Neocort. pyramidal cells subthreshold somatic voltage controls spike propagation (Munro Kopell 2012)

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There is suggestive evidence that pyramidal cell axons in neocortex may be coupled by gap junctions into an ``axonal plexus" capable of generating Very Fast Oscillations (VFOs) with frequencies exceeding 80 Hz. It is not obvious, however, how a pyramidal cell in such a network could control its output when action potentials are free to propagate from the axons of other pyramidal cells into its own axon. We address this problem by means of simulations based on 3D reconstructions of pyramidal cells from rat somatosensory cortex. We show that somatic depolarization enables propagation via gap junctions into the initial segment and main axon, while somatic hyperpolarization disables it. We show further that somatic voltage cannot effectively control action potential propagation through gap junctions on minor collaterals; action potentials may therefore propagate freely from such collaterals regardless of somatic voltage. In previous work, VFOs are all but abolished during the hyperpolarization phase of slow-oscillations induced by anesthesia in vivo. This finding constrains the density of gap junctions on collaterals in our model and suggests that axonal sprouting due to cortical lesions may result in abnormally high gap junction density on collaterals, leading in turn to excessive VFO activity and hence to epilepsy via kindling.
1 . Munro E, Kopell N (2012) Subthreshold somatic voltage in neocortical pyramidal cells can control whether spikes propagate from the axonal plexus to axon terminals: a model study. J Neurophysiol 107:2833-52 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Neuron or other electrically excitable cell; Axon;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex L5/6 pyramidal GLU cell; Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell;
Channel(s): I Na,t; I K; I Sodium; I Potassium;
Gap Junctions: Gap junctions;
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Oscillations; Detailed Neuronal Models; Axonal Action Potentials; Epilepsy;
Implementer(s): Munro, Erin [ecmun at];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell; I Na,t; I K; I Sodium; I Potassium;
function [conn ma_conn] = ...
% Establishes connections between cells
% 1) on main axons within cortical columns, and
% 2) on collaterals 
% "weights" is an array of cell weights
%   - length of unmyelinated axon in um
% "p_intra" - probability that 2 main axons are connected within a cortical
% column on the IS
% "p_inter" - proabability that 2 1-um collateral sections are connected 
% on an unmyelinated section of axon
% "r_c" - maximum distance between connected cell somata on the grid
% "n_x", "n_y" - number of rows and columns in hexagonal grid
% "n_colcells" - number of cells in a single cortical column
% ma_conn gives connection on main axon

% set the seed for the random number generator for repeatability
%stream = RandStream.getDefaultStream;
%stream.reset; % reset stream to initial internal state

% how many cells do we need?
n_cols = n_x*n_y + floor(n_y/2);
n_cells = n_cols*n_colcells;
conn = cell(n_cells,1);
ma_conn = cell(n_cells,1);

% set weights for whole network so that their chosen randomly from 'weights'
weights_ad = set_weights(weights,n_cells);

% for each cortical column, randomly set connections between cells
for i=1:n_cols
  rand_matrix = rand(n_colcells);
  connections = (rand_matrix<=p_intra);
  % take out self and double connections 
  connections = triu(connections,1);
  conn_ind = find(connections);
  for j=1:length(conn_ind)
    % translate index in conn_ind into row and column
    c1 = floor((conn_ind(j)-1)/n_colcells) + 1; % column
    c2 = mod(conn_ind(j)-1,n_colcells) + 1; % row
    % get cell ID based on which columns we're in
    cell1 = c1 + n_colcells*(i-1);
    cell2 = c2 + n_colcells*(i-1);
    % connect cells
    conn{cell1} = [conn{cell1} cell2];
    conn{cell2} = [conn{cell2} cell1];
    ma_conn{cell1} = [ma_conn{cell1} cell2];
    ma_conn{cell2} = [ma_conn{cell2} cell1];

% set connections on unmyelinated axon collaterals between (and within)
% cortical columns

% give every cell coordinates in the Cartesian plane
% first cell is at (0,0)
% x-coord counts off by 2 units in positive direction 
% x-coord is even for all cells in even rows, odd for odd rows
% y-coord counts up by 1, but understood to be multiple of sqrrt(3)
% in Cartesian plane.
coord_list = zeros(n_cols,2);
x_coord = 0;
y_coord = 0;
max_x_coord = n_x*2-1;
for i=1:n_cols
  coord_list(i,:) = [x_coord y_coord];
  x_coord = x_coord + 2;
  if x_coord>max_x_coord
    y_coord = y_coord + 1;
    if mod(y_coord,2)==0 % even row
      x_coord = 0;
      x_coord = -1;
cl = coord_list;

% relative indexes of cells r_c from (0,0) for all columns past this one
d = 2*r_c;
xmax = ceil(d);
ymax = ceil(d/(sqrt(3)));
for x=0:2:xmax
  if x<=d
    rc_list(i,:)= [x 0];
    i = i+1;
for y=2:2:ymax % even rows
  for x=[0:2:d -(2:2:d)]
    if sqrt(x^2+3*y^2)<=d
      rc_list(i,:)= [x y];
      i = i+1;
for y=1:2:ymax
  for x=[1:2:d -(1:2:d)]
    if sqrt(x^2 + 3*y^2)<=d
      rc_list(i,:) = [x y];
      i = i+1;
src = size(rc_list);
rc = rc_list;

% set connections between unmyelinated sections
for i=1:n_cols
  col1 = i;
  col1_coords = coord_list(i,:);
  for j=1:src(1)
    col2_coords = col1_coords+rc_list(j,:);
    if coord_in_bounds(col2_coords,n_x,n_y)
      col2 = coord_to_col(col2_coords,n_x);
      cells1 = (col1-1)*n_colcells + (1:n_colcells);
      cells2 = (col2-1)*n_colcells + (1:n_colcells);
      rand_matrix = rand(n_colcells);
      p = 1 - exp(-p_inter*diag(weights_ad(cells2))*...
      % cells1(1) mult. along 1st col, cells2(1) mult along 1st row
      connections = (rand_matrix<=p);
      if col1==col2 % same columns
                    % remove self and double connections
        connections = triu(connections,1);
      else % only remove double connections
        connections = triu(connections,0);
      conn_ind = find(connections);
      for k=1:length(conn_ind)
        % translate index in conn_ind into row and column
        c1 = floor((conn_ind(k)-1)/n_colcells) + 1; % column
        c2 = mod(conn_ind(k)-1,n_colcells) + 1; % row
        % get cell ID based on which columns we're in
        cell1 = cells1(c1);
        cell2 = cells2(c2);
        if ~any(conn{cell1}==cell2)
          % connect cells
          conn{cell1} = [conn{cell1} cell2];
          conn{cell2} = [conn{cell2} cell1];
        else % cells are already connected on the IS
          % remove them from ma_conn to show that connection stays
          ma_conn{cell1} = setdiff(ma_conn{cell1},cell2);
          ma_conn{cell2} = setdiff(ma_conn{cell2},cell1);
      end %k=1:length(conn_ind)
    end %coord_in_bounds(col2_coords,n_x,n_y)
  end %j=1:length(rc_list)
end %i=1:n_cols

% subfunctions -----------------------------------------

function in_bounds = coord_in_bounds(coord,n_x,n_y)
% Are the given coordinates in the network?
% This assumes that the coordinates are on a hexagonal grid.

x = coord(1);
y = coord(2);
max_x_coord = 2*n_x-1;
in_bounds = 1;
if y>n_y-1
	in_bounds = 0;
elseif y<0;
	in_bounds = 0;
elseif x>max_x_coord
	in_bounds = 0;
elseif x<-1
	in_bounds = 0;

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