Spatiotemporal receptive fields: a dynamical model derived from cortical architectonics

Abstract

We assume that the mammalian neocortex is built up out of some six layers which differ in their morphology and their external connections. Intrinsic connectivity is largely excitatory, leading to a considerable amount of positive feedback. The majority of cortical neurons can be divided into two main classes: the pyramidal cells, which are said to be excitatory, and local cells (most notably the non-spiny stellate cells), which are said to be inhibitory. The form of the dendritic and axonal arborizations of both groups is discussed in detail. This results in a simplified model of the cortex as a stack of six layers with mutual connections determined by the principles of fibre anatomy. This stack can be treated as a multi-input-multi-output system by means of the linear systems theory of homogeneous layers. The detailed equations for the simulation are derived in the Appendix. The results of the simulations show that the temporal and spatial behaviour of an excitation distribution cannot be treated separately. Further, they indicate specific processing in the different layers and some independence from details of wiring. Finally, the simulation results are applied to the theory of visual receptive fields. This yields some insight into the mechanisms possibly underlying hypercomplexity, putative nonlinearities, lateral inhibition, oscillating cell responses, and velocity-dependent tuning curves.

Vertical signal flow and oscillations in a three-layer model of the cortex

“…The axonal branching pattern of mice and rats (Krone et al, 1986) seems to be different from the one described for, for example, cats and monkeys in that pyramidal cells only send axonal collaterals into their own layer and the layers below. Cells in layer IV, however, have additional recurrent axons that reach the upper layers (see Fig.…”

“…Local inhibition and synaptic responses are taken into account by response kernels for the EPSP and IPSP as in the SRM. The general branching pattern is based on the idea that neurons from layer 1 (1 5 1 5 3) get input from all layers above I (including 1 itself) according to available anatomical data (Lund et al, 1979;Creutzfeldt, 1983;Gilbert and Wiesel, 1983;Valverde, 1984;Braitenberg, 1986;Krone et al, 1986;Burkhalter, 1989;Douglas and Martin, 1991;Anderson et al, 1993;Gilbert, 1993). Specifically, we have considered two different, anatomically inspired, axonal branching patterns, AC and A", under various stimulus conditions.…”

“…Additional feedback from layer 3 to layers 2 and 1 removed the differences as shown in the branching patterns Atrans and Afull. A characteristic feature of the present three-layer network is increasing activation from layer 1 to layer 3, due to the dendritic arborization. This is based on the idea that dendrites of neurons in the lower layers are longer than those of neurons on top of the cortex and seems to be a realistic assumption regarding the mean cortical anatomy (Krone et al, 1986;von Seelen et a1.,1987). As a consequence of this feature the activity of the lowest layer 3 is strong enough to excite the upper layer 1 even if there is only a weak signal 01 f-4 -m,(t) 02 m,(t) 00 tr, Finally, the question arises: what are the documented effects good for?…”

“…First, let us consider the dendritic and axonal arborization, which is layer-dependent (Lund et al, 1979;Creutzfeldt, 1983;Gilbert and Wiesel, 1983;Valverde, 1984;Braitenberg, 1986;Krone et al, 1986;Burkhalter, 1989;Douglas and Martin, 1991;Anderson et al, 1993;Gilbert, 1993). Specifically, we assume the following dendritic arborization (see Fig.…”

“…Our model of a layered structure is similar to the models of Krone et al, (1986) Thomas et al, (1991) and Patton et al, (1992). In contrast to these models, which are based on a coding by the mean firing rate, the SRM describes single spikes of single neurons.…”