Rate maintenance and resonance in the entorhinal cortex

Throughout the brain, neurons encode information in fundamental units of spikes. Each spike represents the combined thresholding of synaptic inputs and intrinsic neuronal dynamics. Here, we address a basic question of spike train formation: how do perithreshold synaptic inputs perturb the output of a spiking neuron? We recorded from single entorhinal principal cells in vitro and drove them to spike steadily at ∼5 Hz (theta range) with direct current injection, then used a dynamic-clamp to superimpose strong excitatory conductance inputs at varying rates. Neurons spiked most reliably when the input rate matched the intrinsic neuronal firing rate. We also found a striking tendency of neurons to preserve their rates and coefficients of variation, independently of input rates. As mechanisms for this rate maintenance, we show that the efficacy of the conductance inputs varied with the relationship of input rate to neuronal firing rate, and with the arrival time of the input within the natural period. Using a novel method of spike classification, we developed a minimal Markov model that reproduced the measured statistics of the output spike trains and thus allowed us to identify and compare contributions to the rate maintenance and resonance. We suggest that the strength of rate maintenance may be used as a new categorization scheme for neuronal response and note that individual intrinsic spiking mechanisms may play a significant role in forming the rhythmic spike trains of activated neurons; in the entorhinal cortex, individual pacemakers may dominate production of the regional theta rhythm.

Spike-timing-dependent plasticity of inhibitory synapses in the entorhinal cortex

Actions of inhibitory interneurons organize and modulate many neuronal processes, yet the mechanisms and consequences of plasticity of inhibitory synapses remain poorly understood. We report on spike-timing-dependent plasticity of inhibitory synapses in the entorhinal cortex. After pairing presynaptic stimulations at time t(pre) with evoked postsynaptic spikes at time t(post) under pharmacological blockade of excitation we found, via whole cell recordings, an asymmetrical timing rule for plasticity of the remaining inhibitory responses. Strength of response varied as a function of the time interval Deltat = t(post) - t(pre): for Deltat > 0 inhibitory responses potentiated, peaking at a delay of 10 ms. For Deltat < 0, the synaptic coupling depressed, again with a maximal effect near 10 ms of delay. We also show that changes in synaptic strength depend on changes in intracellular calcium concentrations and demonstrate that the calcium enters the postsynaptic cell through voltage-gated channels. Using network models, we demonstrate how this novel form of plasticity can sculpt network behavior efficiently and with remarkable flexibility.

Optical imaging of neuronal populations during decision-making

We investigated decision-making in the leech nervous system by stimulating identical sensory inputs that sometimes elicit crawling and other times swimming. Neuronal populations were monitored with voltage-sensitive dyes after each stimulus. By quantifying the discrimination time of each neuron, we found single neurons that discriminate before the two behaviors are evident. We used principal component analysis and linear discriminant analysis to find populations of neurons that discriminated earlier than any single neuron. The analysis highlighted the neuron cell 208. Hyperpolarizing cell 208 during a stimulus biases the leech to swim; depolarizing it biases the leech to crawl or to delay swimming.

Reliability and precision of neural spike timing: simulation of spectrally broadband synaptic inputs

Spectrally broadband stimulation of neurons has been an effective method for studying their dynamic responses to simulated synaptic inputs. Previous studies with such stimulation were mostly based upon the direct intracellular injection of noisy current waveforms. In the present study we analyze and compare the firing output of various identified molluscan neurons to aperiodic, broadband current signals using three types of stimulus paradigms: 1. direct injection in current clamp mode, 2. conductance injection using electrotonic coupling of the input waveform to the neuron, and 3. conductance injection using a simulated chemical excitatory connection. The current waveforms were presented in 15 successive trials and the trial-to-trial variations of the spike responses were analyzed using peri-stimulus spike density functions. Comparing the responses of the neurons to the same type of input waveforms, we found that conductance injection resulted in more reliable and precise spike responses than direct current injection. The statistical parameters of the response spike trains depended on the spectral distribution of the input. The reliability increased with increasing cutoff frequency, while the temporal jitter of spikes changed in the opposite direction. Neurons with endogenous bursting displayed lower reproducibility in their responses to noisy waveforms when injected directly; however, they fired far more reliably and precisely when receiving the same waveforms as conductance inputs. The results show that molluscan neurons are capable of accurately reproducing their responses to synaptic inputs. Conductance injection provides an enhanced experimental technique for assessing the neurons' spike timing reliability and it should be preferred over direct current injection of noisy waveforms.

Learning Classification in the Olfactory System of Insects

We propose a theoretical framework for odor classification in the olfactory system of insects. The classification task is accomplished in two steps. The first is a transformation from the antennal lobe to the intrinsic Kenyon cells in the mushroom body. This transformation into a higher-dimensional space is an injective function and can be implemented without any type of learning at the synaptic connections. In the second step, the encoded odors in the intrinsic Kenyon cells are linearly classified in the mushroom body lobes. The neurons that perform this linear classification are equivalent to hyperplanes whose connections are tuned by local Hebbian learning and by competition due to mutual inhibition. We calculate the range of values of activity and size fo the network required to achieve efficient classification within this scheme in insect olfaction. We are able to demonstrate that biologically plausible control mechanisms can accomplish efficient classification of odors.

Robustness and enhancement of neural synchronization by activity-dependent coupling

We study the synchronization of two model neurons coupled through a synapse having an activity-dependent strength. Our synapse follows the rules of spike-timing dependent plasticity. We show that this plasticity of the coupling between neurons produces enlarged frequency-locking zones and results in synchronization that is more rapid and much more robust against noise than classical synchronization arising from connections with constant strength. We also present a simple discrete map model that demonstrates the generality of the phenomenon.