Biomedical subjects

Misha Tsodyks

Publications and source records attributed to Misha Tsodyks.

6 recordsLinked to original sources

PubMed2003

Neuronal assemblies: single cortical neurons are obedient members of a huge orchestra.

Spontaneous cortical activity of single neurons is often either dismissed as noise, or is regarded as carrying no functional significance and hence is ignored. Our findings suggest that such concepts should be revised. We explored the coherent population activity of neuronal assemblies in primary sensory area in the absence of a sensory input. Recent advances in real-time optical imaging based on voltage-sensitive dyes (VSDI) have facilitated exploration of population activity and its intimate relationship to the activity of individual cortical neurons. It has been shown by in vivo intracellular recordings that the dye signal measures the sum of the membrane potential changes in all the neuronal elements in the imaged area, emphasizing subthreshold synaptic potentials and dendritic action potentials in neuronal arborizations originating from neurons in all cortical layers whose dendrites reach the superficial cortical layers. Thus, the VSDI has allowed us to image the rather illusive activity in neuronal dendrites that cannot be readily explored by single unit recordings. Surprisingly, we found that the amplitude of this type of ongoing subthreshold activity is of the same order of magnitude as evoked activity. We also found that this ongoing activity exhibited high synchronization over many millimeters of cortex. We then investigated the influence of ongoing activity on the evoked response, and showed that the two interact strongly. Furthermore, we found that cortical states that were previously associated only with evoked activity can actually be observed also in the absence of stimulation, for example, the cortical representation of a given orientation may appear without any visual input. This demonstration suggests that ongoing activity may also play a major role in other cortical function by providing a neuronal substrate for the dependence of sensory information processing on context, behavior, memory and other aspects of cognitive function.

Action Potentials↗

PubMed2002

Context-enabled learning in the human visual system.

Training was found to improve the performance of humans on a variety of visual perceptual tasks. However, the ability to detect small changes in the contrast of simple visual stimuli could not be improved by repetition. Here we show that the performance of this basic task could be modified after the discrimination of the stimulus contrast was practised in the presence of similar laterally placed stimuli, suggesting a change in the local neuronal circuit involved in the task. On the basis of a combination of hebbian and anti-hebbian synaptic learning rules compatible with our results, we propose a mechanism of plasticity in the visual cortex that is enabled by a change in the context.

Adolescent↗

PubMed2002

Spike-timing-dependent synaptic plasticity - the long road towards understanding neuronal mechanisms of learning and memory.

Depending on the precise temporal relationship between their spiking activities, connections between neurons could be modified in opposite directions. Although the functional implications of this spike-timing-dependent plasticity are not clear, several theoretical studies have indicated that it could underlie important effects such as sequence learning, predictive learning and balancing excitation and inhibition. To explore fully this novel form of synaptic plasticity, it is crucial to understand how the modification builds up over the consecutive spikes of presynaptic and postsynaptic neurons. In the absence of solid data, many theorists assumed a linear summation model. However, recent experiments specifically devised to study this issue have demonstrated that the effects of the consecutive spikes on the overall modification steadily decline, indicating strong non-linearities in the corresponding learning rules.

Animals↗

PubMed2002

Coding of temporal information by activity-dependent synapses.

Synaptic transmission in the neocortex is dynamic, such that the magnitude of the postsynaptic response changes with the history of the presynaptic activity. Therefore each response carries information about the temporal structure of the preceding presynaptic input spike train. We quantitatively analyze the information about previous interspike intervals, contained in single responses of dynamic synapses, using methods from information theory applied to experimentally based deterministic and probabilistic phenomenological models of depressing and facilitating synapses. We show that for any given dynamic synapse, there exists an optimal frequency of presynaptic spike firing for which the information content is maximal; simple relations between this optimal frequency and the synaptic parameters are derived. Depressing neocortical synapses are optimized for coding temporal information at low firing rates of 0.5-5 Hz, typical to the spontaneous activity of cortical neurons, and carry significant information about the timing of up to four preceding presynaptic spikes. Facilitating synapses, however, are optimized to code information at higher presynaptic rates of 9-70 Hz and can represent the timing of over eight presynaptic spikes.

Action Potentials↗

PubMed2002

Spike frequency adaptation and neocortical rhythms.

Spike-frequency adaptation in neocortical pyramidal neurons was examined using the whole cell patch-clamp technique and a phenomenological model of neuronal activity. Noisy current was injected to reproduce the irregular firing typically observed under in vivo conditions. The response was quantified by computing the poststimulus histogram (PSTH). To simulate the spiking activity of a pyramidal neuron, we considered an integrate-and-fire model to which an adaptation current was added. A simplified model for the mean firing rate of an adapting neuron under noisy conditions is also presented. The mean firing rate model provides a good fit to both experimental and simulation PSTHs and may therefore be used to study the response characteristics of adapting neurons to various input currents. The models enable identification of the relevant parameters of adaptation that determine the shape of the PSTH and allow the computation of the response to any change in injected current. The results suggest that spike frequency adaptation determines a preferred frequency of stimulation for which the phase delay of a neuron's activity relative to an oscillatory input is zero. Simulations show that the preferred frequency of single neurons dictates the frequency of emergent population rhythms in large networks of adapting neurons. Adaptation could therefore be one of the crucial factors in setting the frequency of population rhythms in the neocortex.

Action Potentials↗

PubMedDate not supplied

Computation by ensemble synchronization in recurrent networks with synaptic depression.

While computation by ensemble synchronization is considered to be a robust and efficient way for information processing in the cortex (C. Von der Malsburg and W. Schneider (1986) Biol. Cybern. 54: 29-40; W. Singer (1994) Inter. Rev. Neuro. 37: 153-183; J.J. Hopfield (1995) Nature 376: 33-36; E. Vaadia et al. (1995) Nature 373: 515-518), the neuronal mechanisms that might be used to achieve it are yet to be uncovered. Here we analyze a neural network model in which the computations are performed by near coincident firing of neurons in response to external inputs. This near coincident firing is enabled by activity dependent depression of inter-neuron connections. We analyze the network behavior by using a mean-field approximation, which allows predicting the network response to various inputs. We demonstrate that the network is very sensitive to temporal aspects of the inputs. In particular, periodically applied inputs of increasing frequency result in different response profiles. Moreover, applying combinations of different stimuli lead to a complex response, which cannot be easily predicted from responses to individual components. These results demonstrate that networks with synaptic depression can perform complex computations on time-dependent inputs utilizing the ability to generate temporally synchronous firing of single neurons.

Action Potentials↗