; Temporal Coding; Reaction-diffusion;
Implementer(s): Zang, Yunliang ;
Search NeuronDB for information about:  Cerebellum Purkinje GABA cell; I Na,p; I T low threshold; I p,q; I h; I K,Ca; I Sodium; I Potassium;
//Notice the difference between Basketball synaptic inputs and others
numsegsp = 0 // will be total number of segments
forsec spinydend {numsegsp+=nseg}
numsegbs = 0
forsec bs {numsegbs+=nseg}

objref mvecpf,mvecst,mvecbs
mvecpf = new Vector(numsegsp) // will hold cumulative sums of segment length
mvecst = new Vector(numsegsp)
mvecbs = new Vector(numsegbs)

// each element in mvecpf corresponds to a segment in seclist

ii = 0 // to iterate over mvecpf
mtotalpf = 0 // will be total length in seclist
forsec spinydend {
  for (x,0) { // iterate over internal nodes of current section
    mtotalpf += L/nseg // or area(x) if density is in (number)/area
    mvecpf.x[ii] = mtotalpf
    ii += 1

mvecst = mvecpf.c

ii = 0 // to iterate over mvecpf
mtotalbs = 0 // will be total length in seclist
forsec bs {
  for (x,0) { // iterate over internal nodes of current section
    mtotalbs += L/nseg // or area(x) if density is in (number)/area
    mvecbs.x[ii] = mtotalbs
    ii += 1

objref nvecpf, nvecst, nvecbs

objref ampsynlist,gabastsynlist,gababssynlist
objref ampsynprelist,gabastsynprelist,gababssynprelist

objref ampcon[300000]
objref gabastcon[300000]
objref gababscon[300000]
SEED_0 = 100
SEED_1 = 1223456
SEED_2 = 3254764

Loading data, please wait...