Abstract
Local neocortical circuits are characterized by stereotypical physiological and structural features that subserve generic computational operations. These basic computations of the cortical microcircuit emerge through the interplay of neuronal connectivity, cellular intrinsic properties, and synaptic plasticity dynamics. How these interacting mechanisms generate specific computational operations in the cortical circuit remains largely unknown. Here, we identify the neurophysiological basis of both the rate of change and anticipation computations on synaptic inputs in a cortical circuit. Through biophysically realistic computer simulations and neuronal recordings, we show that the rate-of-change computation is operated robustly in cortical networks through the combination of two ubiquitous brain mechanisms: short-term synaptic depression and spike-frequency adaptation. We then show how this rate-of-change circuit can be embedded in a convergently connected network to anticipate temporally incoming synaptic inputs, in quantitative agreement with experimental findings on anticipatory responses to moving stimuli in the primary visual cortex. Given the robustness of the mechanism and the widespread nature of the physiological machinery involved, we suggest that rate-of-change computation and temporal anticipation are principal, hard-wired functions of neural information processing in the cortical microcircuit.
Highlights
Complex brain functions emerge from the orchestrated activity of neural circuits in the cerebral cortex
By means of computational modelling and experiments in cortical slices, how the combination of STD and spike-frequency adaptation (SFA) approximates the computation of the rate of change of the input, and how this can be used as the critical building block of a general predictive scheme for neural information processing
Presynaptic neurons could accommodate their firing through SFA by means of the activation of a calcium-dependent potassium current [9], and their synapses could in addition depress following a phenomenological model calibrated against cortical STD experiments [7]
Summary
Complex brain functions emerge from the orchestrated activity of neural circuits in the cerebral cortex. Specific instances of the causal relationship between cellular and synaptic mechanisms, network signal processing, and behavioral function have been difficult to identify This results in part from the myriad experimentally identified physiological mechanisms of synapses and neurons in the cerebral cortex, and their complex reciprocal interactions. According to the Taylor approximation for a smooth function, by combining the current stimulus value and its instantaneous rate of change it is possible to estimate the value of the stimulus slightly ahead in time [19,20] We illustrate this here by proposing a simple, plausible neural architecture that implements a stimulus anticipation response that is in agreement with experimental observations of cortical responses to moving stimuli [21] and can be extended to other low-level anticipation or control circuits in the nervous system.
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