Cholinergic modulation of cortical oscillatory dynamics

Abstract

1. The effect of cholinergic modulation on cortical oscillatory dynamics was studied in a computational model of the piriform (olfactory) cortex. The model included the cholinergic suppression of neuronal adaptation, the cholinergic suppression of intrinsic fiber synaptic transmission, the cholinergic enhancement of interneuron activity, and the cholinergic suppression of inhibitory synaptic transmission. 2. Electroencephalographic (EEG) recordings and field potential recordings from the piriform cortex were modeled with a simplified network in which cortical pyramidal cells were represented by excitatory input/output functions with gain parameters dependent on previous activity. The model incorporated distributed excitatory afferent input and excitatory connections between units. In addition, the model contained two sets of inhibitory units mediating inhibition with different time constants and different reversal potentials. This model can match effectively the patterns of cortical EEG and field potentials, showing oscillatory dynamics in both the gamma (30-80 Hz) and theta (3-10 Hz) frequency range. 3. Cholinergic suppression of neuronal adaptation was modeled by reducing the change in gain associated with previous activity. This caused an increased number of oscillations within the network in response to shock stimulation of the lateral olfactory tract, effectively replicating the effect of carbachol on the field potential response in physiological experiments. 4. Cholinergic suppression of intrinsic excitatory synaptic transmission decreased the prominence of gamma oscillations within the network, allowing theta oscillations to predominate. Coupled with the cholinergic suppression of neuronal adaptation, this caused the network to shift from a nonoscillatory state into an oscillatory state of predominant theta oscillations. This replicates the longer term effect of carbachol in experimental preparations on the EEG potential recorded from the cortex in vivo and from brain-slice preparations of the hippocampus in vitro. Analysis of the model suggests that these oscillations depend upon the time constant of neuronal adaptation rather than the time constant of inhibition or the activity of bursting neurons. 5. Cholinergic modulation may be involved in switching the dynamics of this cortical region between those appropriate for learning and those appropriate for recall. During recall, the spread of activity along intrinsic excitatory connections allows associative memory function, whereas neuronal adaptation prevents the spread of activity between different patterns. During learning, the recall of previously stored patterns is prevented by suppression of intrinsic excitatory connections, whereas the response to the new patterns is enhanced by suppression of neuronal adaptation.

Regulation of spike timing in visual cortical circuits

“…Neuromodulators generally have multiple effects in cortical circuits. For instance, they can change the DC firing rate of the neuron 37,38 ; this particular effect can be modelled as an additional depolarizing or hyperpolarizing current. Hence, in the model described above, neuromodulators can in principle alter the DC firing rate so that it approximately matches the oscillation frequency of the network that the neuron is embedded in.…”

Computational model of carbachol‐induced delta, theta, and gamma oscillations in the hippocampus

“…A number of papers addressed the generation of theta rhythms in the hippocampus using biophysical models (Traub et al, 1992;Liljenstrom and Hasselmo, 1995;Wallenstein and Hasselmo, 1997a,b;Finkel, 1998, 1999). Traub et al (1992) found carbachol-induced theta-frequency oscillations in a model of the CA3 region of hippocampus.…”

Neural Stability and Flexibility: A Computational Approach

“…Parameter values used for these simulations are, as far as possible, based on physiological and anatomical data, but some parameters, such as connection weights (synaptic strengths), levels of neuromodulation, etc were not possible to obtain from the experimental data, and had to be tuned. We have previously shown that our cortical neural network model can reproduce essential characteristics of olfactory cortex and hippocampus neurodynamics (Liljenström, 1991(Liljenström, , 1995(Liljenström, , 1997Liljenström and Hasselmo, 1995;Liljenström and Wu, 1995;Å rhem and Liljenström, 2001). It describes intrinsic oscillatory properties of these structures, and reproduces response patterns associated with a continuous random input signal and with a shock pulse given to the cortex.…”

“…A mathematical description of the model is summarized below. (A more detailed description with parameter values, etc can be found in Liljenström, 1991;Liljenström and Hasselmo, 1995. ) The time evolution for a network of N neural units is given by a set of coupled nonlinear first-order differential delay equations for all the N internal states, u.…”

“…C is generally a normalization constant. Neuronal adaptation is sometimes implemented as an exponential decay of the output, proportional to the time average of previous output (Liljenström and Hasselmo, 1995). In such cases, C is not a constant, but instead denotes the adaptation function, and the input-output relation becomes…”