Modelling Spontaneous Firing Activity of the Motor Cortex in a Spiking Neural Network with Random and Local Connectivity

Abstract

Computational models of cortical activity can provide insight into the mechanisms of higher-order processing in the human brain including planning, perception and the control of movement. Activity in the cortex is ongoing even in the absence of sensory input or discernable movements and is thought to be linked to the topology of the underlying cortical circuitry (Ringach2009). However, the connectivity and its functional role in the generation of spatio-temporal firing patterns and cortical computations are still vastly unknown. Movement of the body is a key function of the brain, with the motor cortex the main cortical area implicated in the generation of movement. We built a spiking neural network model of the motor cortex which incorporates a laminar structure and circuitry based on a previous cortical model by (Potjans2014). A local connectivity scheme was implemented to introduce more physiological plausbility to the cortex model, and the effect on the rates, distributions and irregularity of neuronal firing, was compared to the original random connectivity method and experimental data. Local connectivity increased the distribution of and overall rate of neuronal firing. It also resulted in the irregularity of firing being more similar to those observed in experimental measurements, and a reduction in the variability in power spectrum measures. The larger variability in dynamical behaviour of the local connectivity model suggests that the topological structure of the connections in neuronal population plays a significant role in firing patterns during spontaneous activity. This model aims to take steps towards replicating the macroscopic network of the motor cortex, replicating realistic firing in order to shed light on information coding in the cortex. Large scale computational models such as this one can capture how structure and function relate to observable neuronal firing behaviour, and investigates the underlying computational mechanisms of the brain.

Circuits in the Motor Cortex Explain Oscillatory Responses to Transcranial Magnetic Stimulation

“…Connectivity is locally defined by a Gaussian probability based on spatial distances described by equation 4. Parameters for this model are shown in table 1 and the connections are described in more detail in Haggie, Besier, and McMorland (2022). This model portrays 1 mm 2 of surface area of the motor cortex as shown in figure 1 A consistent connectivity was defined in the TMS simulation studies by reading in tables of preand post-synaptic neuron indices corresponding to a single instance of the cortical model with local connectivity.…”

“…We used a spiking neural network model of excitatory and inhibitory neurons in the different cortical layers, connected with plausible connection probabilities and synaptic strengths. This motor cortex model is described in Haggie, Besier, and McMorland (2022) and uses elements from a previously published cortical model by Potjans and Diesmann (2014) and the model put forth by Esser, Hill, and Tononi (2005).…”

Linking cortex and contraction—Integrating models along the corticomuscular pathway

“…Here we provide an example of a motor cortex model using a spiking neural network that can provide the descending drive of the corticospinal tract and be integrated with downstream spinal cord and muscle models discussed in Section 2.3 and Section 3.4 , paving the way towards modelling the connection from cortex to muscle. A more detailed description of this motor cortex model can be found in Haggie et al. (2022) .…”