Biomedical subjects

L F Abbott

Publications and source records attributed to L F Abbott.

At least 19 recordsLinked to original sources

PubMed2006

Eigenvalue spectra of random matrices for neural networks.

The dynamics of neural networks is influenced strongly by the spectrum of eigenvalues of the matrix describing their synaptic connectivity. In large networks, elements of the synaptic connectivity matrix can be chosen randomly from appropriate distributions, making results from random matrix theory highly relevant. Unfortunately, classic results on the eigenvalue spectra of random matrices do not apply to synaptic connectivity matrices because of the constraint that individual neurons are either excitatory or inhibitory. Therefore, we compute eigenvalue spectra of large random matrices with excitatory and inhibitory columns drawn from distributions with different means and equal or different variances.

Models, Theoretical↗

PubMed2006

Synaptic democracy in active dendrites.

Given the extensive attenuation that can occur along dendritic cables, location within the dendritic tree might appear to be a dominant factor in determining the impact of a synapse on the postsynaptic response. By this reasoning, distal synapses should have a smaller effect than proximal ones. However, experimental evidence from several types of neurons, such as CA1 pyramidal cells, indicates that a compensatory strengthening of synapses counteracts the effect of location on synaptic efficacy. A form of spike-timing-dependent plasticity (STDP), called anti-STDP, combined with non-Hebbian activity-dependent plasticity can account for the equalization of synaptic efficacies. This result, obtained originally in models with unbranched passive cables, also arises in multi-compartment models with branched and active dendrites that feature backpropagating action potentials, including models with CA1 pyramidal morphologies. Additionally, when dendrites support the local generation of action potentials, anti-STDP prevents runaway dendritic spiking and locally balances the numbers of dendritic and backpropagating action potentials. Thus in multiple ways, anti-STDP eliminates the location dependence of synapses and allows Hebbian plasticity to operate in a more "democratic" manner.

Action Potentials↗

PubMed2006

Neuromodulation of spike-timing precision in sensory neurons.

The neuropeptide allatostatin decreases the spike rate in response to time-varying stretches of two different crustacean mechanoreceptors, the gastropyloric receptor 2 in the crab Cancer borealis and the coxobasal chordotonal organ (CBCTO) in the crab Carcinus maenas. In each system, the decrease in firing rate is accompanied by an increase in the timing precision of spikes triggered by discrete temporal features in the stimulus. This was quantified by calculating the standard deviation or "jitter" in the times of individual identified spikes elicited in response to repeated presentations of the stimulus. Conversely, serotonin increases the firing rate but decreases the timing precision of the CBCTO response. Intracellular recordings from the afferents of this receptor demonstrate that allatostatin increases the conductance of the neurons, consistent with its inhibitory action on spike rate, whereas serotonin decreases the overall membrane conductance. We conclude that spike-timing precision of mechanoreceptor afferents in response to dynamic stimulation can be altered by neuromodulators acting directly on the afferent neurons.

Afferent Pathways↗

PubMed2006

Extending the effects of spike-timing-dependent plasticity to behavioral timescales.

Activity-dependent modification of synaptic strengths due to spike-timing-dependent plasticity (STDP) is sensitive to correlations between pre- and postsynaptic firing over timescales of tens of milliseconds. Temporal associations typically encountered in behavioral tasks involve times on the order of seconds. To relate the learning of such temporal associations to STDP, we must account for this large discrepancy in timescales. We show that the gap between synaptic and behavioral timescales can be bridged if the stimuli being associated generate sustained responses that vary appropriately in time. Synapses between neurons that fire this way can be modified by STDP in a manner that depends on the temporal ordering of events separated by several seconds even though the underlying plasticity has a much smaller temporal window.

Action Potentials↗

PubMed2006

Models and properties of power-law adaptation in neural systems.

Many biological systems exhibit complex temporal behavior that cannot be adequately characterized by a single time constant. This dynamics, observed from single channels up to the level of human psychophysics, is often better described by power-law rather than exponential dependences on time. We develop and study the properties of neural models with scale-invariant, power-law adaptation and contrast them with the more commonly studied exponential case. Responses of an adapting firing-rate model to constant, pulsed, and oscillating inputs in both the power-law and exponential cases are considered. We construct a spiking model with power-law adaptation based on a nested cascade of processes and show that it can be "programmed" to produce a wide range of time delays. Finally, within a network model, we use power-law adaptation to reproduce long-term features of the tilt aftereffect.

Algorithms↗

PubMed2006

Dimensional reduction for reward-based learning.

Reward-based learning in neural systems is challenging because a large number of parameters that affect network function must be optimized solely on the basis of a reward signal that indicates improved performance. Searching the parameter space for an optimal solution is particularly difficult if the network is large. We show that Hebbian forms of synaptic plasticity applied to synapses between a supervisor circuit and the network it is controlling can effectively reduce the dimension of the space of parameters being searched to support efficient reinforcement-based learning in large networks. The critical element is that the connections between the supervisor units and the network must be reciprocal. Once the appropriate connections have been set up by Hebbian plasticity, a reinforcement-based learning procedure leads to rapid learning in a function approximation task. Hebbian plasticity within the network being supervised ultimately allows the network to perform the task without input from the supervisor.

Animals↗

PubMed2005

Signal propagation and logic gating in networks of integrate-and-fire neurons.

Transmission of signals within the brain is essential for cognitive function, but it is not clear how neural circuits support reliable and accurate signal propagation over a sufficiently large dynamic range. Two modes of propagation have been studied: synfire chains, in which synchronous activity travels through feedforward layers of a neuronal network, and the propagation of fluctuations in firing rate across these layers. In both cases, a sufficient amount of noise, which was added to previous models from an external source, had to be included to support stable propagation. Sparse, randomly connected networks of spiking model neurons can generate chaotic patterns of activity. We investigate whether this activity, which is a more realistic noise source, is sufficient to allow for signal transmission. We find that, for rate-coded signals but not for synfire chains, such networks support robust and accurate signal reproduction through up to six layers if appropriate adjustments are made in synaptic strengths. We investigate the factors affecting transmission and show that multiple signals can propagate simultaneously along different pathways. Using this feature, we show how different types of logic gates can arise within the architecture of the random network through the strengthening of specific synapses.

Action Potentials↗

PubMed2005

Cascade models of synaptically stored memories.

Storing memories of ongoing, everyday experiences requires a high degree of plasticity, but retaining these memories demands protection against changes induced by further activity and experience. Models in which memories are stored through switch-like transitions in synaptic efficacy are good at storing but bad at retaining memories if these transitions are likely, and they are poor at storage but good at retention if they are unlikely. We construct and study a model in which each synapse has a cascade of states with different levels of plasticity, connected by metaplastic transitions. This cascade model combines high levels of memory storage with long retention times and significantly outperforms alternative models. As a result, we suggest that memory storage requires synapses with multiple states exhibiting dynamics over a wide range of timescales, and we suggest experimental tests of this hypothesis.

Animals↗

PubMed2005

Drivers and modulators from push-pull and balanced synaptic input.

In 1998, Sherman and Guillery proposed that there are two types of inputs to cortical neurons; drivers and modulators. These two forms of input are required to explain how, for example, sensory driven responses are controlled and modified by attention and other internally generated gating signals. One might imagine that driver signals are carried by fast ionotropic receptors, whereas modulators correspond to slower metabotropic receptors. Instead, we have proposed a novel mechanism by which both driver and modulator inputs could be carried by transmission through the same types of ionotropic receptors. In this scheme, the distinction between driver and modulator inputs is functional and changeable rather than anatomical and fixed. Driver inputs are carried by excitation and inhibition acting in a push-pull manner. This means that increases in excitation are accompanied by decreases in inhibition and vice versa. Modulators correspond to excitation and inhibition that covary so that they increase or decrease together. Theoretical and experimental work has shown that such an arrangement modulates the gain of a neuron, rather than driving it to respond. Constructing drivers and modulators in this manner allows individual excitatory synaptic inputs to play either role, and indeed to switch between roles, depending on how they are linked with inhibition.

Afferent Pathways↗

PubMed2005

Neural network dynamics.

Neural network modeling is often concerned with stimulus-driven responses, but most of the activity in the brain is internally generated. Here, we review network models of internally generated activity, focusing on three types of network dynamics: (a) sustained responses to transient stimuli, which provide a model of working memory; (b) oscillatory network activity; and (c) chaotic activity, which models complex patterns of background spiking in cortical and other circuits. We also review propagation of stimulus-driven activity through spontaneously active networks. Exploring these aspects of neural network dynamics is critical for understanding how neural circuits produce cognitive function.

Action Potentials↗

PubMed2005

Supervised learning through neuronal response modulation.

Neural networks that are trained to perform specific tasks must be developed through a supervised learning procedure. This normally takes the form of direct supervision of synaptic plasticity. We explore the idea that supervision takes place instead through the modulation of neuronal excitability. Such supervision can be done using conventional synaptic feedback pathways rather than requiring the hypothetical actions of unknown modulatory agents. During task learning, supervised response modulation guides Hebbian synaptic plasticity indirectly by establishing appropriate patterns of correlated network activity. This results in robust learning of function approximation tasks evenwhenmultiple output units representing different functions share large amounts of common input. Reward-based supervision is also studied, and a number of potential advantages of neuronal response modulation are identified.

Feedback↗

PubMed2004

Synaptic computation.

Neurons are often considered to be the computational engines of the brain, with synapses acting solely as conveyers of information. But the diverse types of synaptic plasticity and the range of timescales over which they operate suggest that synapses have a more active role in information processing. Long-term changes in the transmission properties of synapses provide a physiological substrate for learning and memory, whereas short-term changes support a variety of computations. By expressing several forms of synaptic plasticity, a single neuron can convey an array of different signals to the neural circuit in which it operates.

Animals↗

PubMed2004

The dynamic clamp comes of age.

The dynamic clamp uses computer simulation to introduce artificial membrane or synaptic conductances into biological neurons and to create hybrid circuits of real and model neurons. In the ten years since it was first developed, the dynamic clamp has become a widely used tool for the study of neural systems at the cellular and circuit levels. This review describes recent state-of-the-art implementations of the dynamic clamp and summarizes insights gained through its use, ranging from the role of voltage-dependent conductances in shaping neuronal activity to the effects of synaptic dynamics on network behavior and the impact of in vivo-like input on neuronal information processing.

Animals↗

PubMed2003

Equalization of synaptic efficacy by activity- and timing-dependent synaptic plasticity.

In many neurons, synapses increase in strength as a function of distance from the soma in a manner that appears to compensate for dendritic attenuation. This phenomenon requires a cooperative interaction between local factors that control synaptic strength, such as receptor density and vesicle release probability, and global factors that affect synaptic efficacy, such as attenuation and boosting by active membrane conductances. Anti-spike-timing-dependent plasticity, in combination with nonassociative synaptic potentiation, can accomplish this feat even though it acts locally and independently at individual synapses. Analytic computations and computer simulations show that this combination of synaptic plasticity mechanisms equalizes the efficacy of synapses over an extended dendritic cable by adjusting local synaptic strengths to compensate for global attenuation.

Action Potentials↗

PubMed2003

Model of song selectivity and sequence generation in area HVc of the songbird.

In songbirds, nucleus HVc plays a key role in the generation of the syllable sequences that make up a song. Auditory responses of neurons in HVc are selective for single syllables and for combinations of syllables occurring in temporal sequences corresponding to those in the bird's own song. We present a model of HVc that produces syllable- and temporal-combination-selective responses on the basis of input from recorded bird songs filtered through spectral temporal receptive fields similar to those measured in field L, a primary auditory area. Normalization of the field L outputs, similar to that proposed in models of visual processing, plays an important role in the generation of syllable-selective responses in the model. For temporal-combination-selective responses, N-methyl-d-aspartate (NMDA) conductances provide a memory that allows inhibitory neurons to gate responses to a final syllable in a sequence on the basis of responses to earlier syllables. When the same network that produces temporal-combination-selective responses is excited by a nonspecific timing signal, it generates a similar pattern of output as it does in response to auditory song input. Thus the same model network can perform both sensory and motor functions.

Algorithms↗

PubMed2003

Balancing homeostasis and learning in neural circuits.

Neural circuits are remarkably adaptable, providing animals with the ability to modify their behavior on the basis of experience. At the same time, they are extremely robust and maintain stability despite the changes associated with adaptation. This combination of adaptability and stability is difficult to achieve, and it provides a strong constraint on any models of plasticity in neural circuits. New evidence suggests that the effect of action potential timing on synaptic plasticity may be an important element in reconciling homeostasis with adaptability. In particular, spike-timing dependent plasticity can act as both an adaptive and a homeostatic mechanism, controlling overall firing rates and distributions of synaptic efficacies while making neurons selective for certain aspects of their inputs. It can also cause networks that initially represent the present state of a stimulus to predict its future state on the basis of experience, a theoretical result supported by experimental data in behaving rats.

Journal Article↗

PubMed2002

Gain modulation from background synaptic input.

Gain modulation is a prominent feature of neuronal activity recorded in behaving animals, but the mechanism by which it occurs is unknown. By introducing a barrage of excitatory and inhibitory synaptic conductances that mimics conditions encountered in vivo into pyramidal neurons in slices of rat somatosensory cortex, we show that the gain of a neuronal response to excitatory drive can be modulated by varying the level of "background" synaptic input. Simultaneously increasing both excitatory and inhibitory background firing rates in a balanced manner results in a divisive gain modulation of the neuronal response without appreciable signal-independent increases in firing rate or spike-train variability. These results suggest that, within active cortical circuits, the overall level of synaptic input to a neuron acts as a gain control signal that modulates responsiveness to excitatory drive.

Action Potentials↗

PubMed2002

Redundancy reduction and sustained firing with stochastic depressing synapses.

Many synapses in the CNS transmit only a fraction of the action potentials that reach them. Although unreliable, such synapses do not transmit completely randomly, because the probability of transmission depends on the recent history of synaptic activity. We examine how a variety of spike trains, including examples recorded from area V1 of monkeys freely viewing natural scenes, are transmitted through a realistic model synapse with activity-dependent depression arising from vesicle depletion or postrelease refractoriness. The resulting sequences of transmitted spikes are significantly less correlated, and hence less redundant, than the presynaptic spike trains that generate them. The spike trains we analyze, which are typical of those recorded in a variety of brain regions, have positive autocorrelations because of the occurrence of variable length periods of sustained firing at approximately constant rates. Sustained firing may, at first, seem inconsistent with input from depressing synapses. We show, however, that such a pattern of activity can arise if the postsynaptic neuron is driven by a fixed population of direct, "feedforward" inputs accompanied by a variable number of delayed, "reverberatory" inputs. This leads to a prediction for the number and latency distribution of the inputs that typically drive a cortical neuron.

Action Potentials↗