A neural model of cortical map reorganization following a focal lesion

Armentrout, Steven; Reggia, James A.; Weinrich, Michael

doi:10.1016/0933-3657(94)90003-5

Cited by 22 publications

(12 citation statements)

References 12 publications

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“…Acute changes in RF might also be explained by other forms of synaptic plasticity. Hebbian synaptic plasticity (Hebb 1949) has been proposed as a mechanism for the maintenance of cortical RFs in several modalities (Pearson et al 1987; Armentrout et al 1994; Benuskova et al 1994; Jenison 1997; Benuskova et al 1999) and models of it can reproduce (Jenison 1997) the normal development of tonotopy (Reale et al 1987) and the changes following lesions reported by Robertson and Irvine (1989). Another possibility is that multineuron circuitry could result in processing that is functionally similar to that which we see in dendrites in this model.…”

Section: Discussionmentioning

confidence: 99%

Retuning of Inferior Colliculus Neurons Following Spiral Ganglion Lesions: A Single-Neuron Model of Converging Inputs

Sumner

Scholes

Snyder

2008

JARO

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Lesions of spiral ganglion cells, representing a restricted sector of the auditory nerve array, produce immediate changes in the frequency tuning of inferior colliculus (IC) neurons. There is a loss of excitation at the lesion frequencies, yet responses to adjacent frequencies remain intact and new regions of activity appear. This leads to immediate changes in tuning and in tonotopic progression. Similar effects are seen after different methods of peripheral damage and in auditory neurons in other nuclei. The mechanisms that underlie these postlesion changes are unknown, but the acute effects seen in IC strongly suggest the “unmasking” of latent inputs by the removal of inhibition. In this study, we explore computational models of single neurons with a convergence of excitatory and inhibitory inputs from a range of characteristic frequencies (CFs), which can simulate the narrow prelesion tuning of IC neurons, and account for the changes in CF tuning after a lesion. The models can reproduce the data if inputs are aligned relative to one another in a precise order along the dendrites of model IC neurons. Frequency tuning in these neurons approximates that seen physiologically. Removal of inputs representing a narrow range of frequencies leads to unmasking of previously subthreshold excitatory inputs, which causes changes in CF. Conversely, if all of the inputs converge at the same point on the cell body, receptive fields are broad and unmasking rarely results in CF changes. However, if the inhibition is tonic with no stimulus-driven component, then unmasking can still produce changes in CF.

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Section: Discussionmentioning

confidence: 99%

Retuning of Inferior Colliculus Neurons Following Spiral Ganglion Lesions: A Single-Neuron Model of Converging Inputs

Sumner

Scholes

Snyder

2008

JARO

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“…More recently, Reggia and colleagues (Sutton, et al, 1994;Armentrout, et al, 1994) have shown that a competitive activation rule can also account for these findings. In addition, they have been able to account for the changes in representation seen after cortical lesions.…”

Section: Stroke and Neurological Injurymentioning

confidence: 96%

Computational Approaches to Neurological Disease

Crystal¹,

Finkel²

1996

Progress in Neural Processing

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Computational models have contributed to current understanding of normal brain function, and can offer new insights into the pathophysiology of neurological disease. We define some of the outstanding clinical questions in stroke, CNS injury, Alzheimer's disease, Parkinson's disease, and epilepsy from a neurologist's perspective, and discuss the potential impact of computational neurology on computational neuroscience. We consider representative examples of models constructed at several different levels of biological detail-from detailed membrane-level simulations to connectionist networks. We focus on what has been learned from several generations of models concerned with the after-effects of neural injury. In particular, we discuss how model assumptions, sometimes of ancillary importance, have constrained the ability to predict subsequent experimental results.

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“…With regard to stroke, this observation, implicit in the general schema and made explicit by Reggia and colleagues (34,37,38), means that recovery from stroke will necessarily involve two phases. Immediately after the stroke occurs, the normal dynamic of the network is suddenly disrupted.…”

Section: Dynamics Of the Cortical Mapmentioning

confidence: 99%

■ REVIEW : Computer Models of Stroke Recovery: Implications for Neurorehabilitation

Lytton

Stark²,

Yamasaki³

et al. 1999

Neuroscientist

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The persistence of cortical plasticity in the adult can help explain functional recovery after stroke. Computer modeling tools developed to explain the process of early development of sensory systems can be extended to help us relate cortical plasticity to both behavior and to underlying molecular and cellular mechanisms. Computer modeling results suggest a two-phase recovery process, involving immediate alterations in activity patterns caused by the loss of the infarcted neurons ("dynamic plasticity"), followed by true plastic changes as the new activity alters synaptic weights between neurons. Recognition of these two phases suggests that timing of physiotherapy and pharmacotherapy may play an important role in their efficacy.Causal links are nonintuitive in complex systems. In the realm of weather, a rogue current in the mid-Pacific can throw off your vacation plans on the Atlantic seaboard. In the brain, a drug or other intervention can have farreaching and unexpected effects. The computer has proven a valuable tool for permitting predictions to be made in complex systems. This is done by making connections between different levels of organization ( 1 ). In the nervous system, these levels of organization can be identified anatomically and physiologically (Box 1) (2, 3). A major goal of computational neuroscience is to reach across these levels-for example, to explain and predict molecular effects at the network level or network effects at the behavioral level. Creation of these conceptual links is important in neurological disease because pharmacotherapeutic interventions are aimed at the molecular level, whereas clinical outcomes are sought at the functional, behavioral levels.In the early 1950s, computer-science pioneer A.M. Turing developed a series of equations that help explain how the leopard got its spots (4). He noted that a chemical process marked by a difference in reaction rates with diffusing reactants could produce a variety of striped and spotted designs that could be responsible for a wide variety of biological patterns (5). The processes of evoked activity and activity-dependent plasticity present in the central nervous system can be viewed as &dquo;reactions&dquo; that occur at different rates, resulting in the development of patterns such as ocular dominance and orientation columns (6). Given mathematical methods that can explain the development of patterns, it was natural to apply these methods to the problem of recreation of appropriate or inappropriate brain patterns after the disruption produced by a stroke. Plasticity in the Adult BrainThe pioneering studies of Hubel and Wiesel introduced the concept of a critical period in brain development. They found that appropriate neural connections from the eye either formed during this period or did not form at all, leaving the animal visually impaired. Extrapolation from this result suggested the hypothesis that neural organization of primary cortex was largely fixed at some definable developmental stage, restricting adult plasticity to remote...

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A neural model of cortical map reorganization following a focal lesion

Cited by 22 publications

References 12 publications

Retuning of Inferior Colliculus Neurons Following Spiral Ganglion Lesions: A Single-Neuron Model of Converging Inputs

Retuning of Inferior Colliculus Neurons Following Spiral Ganglion Lesions: A Single-Neuron Model of Converging Inputs

Computational Approaches to Neurological Disease

■ REVIEW : Computer Models of Stroke Recovery: Implications for Neurorehabilitation

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