Retrieval of pattern sequences at variable speeds in a neural network with delays

Cortical Cell Assemblies: A Possible Mechanism for Motor Programs

The concept of a motor program has been used to interpret a diverse range of empirical findings related to preparation and initiation of voluntary movement. In the absence of an underlying mechanism, its exploratory power has been limited to that of an analogy with running a stored computer program. We argue that the theory of cortical cell assemblies suggests a possible neural mechanism for motor programming. According to this view, a motor program may be conceptualized as a cell assembly, which is stored in the form of strengthened synaptic connections between cortical pyramidal neurons. These connections determine which combinations of corticospinal neurons are activated when the cell assembly is ignited. The dynamics of cell assembly ignition are considered in relation to the problem of serial order. These considerations lead to a plausible neural mechanism for the programming of movements and movement sequences that is compatible with the effects of precue information and sequence length on reaction times. Anatomical and physiological guidelines for future quantitative models of cortical cell assemblies are suggested. By taking into account the parallel re-entrant loops between the cerebral cortex and basal ganglia, the theory of cortical cell assemblies suggests a mechanism for motor plans that involve longer sequences. The suggested model is compared with other existing neural network models for motor programming.

Inhibitory motor control in stop paradigms: review and reinterpretation of neural mechanisms

What is the neurophysiological locus of inhibition when preparation for a manual response is countermanded? This paper evaluates data and models that pertain to inhibitory mechanisms operating in stop paradigms. In a model of De Jong, Coles and Logan (1995), (Strategies and mechanisms in nonselective and selective inhibitory motor control. Journal of Experimental Psychology: Human Perception and Performance, 21, 3, 498-511), a mechanism for nonselective inhibition operates peripheral to the motor cortex, while a selective mechanism operates at a central cortical level. We argue, however, that a peripheral mechanism of inhibition is incorrectly inferred from inhibition data available to date. Neurophysiological and psychophysiological data suggest that inhibitory processes always involve the cortex, and inhibitory effects are exerted upstream from the primary motor cortex. The prefrontal cortex and basal ganglia are candidate agents of response inhibition, whereas possible sites of inhibition are the thalamus and motor cortex.

Context codes and the effect of noisy learning on a simplified hippocampal CA3 model

This paper investigates how noise affects a minimal computational model of the hippocampus and, in particular, region CA3. The architecture and physiology employed are consistent with the known anatomy and physiology of this region. Here, we use computer simulations to demonstrate and quantify the ability of this model to create context codes in sequential learning problems. These context codes are mediated by local context neurons which are analogous to hippocampal place-coding cells. These local context neurons endow the network with many of its problem-solving abilities. Our results show that the network encodes context on its own and then uses context to solve sequence prediction under ambiguous conditions. Noise during learning affects performance, and it also affects the development of context codes. The relationship between noise and performance in a sequence prediction is simple and corresponds to a disruption of local context neuron firing. As noise exceeds the signal, sequence completion and local context neuron firing are both lost. For the parameters investigated, extra learning trials and slower learning rates do not overcome either of the effects of noise. The results are consistent with the important role played, in this hippocampal model, by local context neurons in sequence prediction and for disambiguation across time.

The dynamics of sparse random networks

Recurrent neural networks with full symmetric connectivity have been extensively studied as associative memories and pattern recognition devices. However, there is considerable evidence that sparse, asymmetrically connected, mainly excitatory networks with broadly directed inhibition are more consistent with biological reality. In this paper, we use the technique of return maps to study the dynamics of random networks with sparse, asymmetric connectivity and nonspecific inhibition. These networks show three qualitatively different kinds of behavior: fixed points, cycles of low period, and extremely long cycles verging on aperiodicity. Using statistical arguments, we relate these behaviors to network parameters and present empirical evidence for the accuracy of this statistical model. The model, in turn, leads to methods for controlling the level of activity in networks. Studying random, untrained networks provides an understanding of the intrinsic dynamics of these systems. Such dynamics could provide a substrate for the much more complex behavior shown when synaptic modification is allowed.