The connectivity structure between neurons is useful for determining how groups of neurons perform tasks. Directed information is a measure that can be used to infer connectivity between neurons using their recorded time series. In this paper, we develop a method of calculating the directed information rate from one neuron to another neuron it is connected to, given a particular neuronal topology. We assume a leaky integrate-and-fire (LIF) neuron model with independent and identically distributed random spike train inputs, which governs how the membrane potential of the output neuron evolves. We use this neuron model to find the dynamics of the resulting output spike train from its membrane potential dynamics, both for when the past of the input neuron is observed and when it is not. We show that an action potential in the LIF model causes a conditional independence of the activity before and after it, and we capture this conditional independence via a Markov model. We use these spike train dynamics to then calculate the directed information between the spike train of the input neuron to the spike train generated by the LIF model. In addition, we show how changing the refractory period of the LIF model affects the directed information, and also how the spike train dynamics are affected by memory constraints, which are commonly imposed in estimators of directed information.

An inductively-powered implantable microsystem for monitoring and treatment of intractable epilepsy is presented. The miniaturized system is comprised of two mini-boards and a power receiver coil. The first board hosts a 24-channel neurostimulator SoC developed in a 0.13μm CMOS technology and performs neural recording, electrical stimulation and onchip digital signal processing. The second board communicates recorded brain signals as well as signal processing results wirelessly, and generates different supply and bias voltages for the neurostimulator SoC and other external components. The multi-layer flexible coil receives inductively-transmitted power and sends it to the second board for power management. The system is sized at 2 × 2 × 0.7 cm3, weighs 6 grams, and is validated in control of chronic seizures in vivo in freely-moving rats.

This paper discusses general tradeoffs between wireless communication and computation in closed-loop implantable medical devices for neurological applications. Closed-loop devices enable neural monitoring, automated diagnostics and treatment of neurological disorders. Several topologies for the loop a re discussed, including within the implant, as well as implemented with a wearable, handheld or stationary processor. Common wireless communication data rate and range requirements and algorithmic computational requirements are summarized. As a case study, a 0.13 μm CMOS neurostimulator SoC for closed-loop treatment of intractable epilepsy is presented. Its triple-band radio with a 1m 230Mbps pulse-radio, a 2m 46Mbps pulse-radio 2, and a 10m 1.2Mbps FSK radio provides a versatile transcutaneous interface. The in-implant processor has constrained computational resources which results in a limited detection performance - seizure detection sensitivity of 87%. A higher-performance signal processing algorithm implemented on a stationary device within a loop enhances the seizure detection performance which was improved to a sensitivity of 98% with three times fewer false alarms. This comes at the cost of an increased wireless transmitter power budget, if communicated directly. These results illustrate a fundamental tradeoff between the communication and computation in closed-loop electronic therapies for neurological disorders.

Directed information is a measure that can be used to infer connectivity between neurons using their recorded time series. In this paper we develop a method of finding the directed information of a particular neural topology analytically. We assume a leaky integrate-and-fire (LIF) neuron model, and calculate the directed information between the spike train of an input neuron to the LIF model and the corresponding spike train generated by the LIF model based on this input. We show that an action potential in the LIF model causes a conditional independence of the activity before and after it, and we capture this conditional independence via a Markov model. We then use this model to find the directed information analytically. Additionally, we show how the stationary distribution and transition probabilities of the Markov model can be found using parameters of the LIF neuron. This modeling technique can thus be used to obtain the value of the directed information in a particular neuronal topology.

Directed information between two neural signals has previously been used to successfully determine if there is a synapse connecting one neuron to the other. However, there are situations in which the directed information method leads to false positives for detecting connections due to the influence of a third neuron. We propose a method that accounts for these cases using the delay-profile of the causally-conditioned entropy rate to find a measured delay range and its intersection with a connection-based delay range. Through simulations in NEURON, we show how this method outperforms connectivity inference based on directed information alone.

This paper proposes a new remote frequency calibration method that allows a passive wireless microsensor to adjust the oscillation frequency of its local oscillator using a calibrating reference signal sent by its reader. The reference signal is transmitted wirelessly by the reader in the form of a modulated tone. Frequency calibration is achieved by employing an injection-locking phase-locked loop that adjusts the oscillation frequency of the relaxation oscillator of the microsensor to that of the reference signal. The hysteresis of the relaxation oscillator is created by a novel mono-stable current pulse generating circuit also proposed in this paper. The proposed remote frequency calibration system has been designed in TSMC-0.18µm 1.8V 6-metal 1-poly CMOS technology and analyzed using SpectreRF from Cadence Design Systems with BSIM3V3 device models. The power consumption of the VCO is 525 nW while that of the system is 912 nW.